This document explains some of Bito's privacy and security practices. Our Trust Center outlines our various accreditations (SOC 2 Type II) and our various security policies. You can read our full Privacy Policy at https://bito.ai/privacy-policy/.

Bito AI

Security is top of mind at Bito, especially when it comes to your code. A fundamental approach we have taken is we do not store any code, code snippets, indexes or embedding vectors on Bito’s servers unless you expressly allow it. You decide where you want to store your code, either locally on your machine, in your cloud, or on Bito’s cloud. Importantly, our AI partners do not store any of this information.

All requests are transmitted over HTTPS and are fully encrypted.

None of your code or AI requests are used for AI model training. None of your code or AI requests are stored by our AI partners. Our AI model partners are OpenAI, Anthropic, and Google. Here are their policies where they state that they do not store or train on data related to API access (we access all AI models via APIs):

1. OpenAI: https://openai.com/enterprise-privacy/

2. Anthropic: https://www.anthropic.com/uk-government-internal-ai-safety-policy-response/data-input-controls-and-audit

3. Google Cloud: https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-unveils-ai-and-ml-privacy-commitment (5th paragraph)

The AI requests including code snippets you send to Bito are sent to Bito servers for processing so that we can respond with an answer.

Interactions with Bito AI are auto-moderated and managed for toxicity and harmful inputs and outputs.

Any response generated by the Bito IDE AI Assistant is stored locally on your machine to show the history in Bito UI. You can clear the history anytime you want from the Bito UI.

SOC 2 Type II Compliance

Bito is SOC 2 Type II compliant. This certification reinforces our commitment to safeguarding user data by adhering to strict security, availability, and confidentiality standards. SOC 2 Type II compliance is an independent, rigorous audit that evaluates how well an organization implements and follows these security practices over time.

Our SOC 2 Type II compliance means:

• Enhanced Data Security: We consistently implement robust controls to protect your data from unauthorized access and ensure it remains secure.

• Operational Excellence: Our processes are designed to maintain high availability and reliability, ensuring uninterrupted service.

• Regular Monitoring and Testing: We conduct continuous monitoring and regular internal reviews to uphold the highest security standards.

This certification is an assurance that Bito operates with a high level of trust and transparency, providing you with a secure environment for your code and data.

For any further questions regarding our SOC 2 Type II compliance or to request a copy of the audit report, please reach out to support@bito.ai

Code Flow through Bito’s System

AI Code Review Agent

When you use the self-hosted/docker version that you have setup in your VPC, in the docker image Bito checks out the diff and clones the repo for static analysis and also to determine relevant code context for code review. This context and the diff is passed to Bito's system. The request is then sent to a third-party LLM (e.g., OpenAI, Google Cloud, etc.). The LLM processes the prompt and return the response to Bito. No code is retained by the LLM. Bito then receives the response, processes it (such as formatting), and returns it to your self-hosted docker instance. This then posts it to your Git provider. However, the original query is not retained, nor are the results. After each code review is completed, the diff and the checked out repo are deleted.

If you use the Bito cloud to run the AI Code Review Agent, it runs similarly to the self-hosted version. Bito ephemerally checks out the diff and clones the repo for static analysis and to determine the relevant code context for code review. This context and the diff is passed to Bito's system. The request is then sent by Bito to a third-party LLM (e.g., OpenAI, Google Cloud, etc.). The LLM processes the prompt and return the response to Bito. No code is retained by the LLM. Bito then receives the response, processes it (such as formatting), and posts it to your Git provider. However, the original query is not retained, nor are the results. After each code review is completed, the diff and the checked out repo are deleted.

AI Chat and Code Completions

When we receive an AI request from a user, it is processed by Bito's system (such as adding relevant context and determining the Large Language Model (LLM) to use). However, the original query is not retained. The request is then sent to a third-party LLM (e.g., OpenAI, Google Cloud, etc.). The LLM processes the prompt and return the response to Bito. Bito then receives the response, processes it (such as formatting), and returns it to the user’s machine.

For enterprises, we have the ability to connect to your own private LLM accounts, including but not limited to OpenAI, Google Cloud, Anthropic, or third-party services such as AWS Bedrock, Azure OpenAI. This way all data goes through your own accounts or Virtual Private Cloud (VPC), ensuring enhanced control and security.

Data and Business Privacy Policy

In line with Bito's commitment to transparency and adherence to data privacy standards, our comprehensive data and business privacy policy is integrated into our practices. Our complete Terms of Use, including the Privacy Policy, are available at https://bito.ai/terms-of-use/, with our principal licensing information detailed at https://bito.ai/terms-of-service/.

Data Retention Policy

Our data retention policy is carefully designed to comply with legal standards and to respect our customers' privacy concerns. The policy is categorized into four levels of data:

1. Relationship and Usage Meta Data: This includes all data related to the customer's interaction with Bito, such as address, billing amounts, user account data (name and email), and usage metrics (number of queries made, time of day, length of query, etc.). This category of data is retained indefinitely for ongoing service improvement and customer support.

2. Bito Business Data: Includes customer-created templates and settings. This data is terminated 90 days after the end of the business relationship with Bito.

3. Confidential Customer Business Data: This includes code, code artifacts, and other organization-owned data such as Jira, Confluence, etc. This data is either stored on-prem/locally on the customer’s machines, or, if in the cloud, is terminated at the end of the business relationship with Bito.

4. AI Requests: Data in an AI request to Bito’s AI system. AI requests are neither retained nor viewed by Bito. We ensure the confidentiality of your AI queries; Bito and our LLM partners do not store your code, and none of your data is used for model training. All requests are transmitted via HTTPS and are fully encrypted.

Sub-processor

Bito uses the following third-party services: Amazon AWS, Anthropic, Clearbit, Github, Google Analytics, Google Cloud, HelpScout, Hubspot, Microsoft Azure, Mixpanel, OpenAI, SendGrid, SiteGround, and Slack for infrastructure, support, and functional capabilities.

Personal Data

Bito follows industry standard practices for protecting your e-mail and other personal details. Our password-less login process - which requires one-time passcode sent to your e-mail for every login - ensures the complete security of your account.

Slack Integration

The Bito Slack app has its own dedicated data handling page covering retention, deletion, LLM details, and uninstall procedures specific to Slack data.

→ Slack App – Data Handling

If you have any questions about our security and privacy, please email support@bito.ai

This page details exactly how the Bito Slack app handles, stores, and processes your data. All policies stated here apply specifically to data flowing through the Bito Slack integration.

Data Retention Policy

• Conversational data (messages sent to @Bito, slash command inputs, and AI responses) is not stored by Bito. Only logs are retained for 15 days and are automatically deleted afterward.

• Workspace metadata (workspace ID, installer identity, app configuration): retained for the duration of the active installation.

• Account-level data: see the general Privacy and Security page.

Upon uninstallation, all data is permanently deleted except for logs. Logs are retained for 15 days and then automatically deleted, except where retention is required by law.

Data Archival / Removal Policy

We do not archive messages sent to @Bito, slash command inputs, or AI responses. Only logs are retained in AWS S3 for 15 days and then automatically deleted.

Data Storage Policy

• Primary storage: AWS, US-East region (default). EU residency available for Enterprise customers on request.

• Encryption at rest: AES-256

• Encryption in transit: TLS 1.2 or higher

• Access controls: RBAC with mandatory SSO + MFA for employees

• Audit logs: retained for 15 days

• Third-party sharing: Bito does not sell, rent, or share customer data for marketing.

How to Request Data Deletion

1. Email support@bito.ai with subject "Slack Data Deletion Request"

2. Include workspace name, workspace ID, and admin verification

3. Bito verifies within 2 business days

4. Deletion is completed within 2 weeks of verification

5. Written confirmation is emailed on completion

Deletion covers all conversational data and workspace metadata in production systems. Backup purging follows the next rotation cycle.

LLM (Large Language Model) Information

Which LLMs are used

Bito for Slack uses third-party LLMs selected by task and plan:

• OpenAI

• Anthropic

What data is sent to the LLM

• Your message content to @Bito

• Relevant thread context (only when @Bito is mentioned in a thread)

• System instructions

Never sent: Slack user emails/names beyond attribution, messages from channels where @Bito is not invited, private DMs not involving @Bito.

Training policy

Your data is never used to train LLM models. Bito's contracts with LLM providers explicitly prohibit use of customer prompts/outputs for training. Provider-side retention is limited to abuse-monitoring (typically ≤30 days) per their enterprise policies.

Data residency

LLM API calls are routed to US endpoints by default. Enterprise customers may request EU-only processing via support@bito.ai.

AI safeguards

• Visible AI disclaimer on responses

• Rate limiting and abuse detection

• LLM-vendor content moderation

• Feedback / human escalation via support@bito.ai

AI Disclaimer

Bito for Slack is powered by large language models (LLMs). AI-generated responses may be inaccurate, incomplete, biased, or outdated. Treat outputs as informational suggestions, not as professional, legal, financial, medical, or compliance advice. Always verify critical information before acting on it.

Report inaccurate responses to support@bito.ai.

Uninstalling the Bito Slack App

1. In Slack, go to Settings → Manage Apps → Bito → Remove

2. Upon uninstallation, all data is permanently deleted except for logs. Logs are retained for 15 days and then automatically deleted, except where retention is required by law.

3. To confirm or expedite deletion, email support@bito.ai

Related

• Bito Privacy and Security

• Slack Integration Setup Guide

• Get Support

Get up and running with Bito in just a few steps! Bito seamlessly integrates with Windsurf, providing powerful AI-driven code reviews directly within your editor. Click the button below to quickly install the Bito extension and start optimizing your development workflow with context-aware AI Chat, and more.

Install on Windsurf

Installation Steps

Step 1: Open Windsurf IDE

Launch Windsurf IDE on your computer.

Step 2: Access the extensions marketplace

From the top menu, click View -> Extensions to open the Extensions marketplace.

Step 3: Search for Bito and install the extension

1. In the Extensions search bar at the top, type "Bito"

2. Look for the official Bito extension in the search results. The extension should be published by "Bito"

3. Click the Install button and wait for the installation to complete (this usually takes just a few seconds).

Step 4: Sign in to Bito

Once installed, you'll need to authenticate:

1. Click "Sign up or Sign-in"

2. Bito authentication screen will display.

3. Log in with your Bito account credentials (or create a new account)

Step 6: Verify installation

To confirm Bito is working correctly:

1. You should see the Bito icon in your Windsurf IDE sidebar

2. Click on it to open the Bito panel

3. You should now have access to all Bito features

4. Try asking Bito a simple question to test the connection

Enhance your code reviews with deeper codebase intelligence by connecting Bito's AI Code Review Agent to AI Architect. This integration enables significantly more accurate and context-aware reviews by leveraging the comprehensive knowledge graph that AI Architect builds from your codebase.

What this integration provides

When AI Code Review Agent accesses AI Architect's knowledge graph, it gains a complete understanding of your codebase architecture — including microservices, modules, APIs, dependencies, and design patterns.

This enhanced context allows the AI Code Review Agent to:

• Provide system-aware code reviews - Understand how changes in one service or module impact other parts of your system

• Catch architectural inconsistencies - Identify when new code doesn't align with your established patterns and conventions

• Detect cross-repository issues - Spot problems that span multiple repositories or services

• Deliver more accurate suggestions - Generate fixes that are grounded in your actual codebase structure and usage patterns

• Reduce false positives - Better understand context to avoid flagging valid code as problematic

Setup instructions

Follow these steps to connect AI Architect with AI Code Review Agent:

1. Log in to Bito Cloud

2. Open the AI Architect Settings dashboard.

3. In the Server URL field, enter your Bito MCP URL

4. In the Auth token field, enter your Bito MCP Access Token

Need help?

Contact our team at support@bito.ai to request a trial. We'll help you configure the integration and get your team up and running quickly.

Bito Cloud offers a single-click solution for using the AI Code Review Agent, eliminating the need for any downloads on your machine. You can create multiple instances of the Agent, allowing each to be used with a different repository on a Git provider such as GitHub, GitLab, or Bitbucket.

We also support GitHub (Self-Managed), GitLab (Self-Managed), and Bitbucket (Self-Managed).

Connect Bito to your Git provider

Select your Git provider from the options below and follow the step-by-step installation guide to seamlessly set up your AI Code Review Agent.

The IDE customization settings are accessible through the new toolbar dropdown menu titled "Extension Settings".

Light and Dark Themes

In Visual Studio Code and JetBrains IDEs, you can choose between a light or dark theme for the Bito panel to match your coding environment preference. For VS Code users, Bito also offers an adaptive theme mode in which the Bito panel and font colors automatically adjust based on your selected VS Code theme, creating a seamless visual experience.

You can set the desired theme through the Theme dropdown.

Theme Screenshots

“Always Light” Theme

“Always Dark” Theme

“Light” or “Dark” Theme - Matching IDE

“Adaptive” Theme

Theme adapted from “Noctis Lux”:

Theme adapted from “Solarized Light”:

Theme adapted from “Tomorrow Night Blue”:

Theme adapted from “barn-cat”:

Font Size Control

Take control of your code readability! Within the Bito extension settings, you can now adjust the font size for a comfortable viewing experience.

You can set the desired font size through the Font Size text field. However, if you check the Font Size (Match with IDE Font) checkbox, it will override the set font size with the Editor font size.

Save time and effort by quickly creating a new AI Code Review Agent instance using the configuration settings of an existing one. It’s a fast and simple way to set up multiple Agent instances without having to reconfigure each one.

Follow the steps below to get started:

1. Log in to Bito Cloud and select a workspace to get started.

2. From the left sidebar, select Code Review Agents.

1. If your Bito workspace is connected to your GitHub/GitLab/Bitbucket account, a list of AI Code Review Agent instances configured in your workspace will appear. Locate the instance you wish to duplicate and click the Clone button given in front of it.

1. An Agent configuration form will open, pre-populated with the input field values. You can edit these values as needed.

1. Click Select repositories to choose Git repositories for the new Agent.

1. To enable code review for a specific repository, simply select its corresponding checkbox. You can also enable repositories later, after the Agent has been created. Once done, click Save and continue to save the new Agent configuration.

1. When you save the configuration, your new Agent instance will be added and available on the Code Review Agents page.

Governor is a context-driven model router that sits between your AI coding tools (Claude Code, Cursor, Codex, etc.) and the LLM providers (Anthropic, OpenAI, Groq, etc.). It reduces the tokens each task consumes and the price you pay for every token.

Governor combines Bito's AI Architect with a smart model router:

• Bito's AI Architect cuts the token count. It serves system context from a live knowledge graph of your entire engineering system (code, business context, and tribal knowledge), so your tools skip the discovery work that consumes most of a task's tokens.

• The model router cuts the price per token. It uses the same knowledge graph to see how complex each request is, then sends it to the cost-efficient LLM that can handle it, because most requests do not need a frontier model.

Agent spend is tokens multiplied by price. Governor cuts both numbers, so the savings also multiply rather than add.

Nothing changes about how your team works. Point your coding tools at Governor with one base URL and one key, and everything else stays exactly as it is.

How Governor works

Governor is a drop-in endpoint. Your tools stay unchanged, your provider accounts stay yours, and you can connect Governor directly or through a gateway you already run.

Every request follows the same path:

1. Authenticate. Governor verifies the gateway key your tool sent.

2. Add context. AI Architect answers system questions inside the request, so the tool starts the task already knowing your architecture.

3. Pick the model. The router judges how complex the request is and selects a cost-efficient model that can handle it, within the policy you set.

4. Call the provider. Governor forwards the request to your own LLM provider account using your own credential.

5. Record it. Governor returns the response and logs the model used, tokens consumed, status, and cost.

Deployment options

Governor runs either on Bito's infrastructure or inside your own network. Both use the same configuration model, so a workspace you build during an evaluation transfers to a self-hosted deployment without redesign.

1. Bito-hosted is the faster path. Bito creates your workspace and issues an admin token, you configure providers and routes in the console, and Bito applies updates. Most teams start here to get a measurable result within a day.

2. Self-hosted keeps everything inside your network. One command installs Governor and its database as Docker containers on a single host, and your provider credentials are encrypted with a key that never leaves that host. Choose this when data residency, network egress, or regulatory requirements prevent traffic from transiting a third-party service.

How do I whitelist Bito's gateway IP address for my on-premise Git platform?

To ensure the AI Code Review Agent operates smoothly with your GitHub (Self-Managed), GitLab (Self-Managed), or Bitbucket (Self-Managed), please whitelist all of Bito's gateway IP addresses in your firewall to allow incoming traffic from Bito. This will enable Bito to access your self-hosted repository.

List of IP addresses to whitelist:

• 18.188.201.104

• 3.23.173.30

• 18.216.64.170

The agent response can come from any of these IPs.

How can I prevent the AI Code Review Agent from stopping due to token expiry?

You should set a longer expiration period for your GitHub Personal Access Token (Classic), GitLab Personal Access Token, or Bitbucket Personal Access Token. We recommend setting the expiration to at least one year. This prevents the token from expiring early and avoids disruptions in the AI Code Review Agent's functionality.

Additionally, we highly recommend updating the token before expiry to maintain seamless integration and code review processes.

For more details on how to create tokens, follow these guides:

• GitHub Personal Access Token (Classic): View Guide

• GitLab Personal Access Token: View Guide

• Bitbucket Personal Access Token: View Guide

Why does Bito need access to my Git account?

Bito requires certain permissions to analyze pull requests and provide AI-powered code reviews. It never stores your code and only accesses the necessary data to deliver review insights.

What permissions does Bito need?

Bito requires:

1. Read access to code and metadata: To analyze PRs and suggest improvements

2. Read and write access to issues and pull requests: To post AI-generated review comments

3. Read access to organization members: To provide better review context

I don’t have admin permissions. Can I still use Bito?

If you don’t have admin access, you’ll need your administrator to install Bito on your organization’s Git account. Once installed, you can use it for PR reviews on allowed repositories. GitHub also sends a notification to the organization owner to request the organization owner to install the app.

Does Bito store my code?

No, Bito does not store or train models on your code. It only analyzes pull request data in real-time and provides suggestions directly within the PR.

Can I choose which repositories Bito has access to?

Yes, after installation, you can select specific repositories instead of granting access to all. You can also manage repository access later through our web dashboard.

What happens after I install the Bito App?

Once installed, you’ll be redirected to Bito, where you can:

1. Select repositories for AI-powered reviews

2. Customize review settings to fit your workflow

3. Open a pull request to start receiving AI-driven suggestions

Where can I get help if I have issues installing Bito?

Contact support@bito.ai for any assistance.

Security and privacy are fundamental to how Bito's AI Architect operates. Bito doesn't store your code and we don't use your code for AI model training.

This document explains how AI Architect handles your code and data across different deployment modes.

Code storage: Bito-hosted vs. Self-hosted

The way AI Architect handles your code depends on your deployment choice:

Bito-hosted AI Architect

Your code is not stored on Bito's servers.

When you use Bito-hosted AI Architect, Bito stores only summaries and indexes of your code — not the code itself. These indexes help AI Architect understand your repository structure, key functionalities, service calls, design patterns, variable naming conventions, and architectural relationships across microservices.

How it works:

• AI Architect analyzes your code to build a knowledge graph

• Only metadata and summaries are stored on Bito's servers (e.g., "this repo contains authentication services," "this module calls these other services")

• The actual code is never persisted in Bito's cloud

When code access is needed:

• If you ask a question that requires viewing actual code, AI Architect authenticates with your Git provider using your credentials

• It fetches the specific file on-demand

• Extracts the necessary information

• Passes it back to you through the MCP server

• The code is not retained after the request

Code caching

For performance optimization, Bito temporarily caches individual code files for up to 10 minutes during analysis. You have full control over this behavior — you can configure the cache duration or disable caching entirely through your settings.

Self-hosted AI Architect

Your code remains entirely in your data center.

When you deploy AI Architect on-premises in your own infrastructure, all code and indexes are stored locally. Bito receives no code or code-related information. We only receive anonymous metadata about usage to confirm the product is working properly — no details about what your code does or contains.

Performance considerations

Bito-hosted AI Architect adds a few seconds to requests that require actual code access because the code must be fetched on-demand from your Git provider. This slight latency is the trade-off for keeping your code off Bito's servers. In practice, this difference is not material to user experience.

Code security and encryption

• All requests to and from AI Architect are transmitted over HTTPS and fully encrypted

• Your Git credentials are used only to authenticate and fetch code when needed

• Code accessed during analysis is handled securely and not logged or stored beyond the temporary cache window

No AI model training

Your code is never used for AI model training. Bito doesn't train on, learn from, or retain any of your code snippets or queries for model improvement purposes.

Privacy and third parties

Bito uses leading AI providers (such as Anthropic, OpenAI, etc.) via their APIs to power AI Architect features. None of your code or AI requests are stored by these partners. All AI providers we work with maintain commitments to not use API data for model training or retain user data beyond the immediate request processing.

Metadata and usage analytics

To ensure AI Architect is working correctly and to improve the product, Bito collects:

• Anonymous usage metrics

• Feature usage statistics

• Error logs (without code content)

This information helps us understand how AI Architect is being used and identify areas for improvement.

Compliance and certifications

Bito maintains SOC 2 Type II compliance and follows industry-leading security practices. For detailed information about our security posture and certifications, visit our Trust Center.

For our full privacy practices, see our Privacy Policy.

Questions?

If you have questions about how AI Architect handles your code or data, contact the Bito team at support@bito.ai. We're committed to transparency about security and privacy practices.

On your machine or in a Private Cloud, you can run the AI Code Review Agent via either CLI or webhooks service. This guide will teach you about the key differences between CLI and webhooks service and when to use each mode.

Difference Between CLI and webhooks service

The main difference between CLI and webhooks service lies in their operational approach and purpose. In CLI, the docker container is used for a one-time code review. This mode is ideal for isolated, single-instance analyses where a quick, direct review of the code is needed.

On the other hand, webhooks service is designed for continuous operation. When set in webhooks service mode, the AI Code Review Agent remains online and active at a specified URL. This continuous operation allows it to respond automatically whenever a pull request is opened in a repository. In this scenario, the git provider notifies the server, triggering the AI Code Review Agent to analyze the pull request and post its review as a comment directly on it.

When to Use CLI and When to Use webhooks service

Selecting the appropriate mode for code review with the AI Code Review Agent depends largely on the nature and frequency of your code review needs.

CLI: Ideal for Specific, One-Time Reviews

CLI mode is best suited for scenarios requiring immediate, one-time code reviews. It's particularly effective for:

• Conducting quick assessments of specific pull requests.

• Performing periodic, scheduled code analyses.

• Reviewing code in environments with limited or no continuous integration support.

• Integrating with batch processing scripts for ad-hoc analysis.

• Using in educational settings to demonstrate code review practices.

• Experimenting with different code review configurations.

• Reviewing code on local setups or for personal projects.

• Performing a final check before pushing code to a repository.

CLI mode stands out for its simplicity and is perfect for standalone tasks where a single, direct execution of the code review process is all that's needed.

Webhooks service: For Continuous, Automated Reviews

Webhooks service, on the other hand, is the go-to choice for continuous code review processes. It excels in:

• Continuously monitoring all pull requests in a repository.

• Providing instant feedback in collaborative projects.

• Seamlessly integrating with CI/CD pipelines for automated reviews.

• Performing automated code quality checks in team environments.

• Conducting real-time security scans on new pull requests.

• Ensuring adherence to coding standards in every pull request.

• Streamlining the code review process in large-scale projects.

• Maintaining consistency in code review across multiple projects.

• Enhancing workflows in remote or distributed development teams.

• Offering prompt feedback in agile development settings.

Webhooks service is indispensable in active development environments where consistent monitoring and immediate feedback are critical. It automates the code review process, integrating seamlessly into the workflow and eliminating the need for manual initiation of code reviews.

The AI Code Review Agent supports two deployment options:

• Bito Cloud (fully managed)

• Self-hosted service (run on your own infrastructure)

Each option comes with its own set of benefits and considerations.

This guide walks you through both options to help you determine which deployment model best fits your team’s needs.

Bito Cloud

Bito Cloud provides a managed environment for running the AI Code Review Agent, offering a seamless, hassle-free experience. This option is ideal for teams looking for quick deployment and minimal operational overhead.

Pros:

• Simplicity: Enjoy a straightforward setup with a single-click installation process, making it easy to get started without technical hurdles.

• Maintenance-Free: Bito Cloud takes care of all necessary updates and maintenance, ensuring your Agent always operates on the latest software version without any effort on your part.

• Scalability: The platform is designed to easily scale, accommodating project growth effortlessly and ensuring reliable performance under varying loads.

Cons:

• Handling of Pull Request Diffs: For analysis purposes, diffs from pull requests are temporarily stored on our servers.

Install/run using Bito Cloud

Self-hosted service

Self-hosted AI Code Review Agent offers a higher degree of control and customization, suited for organizations with specific requirements or those who prefer to manage their own infrastructure.

Pros:

• Full Control: Self-hosting provides complete control over the deployment environment, allowing for extensive customization and the ability to integrate with existing systems as needed.

• Privacy and Security: Keeping the AI Code Review Agent within your own infrastructure can enhance data security and privacy, as all information remains under your direct control.

Cons:

• Setup Complexity: Establishing a self-hosted environment requires technical know-how and can be more complex than using a managed service, potentially leading to longer setup times.

• Maintenance Responsibility: The responsibility of maintaining and updating the software falls entirely on your team, which includes ensuring the system is scaled appropriately to handle demand.

Install/run as a self-hosted service

Bito's Governor cuts your coding agent bill in half while task quality holds.

Governor is a context-driven model router that sits between your AI coding tools (Claude Code, Cursor, Codex, etc.) and your LLM providers (Anthropic, OpenAI, Groq, etc.). It reduces the tokens each task consumes and the price you pay for every token.

Your developers keep their tools and their workflow. You point those tools at Governor with one base URL and one key, and every request after that costs less.

Get accessLearn more

How Governor cuts agent spend

Agent spend equals the number of tokens multiplied by the price of each token. Governor reduces both numbers in one layer, so the savings multiply rather than add.

• Fewer tokens, through the code context engine. A coding agent spends most of its tokens searching your codebase for where a change belongs, and every file it reads gets billed again on each later step of the session. Bito's context engine serves system context from a live knowledge graph of your code, business context, and tribal knowledge, so your tools start each task already knowing your architecture and skip the discovery work.

• Lower price per token, through the model router. Governor scores how complex each request is and sends it to the best model for that work, reserving frontier models for the requests that need them. You define the routing tiers in your own words, set the routing direction and a price ceiling, and review every decision in shadow mode before it touches live traffic.

Getting started

Get your team running on Bito Governor. Connect your LLM providers, set up routing and gateway keys, enable AI Architect, and point your coding tools at it.

The context engine behind Governor

Governor draws its context from Bito's code context engine, a living knowledge graph of your repositories, modules, and APIs, combined with the tribal knowledge in your issue trackers and docs. Your team gets a shared, always-current understanding of the system, and makes that understanding available wherever engineering decisions are made:

• In Jira and Linear, for feasibility analysis, technical design and scoping, and cross-repo impact assessment.

• In coding agents via MCP, such as Claude Code, Cursor, and Codex, for grounded code generation, accelerated onboarding, and production issue triage.

• In pull requests (GitHub, GitLab, Bitbucket) for codebase-aware AI code reviews.

• for on-demand architecture questions, incident triage, and team-wide access to codebase knowledge.

• for pulling design docs, RFCs, and runbooks into ticket analysis — and creating, updating, and commenting on pages from Jira or Slack.

• (Claude.ai, Claude Desktop, ChatGPT) for conversational access to your codebase knowledge, system context, and architectural insights.

To set up AI Architect (code context engine) on its own, see:

• Install AI Architect (Bito-hosted) (Fully managed by Bito — no infrastructure setup required)

• Install AI Architect (self-hosted) (Run AI Architect on your own infrastructure for maximum control)

AI Code Review Agent

Bito's AI Code Review Agent runs system-aware code reviews on pull requests in GitHub, GitLab, and Bitbucket. You can also run it in your IDE and CLI.

• Use in Git (GitHub, GitLab, Bitbucket)

• Use in IDE (VS Code, Cursor, Windsurf, JetBrains)

• Use in CLI (integrates seamlessly with AI coding agents like Cursor, Claude Code, Windsurf, and others.)

Security

Bito does not store your code or use it to train AI models. Bito is SOC 2 Type II certified, and Governor stores every LLM provider key envelope encrypted. On a self-hosted deployment, the key that encrypts your provider credentials stays on your own host or cluster.

Helpful resources

Governor

Code context engine

AI Code Review Agent

Video library

Need help?

If you have any questions, feel free to email us at support@bito.ai

Bito's AI Architect builds a knowledge graph of your codebase (from repos to modules to APIs) and operational history (context from issue trackers such as Jira). This gives your team a shared, always-current understanding of the system, and makes that understanding available wherever engineering decisions are made:

• In issue trackers (Jira) for feasibility analysis, technical design, and cross-repo impact assessment.

• In coding agents (Claude Code, Cursor, Windsurf, GitHub Copilot, and more) for grounded code generation, accelerated onboarding, and production issue triage.

• In pull requests (GitHub, GitLab, Bitbucket) for codebase-aware AI code reviews

This fundamentally changes the game for enterprises with many microservices or large, complex codebases.

Key capabilities of the AI Architect include:

• Technical design and planning — AI Architect analyzes your Jira tickets and posts a complete implementation plan directly in comments section: feasibility assessment, story breakdown, effort estimates, risk flags, and dependency mapping.

  • Watch demo video

• Grounded 1-shot production-ready code — The AI Architect learns all your services, endpoints, code usage examples, and architectural patterns. The agent automatically feeds those to your coding agent (Claude Code, Cursor, Codex, any MCP client) to provide it the necessary information to quickly and efficiently create production ready code.

• Consistent design adherence — Code generated aligns with your architecture patterns and coding conventions.

• Spec-driven development — Automatically generate highly detailed, implementation-ready technical requirement documents (TRDs) and low-level designs (LLDs) with a deep, context-aware understanding of your codebase, services, and design patterns, ensuring architectural integrity and consistency at a granular level.

• Triaging production issues — Easily and quickly find root causes to production issues based on errors/logs/etc.

• Faster onboarding — New engineers or AI agents can quickly understand how a system or component system structure.

• Enhanced documentation and diagramming — Through its internal understanding of interconnections between modules and APIs.

• Smarter code reviews — Reviews with system-wide awareness of dependencies and impacts.

The AI Architect builds the knowledge graph by analyzing all your repositories (whether you have 50 or 5,000 repos) to learn about your codebase architecture, microservices, modules, API endpoints, design patterns, and more.

How you can use AI Architect

AI Architect is designed to be flexible and can power multiple use cases across different AI coding tools and workflows.

Why use the AI Architect?

Most AI coding tools struggle with accuracy in real-world codebases because they

1. Don’t fully understand the breadth and depth of your codebase. They read some of the code in your existing repository, but they don’t have a complete graph of your internal APIs, endpoints, libraries, etc. On top of that, if you are accessing a monorepo or many services not available on your machine locally, they have no context or get confused trying to access them. Bito’s AI Architect has built a knowledge graph to provide this information in a cheap and efficient way to your coding agent so it can accomplish the task with grounded and complete information.

2. They don’t fully understand how all of your services and modules interact with each other when you are trying to understand your overall system versus just one component. The AI Architect’s graph contains a mapping of all the dependencies, allowing to provide sophisticated analysis – how you would expect an Architect too.

How AI Architect differs from Embeddings?

Traditional embeddings work like a search engine — they retrieve code snippets or documents similar to a given query.

They can find related content but can’t understand how different parts of your system work together.

The AI Architect, on the other hand:

• Builds a knowledge graph that captures relationships between repositories, modules, APIs, and libraries.

• Provides precise answers and implementations, not just search results.

• Understands context and intent — how and why something is implemented in your codebase.

• Enables system-aware reasoning, allowing AI agents to generate or review code with full architectural understanding.

Getting started

1. Try Bito's AI Architect

2. Get a demo with our team.

3. Lastly, email support@bito.ai if you have any additional questions.

Demos of different ways to use AI Architect

Bito's AI Architect can be self-hosted in two ways depending on your use case. Pick the one that matches how you plan to use it:

1. Standalone mode (for individuals)

A lightweight, single-machine install for individual developers who want to evaluate AI Architect or use it for personal development work. It runs entirely in Docker on your local machine (e.g., laptop) and automatically registers itself with your installed coding agents — no shared infrastructure, no DevOps coordination required.

👉 Go to Standalone mode setup guide

2. Enterprise mode (for teams)

A full-featured, multi-user deployment of Bito's AI Architect designed for teams who want to share a centralized knowledge graph across the organization. It supports both Docker Compose and Kubernetes, can be hosted on any on-premise or cloud infrastructure you control, and includes SSO authentication.

👉 Go to Enterprise mode setup guide

Quick comparison

Common prerequisites

Regardless of which mode you choose, you'll need the following before you start. Each setup guide covers these in full detail:

• A Bito account and Bito Access Key — sign up at https://alpha.bito.ai and generate a key from Settings → Advanced settings.

• A Git Access Token for your provider — GitHub, GitLab, Bitbucket, or Azure DevOps.

• LLM API keys — Bito's AI Architect uses Large Language Models (LLMs) to build a knowledge graph of your codebase. Provide an API key for the LLM provider you plan to use, such as:

  • Anthropic (Claude)

  • OpenAI (GPT)

  • Portkey

  • Google Vertex AI

  • Azure AI

  • Novita

  • AWS Bedrock

  • Google Gemini

  • Local OpenAI-compatible (bring your own server)

• Docker Desktop (or Docker Service on Linux) running on the host machine.

Not sure which mode to pick?

• If you're a single developer trying AI Architect for the first time on your own machine, start with Standalone mode. It's the fastest way to get up and running.

• If you're rolling out AI Architect to a team, need a single shared knowledge graph across developers, or have requirements like Kubernetes or SSO, go with Enterprise mode.

Next steps

• Standalone mode setup guide

• Enterprise mode setup guide

• Available CLI commands

If you no longer need an AI Code Review Agent instance, you can delete it to keep your workspace organized. Follow the steps below to quickly remove any unused Agents.

1. Log in to Bito Cloud and select a workspace to get started.

2. From the left sidebar, select Code Review Agents.

   If your Bito workspace is connected to your GitHub/GitLab/Bitbucket account, a list of AI Code Review Agent instances configured in your workspace will appear.

1. Before deleting an Agent, ensure that any repositories currently using it are reassigned to another Agent otherwise a warning popup will appear.

1. Locate the Agent you wish to delete and click the Delete button given in front of it.

Ask questions directly to the AI Code Review Agent regarding its code review feedback. You can inquire about highlighted issues, request alternative solutions, or seek clarifications on suggested fixes.

Real-time collaboration with the AI Code Review Agent accelerates your development cycle. By delivering immediate, actionable insights, it eliminates the delays typically experienced with human reviews. Developers can engage directly with the Agent to clarify recommendations on the spot, ensuring that any issues are addressed swiftly and accurately.

Bito supports over 20 languages—including English, Hindi, Chinese, and Spanish—so you can interact with the AI in the language you’re most comfortable with.

How to chat?

To start a conversation, type your question directly as a reply to the Agent’s code review comment.

The AI Code Review Agent will analyze your comment and determine if it’s a valid and relevant question.

• If the agent decides it’s a valid question, it will respond with helpful insights.

• If the agent determines it’s unclear, off-topic, or not related to its feedback, it will not respond.

To help the agent recognize your question faster, you can also tag your comment with @bitoagent or @askbito. Tagging informs the Agent that your message is intended as a question. However, tagging does not guarantee a reply. The agent will still analyze your comment and decide whether it is a valid question worth responding to.

Bito usually responds within about 10 seconds.

• On GitHub and Bitbucket, you may need to manually refresh the page to see the response.

• On GitLab, updates happen automatically.

What you can ask about

When chatting with the AI Code Review Agent, you can ask questions to better understand or improve the code feedback it provided. Here are examples of what you can ask:

• Clarifications about a highlighted issue Ask the AI to explain why it flagged a certain line of code or why something might cause a problem.

• Request for alternative solutions Request different ways to fix or improve the code beyond what was originally suggested.

• Deeper explanations If you want to understand the technical reasoning behind a suggestion (e.g., security concerns, performance impacts, best practices), you can ask for more detailed explanations.

• Request for examples Ask the AI to provide an example snippet showing the corrected or improved code.

• Trade-off discussions Ask the AI about pros and cons of different approaches it may have suggested (e.g., performance vs. readability).

• Best practices guidance Request advice on best practices related to the specific code snippet — such as naming conventions, error handling, optimization tips, or design patterns.

• Language-specific advice If you’re working in a particular language (e.g., JavaScript, Python, Java), you can ask for language-specific guidance related to the comment.

• Request for more context If the suggestion feels too "short" or "surface level," you can ask the AI to explain more about the broader coding or architectural concept behind its feedback.

• Security and safety questions If a suggestion touches on security (like input validation, authentication, or encryption), you can ask for further security-related advice.

• Testing and validation Ask the AI if it recommends writing any tests based on its code suggestions and what those tests might look like.

What you cannot ask about

The AI can only answer questions related to its own code review comments.

• You cannot ask general questions about the repository or unrelated topics.

• You cannot start a new thread independently — your question must be a reply to a comment made by Bito’s AI Code Review Agent.

If your comment is not linked to a Bito review comment, the AI will not respond.

Bito integrates with Confluence to enhance functional validation by enriching pull request requirements with deeper context from linked documentation.

It fetches relevant details from Confluence pages linked in Jira issues or directly in pull request – such as edge cases, design decisions, or test scenarios – and incorporates them into structured validation results.

This improves accuracy when validating code changes and ensures that every pull request is aligned with the requirements.

How it works

When a pull request is opened, Bito automatically:

1. Detects Confluence references. Bito looks for Confluence page links in the pull request description or in any linked Jira issues.

1. Fetches documentation. Bito retrieves the content of those Confluence pages and extracts key information (requirements, design decisions, acceptance criteria, etc.).

2. Validates code against the docs. It compares your code changes to the enriched context and generates structured validation feedback.

3. Reports results in the PR. Bito posts a "Functional Validation" table directly in your pull request comments, showing how each documented requirement is met, missed, or partially implemented.

Integration steps

Linking Confluence pages to pull requests

For Bito to fetch the right documentation, your pull requests must reference the relevant Confluence pages:

• With Jira integration: Ensure your Jira issues link to the Confluence pages (for example, by using the Jira Issue macro on the Confluence page). When a Confluence page mentions a Jira issue key, Jira automatically creates a link to that page. Bito will follow those links to gather the documentation context.

• Without Jira integration: Include a link to the Confluence page directly in the pull request description. Bito will use that URL to retrieve the page content for validation.

Understanding the validation results

When Bito completes its analysis, it adds a "Functional Validation by Bito" table to your pull request comments. This table contains four columns:

Source

Displays the Jira issue key (e.g., "QP-11", "QP-123") that references the specific Jira ticket being validated.

Requirement / Code Area

Shows a brief description of the requirement or task that needs to be completed, summarizing what needs to be done according to the Jira ticket and Confluence page.

Status

Indicates the completion status of each requirement:

• Met: The requirement has been fully implemented in the pull request

• Missed: The requirement has not been addressed in the pull request

• Partial: The requirement has been partially implemented but still needs additional work

• Conflict: A change contradicts another requirement (e.g., two requirements cannot both be satisfied by the current code).

• Out‑of‑scope: The change is not in the requirements (the code change does not relate to any defined requirement).

Notes

Provides detailed information about the validation status:

• For "Met" items: Explains what has been successfully implemented

• For "Missed" items: Describes what is missing and needs to be addressed

• For "Partial" items: Details what has been completed and what still remains to be done

• For "Conflict" items: Describes why there is a contradiction between requirements and what might need to be resolved.

• For "Out‑of‑scope" items: Explains why the change is considered outside the defined requirements.

Example validation output

Here's what a typical validation table looks like:

Benefits

• Improved traceability: Syncing code validation with Confluence documentation creates a clear audit trail from requirements to implementation.

• Single source of truth: By pulling from Confluence (the team’s central documentation hub), Bito ensures developers review code against the definitive requirements. Teams spend less time context-switching between tools.

• Aligned development: Automatic validation against documented scenarios helps catch missing features or edge cases early, reducing manual review effort and improving quality.

Troubleshooting

• No "Functional validation" table in pull request: Check that Confluence is connected under Manage integrations and that the Functional Validation setting is enabled for your Agent. Also verify that your PR or its linked Jira issues actually reference the Confluence pages.

• Missing or incorrect context: Make sure the Confluence pages contain the up-to-date requirements or test scenarios.

• Authorization errors: If Bito can't access Confluence, try re-authorizing the integration. Ensure your Atlassian account has permission to read the relevant Confluence pages.

Bito's AI Code Review Agent supports different configuration methods depending on the deployment environment:

1. Bito-hosted – The agent runs on Bito's infrastructure and is configured through the Bito web UI.

2. Self-hosted – The agent runs on user-managed infrastructure and is configured by editing the bito-cra.properties file.

The sections below provide configuration guidance for each setup.

Bito-hosted agent configuration

In Bito-hosted AI Code Review Agent, you can configure the agent through the Bito web UI.

To customize an existing agent, open the Code Review > Repositories page and click the Settings button next to the Agent instance to be modified.

The agent settings page allows configuration of options such as:

• Agent name – Define a unique name for easy identification.

• Review options – Choose the review mode (Essential or Comprehensive), set feedback language, and enable features like auto-review, incremental review, summaries, and change walkthroughs.

• Custom guidelines – Create and apply custom code review rules tailored to your team’s standards directly from the dashboard.

• Filters – Exclude specific files, folders, or branches from review to focus on relevant code.

• Tools – Enable additional checks, such as secret scanning and static analysis.

• Chat – Configure how the agent responds to follow-up questions in pull request comments and manage automatic replies.

These settings tailor the agent’s behavior to match team workflows and project needs. For detailed guidance, see Create or customize an Agent instance.

Self-hosted agent configuration

In self-hosted deployments, configuration is managed by editing the bito-cra.properties file. This file defines how the agent operates and connects to required services.

Key configuration options include:

• Mode

  • mode = cli: Processes a single pull request using a manual URL input.

  • mode = server: Runs as a webhook service and listens for incoming events from Git platforms.

• Authentication

  • bito_cli.bito.access_key: Required for authenticating the agent with the Bito platform.

  • git.provider, git.access_token, etc.: Required for connecting to the appropriate Git provider (e.g., GitHub, GitLab, Bitbucket).

• General feedback settings

  • code_feedback: Enables or disables general feedback comments in reviews.

• Analysis tools

  • static_analysis: Enables static code analysis.

  • dependency_check: Enables open-source dependency scanning.

  • dependency_check.snyk_auth_token: Required when using Snyk for vulnerability detection.

• Review format and scope

  • review_comments: Defines output style (e.g., single post or inline comments).

  • review_scope: Limits the review focus to specific concerns such as security, performance, or style.

• Filters

  • include_source_branches and include_target_branches: Restrict reviews to pull requests that match specified source and target branch patterns.

  • exclude_files: Skips selected files based on glob patterns.

  • exclude_draft_pr: Skips draft pull requests when enabled (default: True).

Each property is documented in detail on the bito-cra.properties file documentation page.

Get up and running with Bito in just a few steps! Bito seamlessly integrates with popular JetBrains IDEs such as IntelliJ IDEA, PyCharm, and WebStorm, providing powerful AI-driven code reviews directly within your editor. Click the button below to quickly install the Bito extension and start optimizing your development workflow with context-aware AI Chat, and more.

Video guide

Watch the video below to learn how to download the Bito extension on JetBrains IDEs.

Step-by-step instructions

1. In JetBrains IDEs such as IntelliJ, go to File -> Settings to open the Settings dialog, and click Plugins -> Marketplace tab in the settings dialog. Search for Bito.

2. Click "Install" to install the Bito extension. We recommend you restart the IDE after the installation is complete.

3. Bito panel will appear on the right-hand sidebar. Click it to complete the setup process. You will either need to create a new workspace if you are the first in your company to install Bito or join an existing workspace created by a co-worker. See Managing workspace members

Get up and running with Bito in just a few steps! Bito seamlessly integrates with Cursor, providing powerful AI-driven code reviews directly within your editor. Click the button below to quickly install the Bito extension and start optimizing your development workflow with context-aware AI Chat, and more.

Install on Cursor

Installation Steps

Step 1: Open Cursor IDE

Launch Cursor IDE on your computer.

Step 2: Access the extensions marketplace

From the top menu, click View -> Extensions to open the Extensions marketplace.

Step 3: Search for Bito and install the extension

1. In the Extensions search bar at the top, type "Bito"

2. Look for the official Bito extension in the search results. The extension should be published by "Bito".

3. Click the Install button

4. Wait for the installation to complete (this usually takes just a few seconds)

Step 4: Sign in to Bito

Once installed, you'll need to authenticate:

1. Click "Sign up or Sign-in"

2. Bito authentication screen will display.

3. Log in with your Bito account credentials (or create a new account)

Step 5: Verify installation

To confirm Bito is working correctly:

1. You should see the Bito icon in your Cursor IDE sidebar

2. Click on it to open the Bito panel

3. You should now have access to all Bito features

Keeping your Bito plugin up to date ensures you have access to the latest features and improvements. In this article, we will guide you through the steps to update the Bito plugin on both VS Code and JetBrains IDEs. Let's dive in!

Updating Bito Plugin on VS Code

1. Open your VS Code IDE

2. Navigate to the Extensions view by clicking on the square icon in the left sidebar

3. In the search bar, type "Bito" to locate the Bito plugin

4. Once you locate the Bito plugin, click on the update button to initiate the update

Pro Tip 💡: Enable Auto-update for Bito Plugin on VS Code (as shown in the video)

Updating Bito Plugin on JetBrains IDEs

1. Open your JetBrains IDE (e.g., IntelliJ IDEA, PyCharm, etc.)

2. Go to Settings by clicking on "File" in the menu bar (Windows/Linux) or by clicking on "IntelliJ IDEA" in the menu bar (macOS).

3. In the Settings window, navigate to the "Plugins" section

4. Switch to the "Installed" tab to view the list of installed plugins

5. Locate the Bito plugin in the list and click on the update button to initiate the update

Bito AI chat is the most versatile and flexible way to use AI assistance. You can type any technical question to generate the best possible response. Check out these Use cases and examples to understand all you can do with Bito.

To use AI Chat, type the question in the chat box, and press 'Enter' to send. You can add a new line in the question with 'SHIFT+ ENTER'.

Bito starts streaming answers within a few seconds, depending on the size and complexity of the prompt.

Bito makes it super easy to use the answer generated by AI, and take a number of actions.

Copy Answer

Copy the answer to the clipboard.

Regenerate Answer

AI may not give the best answer on the first attempt every time. You can ask Bito AI to regenerate the answer by clicking "Regenerate" button next to the answer.

Rate Response

Vote response "Up" or "Down". This feedback Bito improve the prompt handling.

Modify Last Prompt

Many of these commands can be executed with keyboard shortcuts documented here: Keyboard shortcuts

Bito automatically saves the chat session History. The session history is stored locally on your computer. You can return to any chat session and continue the AI conversation from where you left off. Bito will automatically maintain and restore the memory of the loaded chat session.

You can "Delete" any saved chat session or share a permalink to the session with your coworkers.

Here is the video overview of accessing and managing the session history.

Bito has created the ability for our AI to understand your codebase, which produces dramatically better results that are personalized to you. This can help you write code, refactor code, explain code, debug, and generate test cases – all with the benefits of AI knowing your entire code base.

Bito AI automatically figures out if you're asking about something in your code. If it's confident, it grabs the relevant parts of your code from our index and feeds them to the Large Language Models (LLMs) for accurate answers. But if it's unsure, Bito will ask you to confirm before proceeding.

For now, this feature is only available for our Team Plan which costs $15 per user per month. We have plans to release it for our Free Plan soon. But it will be limited to repos of 10MB indexable size.

Recent breakthroughs in Generative AI and Large Language Models (LLMs) have helped make many AI Coding Assistant tools available, including Bito, to help you develop software faster.

The major issue with these AI assistants, though, is that they have no idea about your entire codebase. Some tools take context from currently opened files in your IDE, while others enable you to manually enter code snippets in a chat-like interface and then ask questions about them.

But with Bito’s AI that understands your entire repository, this is a whole new capability. For example, what if you could ask questions like:

• how can I add a button to mute and unmute the song to my code in my music player? By default, set this button to unmute. Also, use the same design as existing buttons in UI.

• In my code list all the files and code changes needed to add column desc in table raw_data in dailyReport DB.

• In my code suggest code refactoring for api.py and mention all other files that needs to be updated accordingly

• Please write the frontend and backend code to take a user’s credentials, and authenticate the user. Use the authentication service in my code

This will definitely improve the way you build software.

When you open a project in Visual Studio Code or JetBrains IDEs, Bito lets you enable the indexing of code files from that project’s folder. Basically, this indexing mechanism leverages our new AI Stack that enables Bito to understand your entire codebase and answer any questions regarding it.

The index is stored locally on your system to provide better performance while maintaining the security/privacy of your private code.

How to Ask Questions?

Once indexing is complete, you can ask any question in the Bito chatbox. Bito uses AI to determine if you are asking about something in your codebase. If Bito is confident, it grabs the relevant parts of your code from our index and feeds them to the Large Language Models (LLMs) for accurate answers. But if it's unsure, Bito will ask you to confirm before proceeding.

In case you ask a general question (not related to your codebase), then Bito will directly send your request to our LLM without first looking for the appropriate local context.

However, if you want to ask a question about your code no matter what, then you can use specific keywords such as "my code", "my repo", "my project", "my workspace", etc., in your question.

The complete list of these keywords is given on our Available Keywords page.

Once Bito sees any input containing these keywords, it will use the index to identify relevant portions of code or content in your folder and use it for processing your question, query, or task.

Security of your code

As usual, security is top of mind at Bito, especially when it comes to your code. A fundamental approach we have taken is to keep all code on your machine, and not store any code, code snippets, indexes, or embedding vectors on Bito’s servers or our API partners. All code remains on your machine, Bito does not store it. In addition, none of your code is used for AI model training.

Learn more about Bito’s Privacy and Security Practices.

Plugins add optional capabilities — like answering questions in Slack or analyzing Jira tickets — on top of your self-hosted AI Architect installation. Each plugin is a self-contained service you install, configure, and remove with a single command. Installing, updating, or removing a plugin never affects the base platform, its databases, or your indexed repositories.

This guide covers installing and managing plugins on an existing self-hosted deployment. If AI Architect itself is not installed yet, set it up first — see Install AI Architect (self-hosted).

Available plugins

Both plugins run on the same platform your base install already provides. Any shared infrastructure a plugin needs (such as an event bus or cache) is provisioned automatically on first use and shared across plugins.

Before installing a plugin, make sure you have:

1. A running self-hosted AI Architect deployment. Check with bitoarch status.

2. Plugin entitlement on your plan. Plugins are part of the Bito Enterprise plan. If your workspace is not entitled, the installer stops and tells you.

3. A public URL that Slack or Jira can reach. Both plugins receive events from an external service, so the plugin's endpoint must be reachable from the internet over HTTPS with a valid certificate. You provide this during setup.

Every plugin installs with a single command that runs a short guided setup:

The installer resolves shared dependencies, provisions the plugin's database, deploys the service, and then prompts you for exactly what that plugin needs. You don't pass values as flags — you answer the prompts, and each answer applies immediately. Check health at any time with bitoarch plugin status <name>, and change anything later with bitoarch plugin config <name> (it re-runs the same prompts). If you skip a required value, the plugin is left pending and the output tells you how to finish — nothing is half-installed.

The two plugins and their setup steps follow.

You need admin rights to create a Slack app, and a public HTTPS URL Slack can reach. Start the install:

The guided setup then walks you through:

1. Public URL. Enter the public HTTPS address where the bot is reachable.

2. Create the Slack app. The setup prints a ready-to-paste Slack app manifest — your URL and every event/request URL are already filled in — and the path to the file to copy it from. Open → Create New App → From an app manifest, paste it, and create the app.

3. Credentials. From the new app's Basic Information → App Credentials, the setup collects the Client ID, Client Secret, and Signing Secret.

Once it reports healthy, add the bot to a channel with /invite @Bito, then mention @Bito with a question or send it a direct message — it reads the full thread (and supported text attachments) for context. To change any of these settings later, run bitoarch plugin config ai-assistant, which re-runs the same steps.

You need a public HTTPS URL Jira can reach, and access to your Jira project. Start the install:

The guided setup then walks you through:

1. Public URL. Enter the public HTTPS address where the webhook receiver is reachable.

2. Connect Jira. If you already connected Jira for Insights, the plugin reuses that connection. Otherwise it asks for your Jira site URL, account email, and an API token (Jira Cloud) or personal access token (Jira Server/Data Center).

3. Register the webhook.

To change the respond mode, watched projects, or the Jira connection later, run bitoarch plugin config task-analyzer, which re-runs the same steps.

This shows the plugin's current configuration and walks you through the same setup prompts, so you can update the public URL, credentials, connection, or any plugin-specific option. Changes apply immediately.

Upgrades are atomic and preserve your configuration and data. If an upgrade needs a credential you haven't provided, it holds and tells you what to add rather than starting a broken version. Installed plugins are also checked for updates daily, while at least one plugin is installed.

By default, uninstalling keeps the plugin's database and configuration, so reinstalling restores your setup. To also delete them, add --purge — on purge, Bito Task Analyzer additionally removes the webhook it registered in your self-hosted Jira.

The base lifecycle commands are plugin-aware: bitoarch stop, start, restart, and update include your installed plugins automatically.

Yes. Each plugin is independent. A customer can install either or both.

Uninstalling keeps the plugin's data and configuration by default, so reinstalling restores everything. A full purge is available if you want a clean removal.

No. Plugins run on the same infrastructure the user already deployed for AI Architect. However the minimum server requirements has slightly modified.

Yes. Plugins have their own version and can be upgraded independently via bitoarch plugin upgrade. A base platform upgrade also upgrades any installed plugins automatically.

Yes. The Task Analyzer supports Jira Cloud, Jira Server, and Jira Data Center. Self-hosted Jira gets automatic webhook registration; Cloud requires one manual step due to technical constraint.

Templates help you improve your code quality instantly with AI-powered analysis. Get automated suggestions for performance optimization, security fixes, style improvements, and code cleanup without leaving your editor. Each template provides actionable feedback and ready-to-use code improvements that you can review and apply with a single click.

Available templates

1. Performance Check: Optimize code performance and efficiency

2. Security Check: Identify and fix security vulnerabilities

3. Style Check: Apply coding style and formatting standards

4. Improve Readability: Enhance code clarity and organization

5. Clean Code: Remove debugging and logging statements

Select the code you want to analyze in your editor before using any template.

1. Select code in your editor

2. Click the Templates button at the bottom of the Bito extension panel

1. Choose the desired template from the dropdown menu

1. Select code in your editor

2. Right-click in the editor window

3. Hover over "Bito AI" in the context menu

4. Select the desired template from the submenu

1. Select code in your editor

2. Type / at the start of the Bito chat box

3. Choose the desired template from the dropdown menu

1. Type some text after the slash / to filter templates by name

1. Select code in your editor

2. Go to View → Command Palette (or press Ctrl+Shift+P / Cmd+Shift+P)

3. Type "bito" to see available templates

4. Select the desired template from the list

When templates provide code improvements, you'll see an Apply button above the suggested code snippet.

1. Click the Apply button to open the diff view

1. Review the changes highlighted in the diff:

   • Red lines show code to be removed

   • Green lines show code to be added

2. Choose your action:

• Select meaningful code blocks for better analysis results

• Templates work best with complete functions or logical code segments

• Review suggested changes before applying them to your codebase

• Verify that the changes don't break existing functionality

Bito-hosted AI Architect runs on managed infrastructure and unifies your codebase, tickets, design docs, and team conversations into a single knowledge graph.

Your coding agents, issue trackers, and chat tools query that graph so planning, design, coding, review, and incident discussions all share the same system context.

Follow this guide to connect a Git provider, pick repositories for indexing, and wire AI Architect into Claude Code, Cursor, Jira, Linear, Slack, Confluence, and the other tools your team already uses.

Prerequisites

Bito-hosted AI Architect is currently invitation-only. You can start a 14-day free trial to get instant access, no credit card required. If you're on a free or paid plan and would like access for your workspace, please contact support@bito.ai.

Installation steps

Bito-hosted AI Architect includes smart code retrieval () that works behind the scenes. When you ask questions about specific code — like "show me the authenticate method in the Auth class" — AI Architect automatically fetches the relevant code at runtime using your Git credentials.

This means you get detailed, accurate code references without storing any code on Bito's servers. Code is fetched on-demand and never persisted.

Bito offers two deployment options for AI Architect:

1. Bito-hosted (Fully managed by Bito — no infrastructure setup required)

2. Self-hosted (Run AI Architect on your own infrastructure for maximum control)

For information on self-hosted deployment, visit the .

If you have questions about the setup process or need to modify your indexed repositories, reach out to the Bito team at . We're here to help you get the most out of AI Architect.

Bito Cloud lets you integrate the AI Code Review Agent into your CI/CD pipeline for automated code reviews. This document provides a step-by-step guide to help you configure and run the script successfully.

Installation and Configuration Steps

1. Select the appropriate Git provider guide from this link based on your Git provider, and follow the step-by-step instructions to install the AI Code Review Agent using Bito Cloud. Be sure to review the prerequisites and the installation/configuration steps provided in the documentation.

2. Download the bito-action-script folder from GitHub, which includes a shell script (bito-actions.sh) and a configuration file (bito_action.properties).

3. You can integrate the AI Code Review Agent into your CI/CD pipeline in two ways, depending on your preference:

• Option 1: Using the bito_action.properties File

  • Configure the following properties in the bito_action.properties file located in the downloaded bito-action-script folder.

• Run the following command:

  • ./bito_actions.sh bito_action.properties

  • Note: When using the properties file, make sure to provide all the three parameters in .properties file

• • Provide all necessary values directly on the command line:

    • ./bito_actions.sh agent_instance_url=<agent_instance_url> agent_instance_secret=<secret> pr_url=<pr_url>

    • Replace <agent_instance_url>

1. Incorporate the AI Code Review Agent into your CI/CD pipeline by adding the appropriate commands to your build or deployment scripts. This integration will automatically trigger code reviews as part of the pipeline, enhancing your development workflow by enforcing code quality checks with every change.

Easily share insights from any AI Chat session by creating a unique shareable link directly from the Bito extension in VS Code or JetBrains IDEs.

Whether you need to share AI-generated code suggestions, explanations, or any other chat insights, this feature allows you to create a public link that others can access. The link will remain active for 15 days and can be viewed by anyone with access to the URL, making collaboration and knowledge sharing seamless.

Additionally, you can quickly share your AI Chat session through a pre-written Tweet or an Email.

Let's see how it is done:

1. Open Bito in Visual Studio Code or any JetBrains IDE.

2. Start a conversation in Bito’s AI Chat user interface.

3. Locate the share button on the top right of the Bito extension side-panel.

4. Click the share button to open a menu with options, including X (Twitter), Email, and Link.

5. Share on X (Twitter):

   1. Click on X (Twitter) from the menu, and a dialogue window will appear, asking whether you want to open the external site.

   2. Simply click "Open" to proceed.

6. Share Through Email:

   1. Click on Email from the menu, and you will be redirected to your email application.

   2. Select your email account if needed.

   3. The email will be pre-filled with all the necessary information, including the link to your Chat Session.

7. Share the Link:

   1. Click on Link from the menu.

   2. A confirmation popup will appear. Click Share session to generate a unique URL for your chat session, which will automatically be copied to your clipboard for easy sharing.

This guide will help you set up and start using Bito's AI-powered tools. Whether you're looking to enhance feedback of your coding agents with AI Architect or automate code reviews with our AI Code Review Agent, you'll find everything you need below.

AI Architect

Auto-routing sends each request to the model that fits the work. Simple requests go to a cost-efficient, faster model, and complex requests go to a more capable one.

Without it, every request reaches the same model. Renaming a variable costs the same as planning a migration across four services, because both go to whichever model the coding tool asked for.

sorts each request into one of four tiers, Simple, Medium, Complex, and Reasoning, then sends it to the model you assigned to that tier. You write the description that defines each tier, so requests are sorted by your own words rather than by a fixed rule.

Your engineers change nothing. Their tools keep asking for the same model name, and Governor answers from whichever model it selected.

You turn auto-routing on one model name at a time. That name is the alias your coding tools ask for, such as claude-opus-5 or claude-*. Each alias appears as a group on the Routes page, with a Direct and an Auto control.

MCP server provides a comprehensive suite of tools for exploring, analyzing, and understanding your organization's codebase. These tools enable AI coding assistants to access deep repository intelligence, architectural insights, and code-level information across all your Git repositories.

Below is the complete list of MCP tools provided by AI Architect:

AI Architect is a full engineering workflow platform built around a knowledge graph of your codebase, business context, tribal knowledge, docs, tickets, and observability data. It operates across three solution areas — technical design & scoping, grounded coding, and code review — surfacing system-level intelligence at every stage of development.

This page covers the core features that power those workflows.

The knowledge graph is the context engine at the core of AI Architect. It ingests your codebase, Git history, issue tracker data (Jira, Linear), documentation (Confluence), observability signals, and Slack conversations into a unified, continuously updated graph.

Rather than treating your codebase as searchable text, the knowledge graph models the relationships between services, APIs, dependencies, past decisions, and recurring incident patterns — capturing how your system actually fits together.

This shared context powers every capability in AI Architect. When an agent runs a feasibility analysis, generates a technical design, or reviews a pull request, it draws from the same knowledge graph — so output is grounded in your real architecture, not generic patterns.

The knowledge graph also enables cross-repo reasoning: it understands blast radius, tracks instability histories, and connects a Jira incident from six months ago to the service a developer is about to change today.

The Bito free trial gives you access to premium features for 14 days, allowing you to experience the full capabilities of Bito's AI-powered coding assistant.

You can start your free trial directly from the Bito IDE extension using any of the three methods below.

The easiest way to start your trial is through natural interaction:

1. Type a message in the Bito chat box and send it.

2. Look for the popup that appears after sending your message.

The self-hosted AI Code Review Agent offers a more private and customizable option for teams looking to enhance their code review processes within their own infrastructure, while maintaining complete control over their data. This approach is ideal for organizations with specific compliance, security, or customization requirements.

When setting up the AI Code Review Agent, you have the flexibility to choose between two primary modes of operation: CLI and webhooks service.

• CLI allows developers to manually initiate code reviews directly from terminal. This mode is ideal for quick, on-demand code reviews without the need for continuous monitoring or integration.

• Webhooks service transforms the Agent into a persistent service that automatically triggers code reviews based on specific events, such as pull requests or comments on pull requests. This mode is suitable for teams looking to automate their code review processes.