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See the HoundDog.ai Dataflow Context Engine in action

A code-first service catalog that makes AI coding agents 6x cheaper and 7x faster.

6xcheaper in tokens
7xfaster in wall-clock time
200Kline Go monorepo

Watch Claude Code complete the same task 6x cheaper and 7x faster.

AI coding agents waste enormous time rediscovering APIs on every prompt in large monorepos and complex microservices architectures: grepping the codebase, generating bash scripts, and piecing together which services exist and how they connect.

The HoundDog.ai Dataflow Context Engine solves this. Our free, lightweight scanner is built in Rust, runs entirely on your machine in seconds, and pre-indexes your APIs, services, consumers, and fields into an always-fresh code-first service catalog and API graph that your agent can query directly through MCP, CLI, or Skills. Nothing leaves your environment.

In the demo above, we run the same prompt against Claude Code twice: generate API documentation in a 200,000-line Go monorepo with a web of interconnected gRPC services. The first run is Claude Code on its own. The second connects Claude Code to the HoundDog.ai Dataflow Context Engine. The result: the same task completes 6x cheaper in tokens and 7x faster in wall-clock time, with more accurate output and no hallucinated field types. For any prompt involving API documentation or service updates, expect at least 5x less cost and time compared to letting the agent rediscover everything itself.

The roadmap extends beyond APIs into broader code indexing, with the local scanner free to download and use. An enterprise platform is in development for larger teams, centralizing context across the organization with direct source code integrations that run indexing through CI, so nothing scans on developer machines. Your agent gets the context when it needs it.

Claude Code Cursor GitHub Copilot Codex JetBrains AI Windsurf OpenCode + Any MCP-compatible agent
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FAQ

Frequently asked questions

What API protocols are supported?+
gRPC is supported in the first release (June 2026), with REST, GraphQL, and Apache Thrift coming soon. The roadmap extends to additional API protocols based on customer demand.
Which AI coding agents does the HoundDog.ai Dataflow Context Engine work with?+
Any AI coding agent that supports the Model Context Protocol (MCP), including Claude Code, Cursor, GitHub Copilot, Codex, JetBrains AI, Windsurf, and OpenCode. The scanner can also be accessed directly through CLI and Skills, so it works with any agent or workflow that can invoke a local command.
Is the local version really free?+
Yes. The local scanner is free to download and use. It runs entirely on your machine, scans whatever code is available locally, and integrates with your AI coding agent through MCP, CLI, or Skills. No account, no time limit, no usage cap.
Do you offer an enterprise version?+
Yes. The local version is designed for individual developers and scans whatever code is on a single machine. The enterprise platform, currently in development, solves this at organizational scale. It connects directly to your source code management platforms (GitHub, GitLab, Bitbucket), automatically pushes CI configuration to your selected repositories (even across thousands of them), runs scans entirely within your CI environment, and centralizes context across every repo, including cross-repo dependencies between microservices. The result: a developer using the HoundDog.ai MCP server gets the right context at the right time for the repos they are working on and the repos that depend on them, without having to clone everything locally or run scans on their own machine.
How does the HoundDog.ai Dataflow Context Engine handle data privacy and security?+
The local scanner runs entirely on your machine. No code, no API definitions, and no metadata leaves your environment. The enterprise platform deploys within your own infrastructure or VPC and integrates with your existing CI environment, so source code never transits through HoundDog.ai infrastructure. The underlying engine is the same minimal-dependency Rust scanner used by Replit to detect privacy risks for 45 million creators, and HoundDog.ai is SOC 2 compliant.
How is this different from a developer service catalog like Backstage?+
Traditional developer service catalogs rely on YAML files, manual updates, and human-curated metadata that goes stale the moment someone ships a new consumer. The HoundDog.ai Dataflow Context Engine is a code-first service catalog: it is generated automatically from your protobuf files and service code, kept continuously in sync, and built from day one to feed AI coding agents through MCP. There is nothing for engineers to maintain by hand.
How fast is the scanner?+
The Rust engine analyzes large codebases in seconds. The 200,000-line Go monorepo shown in the demo above scans in under 30 seconds.