AI integrations that read your data and act on it
Custom AI agents, RAG pipelines, and semantic search built on your own data and your own infrastructure — engineered so the system says “I don't know” instead of making things up.
Anyone can wire a model to a prompt. The work that matters is everything around it: agents with typed tools and structured outputs, retrieval that's grounded in your real documents, and verification layers that make hallucination structurally difficult — like findings that must quote their source word-for-word, with the server checking the quote actually exists before anyone sees it.
The pattern is proven across my builds: Langley, a multi-agent crypto-risk system where deterministic code can override the LLM but only toward the safer answer; ChronosGuard, a temporal RAG auditor on pgvector that judges policies against the law as it stood on any date at ~$0.03 per audit; and a multi-channel support agent with 11 function tools that answers only from the knowledge base and escalates when it shouldn't answer at all.
I treat evaluation as part of the build, not an afterthought. Langley's honest held-out score started at F1 0.63 — which exposed a real bug a synthetic benchmark had hidden — and reached 0.94 with zero fatal errors after tuning on a training split. If a system's quality isn't measured, it isn't known.
What you get
- AI agent design and build — OpenAI Agents SDK, typed function tools, structured outputs
- RAG pipelines: ingestion, chunking, embedding, retrieval tuning, grounded prompting
- Vector search on pgvector or Qdrant — usually inside the database you already run
- Guardrails: schema validation, citation verification, deterministic safety gates, abstain-over-guess behavior
- Evaluation harnesses — judge LLMs, held-out test sets, hallucination counters
- Cost engineering: model-fit per task, caching, batching — with per-unit costs measured
How an engagement runs
Define the failure that matters
Every AI system has one unforgivable error — a false 'compliant', a wrong 'safe', an invented refund policy. We name it first; the architecture follows from it.
Ground truth before cleverness
A labeled, held-out evaluation set — even a small one — so quality is measured against reality, not vibes.
Build with fakes in CI
Deterministic fake providers make tests free and reliable; the real model runs in a separate eval lane.
Measure, calibrate, ship
Eval scores gate the release. After launch, the same harness keeps watching so drift gets caught by you, not your users.
Proof, not promises
- LangleyA multi-agent system that judges whether a Solana token is a scam and explains every call with cited evidence.
- ChronosGuardA compliance auditor that treats regulation as a time machine: it judges corporate policies against the law exactly as it stood on any chosen date, so historical audits never leak rules that weren't yet in force.
- AI Customer Support AgentMulti-channel AI support system across Gmail, WhatsApp, and Web.
Common questions
- How do you stop the AI from hallucinating?
- Layers, not hope: retrieval grounding so the model answers from real documents; structured outputs so claims must carry evidence; server-side verification — in ChronosGuard, every finding's quote is checked word-for-word against the source, and findings that fail are dropped and counted; and abstain-over-guess behavior, where thin data produces 'insufficient evidence' rather than a confident answer.
- Which model and vector database should we use?
- Whatever the task and the evals justify — not whatever is newest. Small models with good retrieval beat big models with bad retrieval, and a judge model should differ from the one being judged. For vectors I default to pgvector inside the Postgres you already run; a dedicated store like Qdrant earns its place at larger scale or for specialized filtering.
- Does our data stay private?
- The pipelines run on your infrastructure with your API keys; documents and embeddings live in your database. Multi-tenant systems get isolation enforced by the database itself — ChronosGuard uses Postgres Row-Level Security that fails closed, proven by a dedicated blocking test suite.
- What does an AI feature cost to run?
- It should be measured per unit, not guessed: ChronosGuard audits cost ~$0.03 each, tracked per run in the database. Cheap embeddings, the smallest model that passes evals, caching, and batching keep unit economics sane — and the eval harness is what lets you downgrade a model with confidence later.