AI development built into the product, not bolted on

Too often, "AI development" means one thing: a third-party chat widget dropped into the corner of your site, an invoice for "AI integration", and a handover email. Six months later the widget is answering questions about your refund policy incorrectly, nobody can tell you what it costs to run per month, and the workflow that actually deserved automating — the intake triage, the quote drafting, the report that eats someone's Friday — is still done by hand.

We treat AI as an engineering discipline, not an add-on. The same standards that apply to our webapps — typed code, measured performance, boring reliability — apply to every AI system we ship. And we run our own: the AI chatbot answering visitors on redenn.com and the internal AI audit generator our team uses for outreach research were both designed, built, evaluated, and deployed in-house, and both run in production today. We don't resell someone's widget. We build the system.

What "AI development" means in our hands

It starts with the workflow, not the model. The first question is never "which model?" — it's "which task, done by whom, how often, and what does a wrong answer cost?" Some problems want a frontier model; plenty want a small, cheap one plus good retrieval; a few shouldn't use AI at all. We tell you which is which before anything gets built.

Evals before launch, monitoring after. Every system we ship is tested against an evaluation set built from your real data — real customer questions, real documents, real edge cases — with pass/fail criteria you agree to before go-live. After launch, every request is logged, costed, and reviewable, so "is it working?" is a dashboard, not a feeling.

Built into the product, not beside it. AI features live inside your existing app, portal, or admin — same auth, same database, same design system — not in a bolted-on iframe. That's the difference between a feature your customers use and a gadget they close.

The stack and the approach

• Application layer: Next.js 16 + TypeScript + the Vercel AI SDK, streaming responses by default
• Models: Claude and GPT families via API; open-weight models where data residency or unit economics demand it — we are deliberately model-agnostic
• Retrieval: Postgres + pgvector, so your knowledge base lives in the same database as your app, not in a separate SaaS you rent forever
• Evaluation: task-specific eval harnesses that run before any prompt or model change ships
• Observability: full request logging with token-level cost accounting, surfaced in your admin dashboard

Everything deploys the way our custom-coded websites do: Vercel, version-controlled, with instant rollbacks.

What this looks like in practice

Two typical engagement shapes:

An AI feature inside an existing product. A service business adds an assistant to its client portal or website: answering questions from its own documentation, drafting replies, summarising records, qualifying leads before they reach a human. It is built on top of an existing site or webapp — ours or someone else's.

A back-office automation. A manual process — intake triage, document extraction, quote or report drafting, data entry between systems — rebuilt as an AI pipeline with a human approval step, so your team reviews and sends instead of researching and writing. The timeline depends on how many systems the pipeline touches.

If the AI system needs a webapp around it, that's our home turf — see /services/webapps. Ongoing tuning, eval refreshes, and monitoring after launch can fold into a Care Plan.

Pricing

AI work is scoped to the project. The spread between "add one well-evaluated feature" and "automate a department's workflow" is too wide for a rate card to be honest, so every engagement gets a fixed-price proposal after a scoping call. Contact us for a quote.