Examples of pain points I've fixed

These are not abstract demos. They are recurring failure patterns I have fixed inside real AI products: codebases moving faster than control, agentic systems with invisible risk, voice AI that breaks outside the demo, and RAG/search systems users cannot trust. They are examples, not the full list — the same approach applies wherever an AI product has to get reliable, secure, and trusted enough to ship.

AI codebases moving faster than control

AI coding tools and agents create speed, but they also create fragile code, weak tests, phantom references, unclear ownership, and build instability.

I audit the codebase, identify the launch blockers, repair the critical path, and put quality gates around the parts that matter.

An edit-compile-run-debug loop traces across a live code graph: a node changes, the build fans out, tests fire, a failure lights up, and the agent patches and re-runs — the inner loop of agentic coding, made legible.

Code harnesses, agentic coding workflows, AI-generated code control, codebase rescue, and release-readiness checks.

See how the control loop works

Agentic systems with invisible risk

Agentic AI risk hides across tools, memory, retrievers, APIs, files, permissions, prompts, and human approval boundaries.

I map what the system can read, write, call, trigger, or leak — then fix the control gaps before launch.

A code property graph assembles node by node — functions, variables, and calls — then a taint path lights up and threads from an untrusted source through the graph to a sensitive sink, the way a CPG exposes an attack path.

Code property graphs, trust-boundary mapping, control-surface analysis, agent permissions, and AI security review.

See how the risk map works

Voice AI that works in demo but not production

Voice AI breaks on latency, silence detection, streaming, transcription quality, turn-taking, inference cost, telephony, deployment, and real users.

I turn fragile voice prototypes into production-ready pipelines.

A live sales call, annotated in real time: the pipeline transcribes the client–salesperson conversation and categorizes each exchange against four different sales strategies as the call unfolds. The annotator also lets the user mark key parts of the call by hand — those annotations feed back in to train the AI.

Real-time speech, VAD, streaming, inference acceleration, call handling, deployment, and production-readiness.

See how voice becomes production-ready

RAG and search systems users cannot trust

Naive RAG gives plausible answers. Shippable RAG gives grounded, complete, cited, testable answers — and knows when to abstain.

I rescue document-intelligence systems that miss evidence, hallucinate specifics, fail exhaustive queries, or cannot prove where answers came from.

Standard · single model
Advanced · open-weights SOTA frontier
One query forks two ways: a vector path drifts through an embedding cloud to nearest neighbors while a structured-filter path snaps a metadata grid down to qualifying rows; the two streams converge and rerank into a single ordered result.

Hybrid retrieval, structured filtering, citation grounding, exhaustive search, evaluation, and trustworthy document intelligence.

See how trustworthy retrieval works