HoundDog.ai is the dataflow context engine for engineering teams shipping AI-assisted code at scale and for privacy teams that need code-level evidence in their RoPA, PIA, and DPIA workflows. We do one thing exceptionally well: trace how sensitive data and APIs flow through your code, fast, cheaply, and deterministically, so the slowest, most expensive part of working with large codebases stops being the bottleneck.
HoundDog.ai's mission is to provide the dataflow context that AI coding agents need to perform their best on large codebases, codebases beyond 200,000 lines of code, and to empower privacy and compliance teams to prevent risks early in development rather than document them after the fact.
Engineering teams already use AI agents to generate code, review it, find vulnerabilities, and, when compliance requires it, draft data flow maps. For small codebases under 100k lines of code, that approach holds up. At larger scale, cost, speed, and the need for deterministic evidence become the bottlenecks that define whether AI assistance keeps delivering value or starts breaking down.
Replit's flagship deployment of HoundDog.ai showed that grounding an AI agent's contextual analysis with deterministic results from a static scanner produces 90% better outcomes than relying on AI agents alone.
Read the full case studyAI agents now write code that easily passes 200k lines. Vibe coders and solopreneurs ship features at a pace that used to take whole teams. The bottleneck has shifted: it is no longer how fast you can produce code, but how reliably an AI agent can reason about code that already exists.
Whether the code is fully AI-generated for a greenfield project or strategically used for incremental enhancements, the core problem stays the same. Every prompt to update a service or API forces the agent to rediscover what the code already knows:
On a small codebase this is cheap. At scale it becomes a real drain:
Using AI agents to generate data maps for RoPA documentation, or to validate PIA and DPIA reviews in development, creates three structural problems:
That last one matters most. RoPA and PIA evidence has to stand up in front of regulators and auditors. Output that changes from run to run puts a question mark on every claim.
Anthropic's own guidance for rolling out coding agents on large codebases says it directly: teams need to "invest in codebase setup to see better results." The recommended setup uses MCP servers and skills that provide the right context to agents, not to maximize how much the agent does on its own, but to maximize the safety and accuracy of the code generated while controlling the cost and time the agent burns through.
That is exactly the gap HoundDog.ai fills. A deterministic dataflow context engine that runs on every prompt, on every PR, on every commit, gives the AI agent the cross-repo, cross-service, field-level context it cannot reliably reconstruct on its own. The agent focuses on what it does well. The static scanner handles what it does not.
Amjad is a serial entrepreneur with a rich background in cybersecurity. He led his first company, DCHQ, a cloud management startup, to acquisition, and later founded APISec.ai, which developed one of the first API security scanners. Before founding HoundDog.ai, Amjad served as VP of Product at Cyral, a data security platform that implements security controls on production data. His experience at Cyral, coupled with significant feedback from security and privacy teams frustrated by the reactive approach to data security and privacy that remains unaligned with evolving codebases, inspired him to start HoundDog.ai.
Joohwan is an experienced engineering leader skilled in both scaling services for millions of users and developing new software from scratch. Before joining HoundDog.ai, he was a founding engineer at Aktos, a FinTech startup focused on modernizing the accounts receivable management industry. Joohwan has also led key projects at Meta, Amazon, and Instacart. Today he oversees the development of HoundDog.ai's cloud platform and the AI workflows that significantly enhance the scanner's accuracy and coverage.
Talk to us about your codebase, your CI pipeline, and your compliance program. We will show you, on your own repos, what changes when dataflow analysis stops being an afterthought.