AI features that earn their keep —
not demo-ware.
Chatbots that actually answer. Search that understands intent. Automation that saves headcount. Built with the Vercel AI SDK and Claude / OpenAI APIs, grounded in your real data — and measured by business metrics, not vibes.
AI with a job description.
RAG chat & support assistants
Customer-facing chat grounded in your docs, catalogue and policies — answers with sources, hands off to humans gracefully, never invents a refund policy.
Intelligent search & recommendations
Search that understands “something for a beach wedding under 500” — semantic retrieval over your real inventory, tuned for conversion.
Workflow automation
Drafting, summarising, enriching, routing — the repetitive work your team does in tabs all day, automated with human review where it matters.
AI inside your product
Streaming UI, agents, tool calls, evals — production AI features built into your app with the Vercel AI SDK, not bolted on via iframe.
Why teams pick me for AI work.
Production engineering
Anyone can demo a chatbot. Fallbacks, evals, rate limits, cost ceilings, observability — the unglamorous 80% that makes AI dependable is the job.
Privacy by design
Proper data boundaries, provider agreements, redaction where needed. Your customer data never becomes someone else's training set.
Metrics or it didn’t happen
Every AI feature ships with a success metric: deflection rate, conversion lift, hours saved. We measure, iterate — or kill it honestly.
Receipts, not promises.
WhatsApp campaign automation
Led an automated campaign tool that lifted engagement and retention for restaurant ordering — production AI before it was fashionable.
AI features for agency clients
Generative AI features shipped inside client builds with the Vercel AI SDK — content generation, intelligent search, assistants.
Commerce + AI stacks
The same production stacks these brands run on are AI-ready by architecture — streaming, edge, structured content.
19 years of integration work
AI is another system to integrate well: APIs, queues, failure modes. That has been the job since 2006.
Asked & answered.
Which models and providers do you use?
Claude and OpenAI as defaults, open-source models where data residency or cost demands it. The architecture is provider-agnostic — you can swap models without rebuilding.
What about hallucinations?
Grounding (RAG) plus evals plus honest UX. The assistant cites sources, declines outside its scope, and hands off to humans. We test against a curated question set before launch — and monitor after.
Is our data safe?
Yes — data boundaries are designed first: what leaves your systems, what gets redacted, which provider agreements apply. Nothing of yours trains anyone's model.
What does a pilot look like?
Two to six weeks: one use case, real data, a success metric agreed up front. You get a working feature in production with usage data — then we decide whether to expand. No moonshot contracts.