AI Product Build

Ship the AI feature without hiring a team

Why this matters

Most businesses have the same three things sitting on a list right now. An AI feature or workflow people keep asking for. A support and qualification load growing faster than headcount. Nobody is free to work on either.

So it slips. Or someone wires up an API call over a weekend, ships it, and it works fine in the demo and badly in production. LLM features fail in ways normal software doesn't. Nothing throws an error. The status code is 200. The answer is just wrong, and nobody notices until a customer screenshots it or a lead gets qualified into the wrong bucket.

The teams shipping AI well have the boring parts in place. Retrieval that returns the right context. Evals that catch regressions before release. Traces they can read when something behaves strangely. Most companies skip all of it because none of it looks like a feature, then spend six months firefighting.

There's a second thing worth knowing. Your customers increasingly work inside AI assistants. If your product, catalog, or booking flow can't be reached from there, you sit outside the workflow. An MCP server is a small piece of work that puts you back in it, and hardly anyone has built one yet.

What gets built

• MCP servers, so your product or data works with Claude, ChatGPT, and whatever comes next.
• RAG and in-app assistants over your docs, your catalog, your CRM, or your customers' data.
• Natural language search across products, inventory, or account data.
• Lead qualification and intake agents that ask the right questions, score properly, and hand off to sales with context attached.
• Support deflection. An agent that handles tier one, escalates cleanly, and admits when it doesn't know.
• Onboarding agents that shorten time-to-value and show you where people get stuck.
• Evals and observability with Langfuse, Braintrust, or LangSmith depending on your stack. Datasets, scorers, regression gates in CI.
• Guardrails, model routing, and cost control, so your margin doesn't quietly move to your model provider.

How it works

Scoping conversation first. You get a straight read on whether the thing you're describing is a two-week build or a six-month one, before anyone commits.

Then a working prototype, early, evaluated against real examples you supply. Real tickets, real leads, real product questions. Then production hardening. Evals, tracing, cost controls, failure handling.

Then handover with documentation, or ongoing operation and tuning. AI features need attention that normal features don't, and it's better to say that now than six weeks in.

Who this is for

SaaS companies from seed to mid-stage. Lead gen businesses drowning in unqualified inbound. Ecommerce brands with a catalog nobody can search properly and a support inbox that never empties. Any company sitting on interesting data with no good interface to it.

The common thread is a technical person who understands the work and has no hands-free.

Worth being straight about

Not every business needs an AI feature. Some of what gets shipped is a worse version of a button or a form. If a scoping call turns that up, you'll hear it. Saying so costs both of us far less than building it.

If you take one thing from this page and do it yourself, set up evals before you ship. A feature with evals gets better over time. A feature without them degrades quietly while everyone assumes it's fine.

Tell me what you're trying to ship. You'll get an honest read on scope, approach, and whether it's worth building.