AI Enablement
Your team talks about AI.
Competitors ship it.
I integrate LLMs, RAG, and agents into your real product — with evals, cost caps, and observability. Production systems, not a ChatGPT wrapper that breaks in week two.
Sound familiar?
Where AI projects stall
The PoC works. Production doesn't.
A demo that wowed the team falls over under real traffic, real data, and real edge cases. Nobody knows how to harden it.
No evals — you're flying blind
You changed the prompt and "it feels better." No regression suite, no quality metric, no way to prove the model got worse after a change.
Token spend is exploding
No caching, no model routing, no cost caps. The bill scales linearly with usage and nobody budgeted for it.
Hallucinations and data leakage risk
The model invents facts, or worse — leaks data it should never see. No guardrails, no retrieval grounding, no PII handling.
How I work
From idea to production AI
1. Scan — free 30-min call
Tell me the use case. I tell you if AI is the right tool, the realistic approach (RAG vs fine-tune vs prompt), rough cost, and the main risks. Honest answer even if that answer is "you don't need AI for this."
2. Design & prototype
Architecture for the AI feature: retrieval, model choice, prompt strategy, eval harness, cost controls, fallbacks. A working prototype wired into your stack — not a notebook.
3. Harden for production
Evals in CI, cost caps and model routing, observability (token/latency/quality), PII handling, graceful degradation. Ship it behind a flag, measure, roll out.
What I build
The boring parts that make AI actually work
Chunking, embeddings, vector store, retrieval tuning. Grounded answers, fewer hallucinations.
Function calling, multi-step workflows, guardrails. Agents that do work, not just chat.
Golden datasets, automated scoring, regression gates in CI. Prove quality, catch drift.
Prompt caching, model routing (cheap model first), token budgets, usage caps per tenant.
Trace every call: token spend, latency, quality score. Know what your AI is doing in prod.
PII redaction, prompt-injection defence, data residency, no-train guarantees. Safe with real data.
Engagement options
Three ways to work together
Free
30-min scan
$0
- ✓ Is AI right for this?
- ✓ Realistic approach & cost
- ✓ Main risks named
Fixed scope
Prototype sprint
2-4 weeks
- ✓ Working prototype in your stack
- ✓ Architecture & model choice
- ✓ Eval harness + cost controls
- ✓ Production-readiness plan
Hands-on
Build to production
From 6 weeks
long-term if needed
- ✓ Ship the feature with your team
- ✓ Evals in CI, observability
- ✓ Cost & security hardening
- ✓ Knowledge transfer
Common questions
Which models / providers do you work with?
Provider-agnostic — Anthropic Claude, OpenAI, open models (Llama, Mistral) via your own infra. I pick based on cost, latency, data residency, and quality for your use case, not hype.
Will my data be used to train models?
Not unless you choose it. I default to no-train API tiers and can keep everything inside your VPC with self-hosted or Bedrock/Azure OpenAI deployments where data residency matters.
We don't have an AI/ML team. Is that a problem?
No — that is the common case. Modern AI integration is software engineering, not ML research. I build it and transfer knowledge so your existing engineers can maintain it.
How do you stop hallucinations?
Retrieval grounding (RAG), structured output validation, eval gates, and honest UX (cite sources, show confidence, allow "I don't know"). You cannot eliminate them — you engineer around them and measure.
What does this cost to run?
Depends on volume and model. Part of the work is making it cheap: caching, routing to smaller models, token budgets. I give you a cost-per-request estimate before you commit to a rollout.
Ship AI that survives production
30 minutes, your use case, an honest answer on whether AI fits and how I'd build it.