Agentic AI built into your product.
Verified, auditable agents with real tool access and a full audit trail. They answer from cited, versioned facts, a verifier blocks anything ungrounded, and high-stakes calls route to a human.
This is something I build into your product, inside your codebase, owned by your team. Not a chatbot demo, and not a SaaS you rent.
Citation-grounded answers
Cited, versioned facts, no fine-tuning
Verifier hard-blocks
Ungrounded answers are vetoed, not penalized
Human gate
Anything uncertain escalates to a person
Replayable audit log
Replays any decision with zero model calls
Naive RAG answers confidently. A verified agent knows when to stop.
The same agent across three jobs. Flip between a naive bot and the verified agent on the same question, and watch what changes. This is a reference build, not a client system.
- 01INTAKEParse the request into structured parameters. Never guess. Ask when unclear.
- 02RETRIEVEPull cited, versioned facts from an inspectable claim store, not raw model memory.
- 03REASONCall real tools with system access. Assemble a structured, cited proposal.
- 04VERIFYA verifier checks every citation, blocks anything ungrounded, respects hard constraints.
- 05GATEAnswer, redirect, or escalate to a human when the stakes or uncertainty are high.
An agent that knows when it is unsure.
Most AI demos answer confidently, even when wrong. I build systems that ground every answer in cited facts, let a verifier and a human catch what the agent is unsure about, and leave a log you can replay.
Citation-grounded knowledge
Every answer points to a cited, versioned source your team can read and edit, searched by meaning. Nothing is baked into model weights.
Multilingual retrieval
An English question can match a German source. The right fact is found whatever language it was written in.
Real tool access
The agent calls your APIs and databases to take real actions, not paste suggestions into a chat window.
A verifier and a human decide
A verifier rechecks every citation and constraint, with the decisive checks recomputed in code. Hard limits are walls, not scores, and anything uncertain routes to a person.
Built into your product
Built inside your codebase, owned by your team. No bolt-on SaaS, and you can swap cloud models for local ones when data cannot leave your network.
Replayable audit log
Every step is appended to a log that reconstructs any decision with zero model calls. A diligence asset, not a debugging afterthought.
Agents running inside real products.
This is not a thought experiment. These are agentic systems I have built into products that people depend on.
AI studio agent
An agent built into the product that turns a brief into structured, generated output, with the architecture to keep its work grounded and reviewable.
Read the case studyTranscription and real-time voice
AI consultation transcription and a real-time voice and chat system, built for a medical platform across three phases.
Read the case studyAI invoice agent
An agent that reads invoices, extracts structured data, and proposes actions, with a verification step before anything that touches money.
What you get.
A focused build that puts one auditable agent into production inside your product, with your team able to run it after I leave.
- Agent design scoped to one high-value workflow in your product
- A cited, versioned claim store your team can read and edit
- Direct tool and API integration so the agent takes real actions
- A verifier with code-level checks, hard vetoes, and human approval for anything uncertain
- A correction loop: every human review creates a new version of the knowledge
- Durable workflows that pause for a human and resume, with a replayable audit log
- An eval harness so you can measure the wrong-answer rate, not guess it
- Built inside your codebase, owned by your team, with knowledge transfer
Put a verified agent into your product.
Get in touch to discuss whether agentic AI fits your product.