AI product engineering
The AI feature itself — built to be reliable from day one, with the evaluation seams already in place so it can be proven later.
#What I build
Conversational assistants / chatbots
RAG over your docs, streaming UI, source citations, and a scope guard so it stays on-topic.
AI voice agents
Inbound + outbound voice that qualifies, books, and follows up — with a hard consent gate on outbound.
Document intake & extraction
Invoices, forms, and PDFs turned into validated structured data, with a review lane for low-confidence cases.
Internal copilots
A private assistant wired into your wiki, code, and tickets — access-scoped and auditable.
RAG pipeline engineering
Chunking, embeddings, hybrid retrieval, reranking, and grounding — with retrieval-quality evals in front.
Multi-agent orchestration
LangGraph-style flows with explicit state, tool boundaries, retries, and approval checkpoints.
#How I build it
- Structured outputs and function-calling are schema-validated, with fallbacks on parse failure and raw inputs logged beside every decision.
- Grounding is enforced by construction where it matters — e.g. no uncited generation path — not asked for in a prompt and hoped for.
- The evaluation harness is designed in from the start, so the feature is provable, not just shippable.
Concretely, an LLM step is a typed, validated function — not a free-text call you hope parses:
const Ticket = z.object({
category: z.enum(["billing","bug","howto","other"]),
urgency: z.enum(["low","med","high"]),
needs_human: z.boolean(),
});
const out = Ticket.safeParse(await llm(prompt, { schema: Ticket }));
if (!out.success) return { category: "other", needs_human: true }; // safe fallback
log({ input, decision: out.data }); // every decision is auditable