Bito

What Bito does

Bito's AI Architect is a context layer that powers the entire engineering workflow, enabling every agent to reason like a top architect. It addresses the fragmentation of engineering context across codebases, Jira tickets, Confluence docs, Slack threads, and senior engineers' knowledge. AI Architect builds a knowledge graph from all these sources, mapping services, dependencies, APIs, and operational history across every repository. This context then powers every phase of the engineering workflow, including technical design and feasibility analysis, grounded code generation, and codebase-aware code reviews. Bito emphasizes that no code is stored, and no model is trained on customer code, and the company is SOC 2 Type II certified. They aim to accelerate developer productivity and innovation by leveraging AI-driven development.

Products

• AI ArchitectA context layer that builds a knowledge graph from code, commits, issues, and documentation, mapping services, APIs, dependencies, and architectural patterns across all repositories. It provides system context for feasibility analysis, technical design, impact assessment, scope breakdown, grounded code generation, accelerated onboarding, and production issue triage.
• AI Code ReviewsProvides codebase-aware pull request reviews with cross-repo impact analysis, dependency risk detection, and blast radius mapping. It offers 1-click apply for AI fixes, chat with the agent, static code analysis, linter support, security analysis, incremental code reviews, smart reports, and custom review guidelines.
• OrchestraAn autonomous agent system for complex, cross-service changes across repositories. It splits changes into tasks, resolves dependencies, and runs a fleet of agents grounded in the AI Architect knowledge graph to implement, test, and review changes.
• Bito Issue AnalyzerAnalyzes issues and epics against the live codebase to deliver feasibility analysis, technical design, and effort estimation directly in Jira and Linear.
• Bito Slack AgentAnswers system-level engineering questions in Slack, grounded in the knowledge graph, covering architecture, dependencies, past decisions, and incident history.

Key capabilities

• Deep technical designs grounded in code, commits, docs, and issues
• Knowledge graph creation from code, commits, issues, and docs
• Mapping of services, APIs, dependencies, and architectural patterns
• Feasibility analysis
• Technical design document drafting
• Impact assessment across services, APIs, and dependencies
• Scope breakdown into Jira-ready stories with effort estimates
• Grounded code generation
• Accelerated onboarding for new engineers
• Production issue triage and root cause analysis
• AI-powered pull request reviews
• Cross-repo impact analysis in PRs
• No code storage or model training on customer code
• Flexible deployment (on-prem or Bito cloud)
• SOC 2 Type II certified
• End-to-end encrypted communication
• Usage-based pricing for AI Architect
• Per-seat plans for AI Code Reviews
• Integration with issue trackers (Jira, Linear)
• Integration with collaboration tools (Slack)
• Integration with coding agents (Cursor, Claude Code, Codex, GitHub Copilot)
• Integration with Git platforms (GitHub, GitLab, Bitbucket)
• Integration with IDEs (VS Code, JetBrains IDEs, Cursor, Windsurf)
• Custom analysis triggers and templates
• Custom configurations (workspace admin controls)
• SSO and SAML support
• Jira and Confluence graph indexing
• Google Docs graph indexing
• Observability data indexing
• Custom review guidelines for AI Code Reviews
• Auto-learn from feedback for AI Code Reviews
• CI/CD pipeline reviews
• 1-click apply for AI fixes in code reviews
• Chat with the Agent in PRs
• Static code analysis (Mypy, fbinfer)
• Linter support (ESLint, golangci-lint, Astral Ruff)
• Security analysis (Snyk, Whispers, detect-secrets)
• Incremental code reviews
• Smart reports for code review analytics
• Web research for external patterns and industry context
• Detailed agent specs for coding agents
• Model Context Protocol (MCP) compatibility

Use cases

• Autonomous development
• Cutting agentic coding costs
• Feasibility analysis before committing to builds
• Drafting technical design documents
• Assessing impact of changes across repositories
• Breaking down epics into Jira-ready stories
• Generating production-ready code grounded in system patterns
• Accelerating onboarding for new engineers
• Triage of production issues and root cause identification
• Accelerating pull request cycles
• Catching bugs and downstream risks in code reviews
• Managing complex, cross-service changes
• Service migrations across multiple services
• Building multi-service features from spec to tested code
• Codebase-wide refactoring
• Addressing system-wide tech debt
• Analyzing issues and epics for planning
• Answering system-level engineering questions in Slack
• Improving task success rate for coding agents
• Reducing token cost for coding agents
• Reducing planning overhead for large projects
• Ensuring code changes align with project specifications (Jira integration)
• Validating pull requests against documentation (Confluence integration)

AI approach

Bito's AI Architect builds a knowledge graph from an organization's code, commits, issues, and documentation, mapping services, APIs, dependencies, and architectural patterns. This context layer powers various engineering workflows, including technical design, grounded code generation, and code reviews. The company emphasizes that no code is stored or used for model training. They use Retrieval-Augmented Generation (RAG) to ground AI responses to the codebase.

Tech named: Generative AI, Retrieval-Augmented Generation, RAG, Model Context Protocol, MCP, AWS Bedrock, Azure AI, Mypy, fbinfer, ESLint, golangci-lint, Astral Ruff, Snyk, Whispers, detect-secrets

Industries served

• Software Development

What it says sets it apart

• Context layer for autonomous development, mapping services, dependencies, APIs, and operational history across all repositories.
• Builds a living knowledge graph from code, commits, issues, and docs, unlike traditional RAG that retrieves text chunks.
• No code stored and no model trained on customer code, ensuring data privacy and security.
• SOC 2 Type II certified and end-to-end encrypted.
• Flexible deployment options: on-prem or Bito cloud.
• Integrates deeply into existing engineering workflows (Jira, Linear, Slack, Git platforms, IDEs) without requiring new tools or workflow changes.
• Provides system-level context to coding agents via Model Context Protocol (MCP), significantly improving task success and reducing token costs.
• Offers comprehensive AI Code Reviews with cross-repo impact analysis, static/security analysis, and custom guidelines.
• Orchestra product enables autonomous agents for complex, cross-service changes, managing dependencies and ensuring team sign-off.
• Proven to reduce technical design time and accelerate PR cycles, as demonstrated in case studies with companies like Kredivo, PubMatic, and Privado.

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.