Dataflow Context Engine · CI Integrations

Keep AI Context Fresh With Every Build

AI coding agents are only as useful as the context they can access. In large codebases, that context changes every time services, APIs, fields, consumers, or dependencies change. HoundDog.ai integrates directly into your CI workflows to continuously regenerate this context from source code.

HoundDog.ai CI integrations: GitHub, GitLab, Bitbucket, Jenkins, CircleCI, Azure Pipelines, with an example YAML job configuration that runs the scan on every push
What CI Regenerates

One deterministic chain, recomputed as your code changes

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Our fast, deterministic analysis engine runs on standard CPU infrastructure with minimal impact on CI latency. Instead of asking AI agents to repeatedly search repositories and reconstruct the same relationships, HoundDog.ai computes those relationships once and refreshes them as your code changes.

The resulting evidence can power your service catalog, API and gRPC and Thrift discovery, microservices documentation, change blast radius analysis, and organization wide context for AI coding agents.

Through MCP, authorized agents such as Claude Code can query this continuously updated context whenever they need it.

Division of Labor

Static analysis discovers. AI reasons. MCP delivers.

Use AI where reasoning adds value. Let CI handle the deterministic discovery.

A deterministic context layer for AI agents: source code feeds deterministic Rust static analysis, which produces a continuously updated dataflow graph of services, APIs, callsites, fields and dataflows; the MCP server then delivers structured traces to AI agents for reasoning, with no source code access.
Deterministic discovery runs in CI. The MCP server delivers structured traces, not raw source code. AI agents reason over facts they no longer have to rediscover.
Automated CI Configurations

Roll Out CI Scans Across Thousands of Repos in Minutes, Not Months

For enterprises with thousands of repositories that prefer running scans in their own CI workflows, configuring CI across every codebase by hand can take months of engineering effort. With HoundDog.ai, it becomes a five-minute task.

Automated CI configuration UI for a HoundDog.ai connected repository: pull request scans, automatic pull request comments, periodic scans, build blocking with a severity threshold, and CI runner selection

Push Tailored CI Configurations Directly From the HoundDog.ai Cloud Platform

The HoundDog.ai Cloud Platform integrates directly with your source management platform, including GitHub, GitLab, and Bitbucket in both cloud and enterprise editions, and automatically pushes CI configurations as direct commits or pull requests requiring approval, tailored to your needs.

Each configuration supports deep customization of scan frequency, pull request blocking logic, developer-facing feedback through actionable comments in pull requests, and the option to use cloud-hosted or self-hosted runners.

Platform and engineering teams roll out continuous context refreshes organization-wide without asking each team to write and maintain their own YAML. Learn more about SCM integrations.

What Ships to Your Agents

What the Dataflow Context Engine Delivers

A deterministic, always-fresh view of every API, field, and downstream consumer across your repositories, built for AI coding agents and engineering teams working in large monorepos and microservice estates.

Service Catalog & API Context

An always-fresh catalog of every API, field, and downstream consumer across your repos, traced from source code. Learn more

AI Coding Agent Context

Real-time codebase context for coding agents, cutting token costs on large repos while improving change safety. Learn more

Cross-Repo Dataflow

Trace dataflows across monorepos and microservice estates without asking each team to instrument or annotate their code. See how it works

Let CI Do the Discovery

Compute the facts once, refresh them on every build, and let every authorized AI agent reason over context that is already current.