SERVICE · AI PRODUCT DEVELOPMENT

AI product development for founders who need it to work on day one

We design and build AI-native products and AI features for existing ones: LLM workflows, retrieval, structured generation and the guardrails around them. We run two AI products of our own in production, so we build yours with the same discipline.

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WHY THIS EXISTS

The part most teams underestimate.

Most AI features fail in the same few ways. The model returns something the UI can't render. A prompt that worked in a demo produces bland or wrong output on real data. Costs scale faster than revenue because nothing is metered. Users stop trusting the feature after one bad answer, and the team has no way to tell which answers were bad.

None of those are model problems. They are product engineering problems, and they're the part we specialize in. We treat model output as untrusted input: schema-validated, scored, retried or rejected before a user sees it. We pick the cheapest model that does the job, meter usage in the database, and design the screen around what the model is actually good at.

WHO IT'S FOR

Built for teams like yours.

  1. 01

    Founders turning a workflow into an AI product

    You know a specific, painful workflow in an industry, like proposals, content or compliance, and want an AI-native product built around it, not a chatbot bolted onto a form.

  2. 02

    SaaS teams adding a first AI feature

    You have a product and users, and you want an AI capability that fits your existing stack, data model and permission system without a rewrite.

  3. 03

    Teams whose AI prototype stalled

    You have a demo that impresses in a meeting but breaks on real inputs. You need it hardened: validation, fallbacks, evaluation and cost control.

PROBLEMS WE SOLVE

What goes wrong, and how we handle it.

  1. 01

    Unreliable output

    We use provider-native structured output (JSON schemas) and validate every response with Zod or Pydantic before it touches your database. A malformed response is retried or rejected, never rendered.

  2. 02

    Generic, obviously-AI copy

    Domain-specific system prompts, few-shot examples drawn from your real data, and automated quality checks such as length, banned phrases and required clauses, so output reads like your best employee wrote it.

  3. 03

    Runaway cost

    Per-plan quotas reserved atomically in Postgres before any model call and released on failure, with model choice based on measured cost per task rather than leaderboard rank.

  4. 04

    Answers without grounding

    Retrieval over your own documents with pgvector or ChromaDB, with sources passed through to the UI so users can check where an answer came from.

DELIVERABLES

What you get.

  1. 01

    Scoped AI spec

    What the model does, what it never does, the output schema, the evaluation criteria, and a written out-of-scope list.

  2. 02

    Production feature

    The AI workflow wired into your app: prompts, structured output, validation, retries and model fallback.

  3. 03

    Guardrails and evaluation

    Automated checks on every output, plus a small evaluation set you can rerun when you change prompts or models.

  4. 04

    Metering and limits

    Usage tracking and plan limits enforced server-side, so pricing and AI cost stay connected.

  5. 05

    Observability

    Structured logs for every model call with latency, tokens, cost and failure reason, so you can debug from evidence.

  6. 06

    Handover documentation

    How the prompts are organized, how to change models, and what to watch in production.

TIMELINE

A realistic schedule.

  1. 01

    ScopeWeek 1

    Map the workflow, define the output schema, collect real sample inputs, and agree the out-of-scope list in writing.

  2. 02

    Prototype on real dataWeeks 1–2

    Prompt and schema iteration against your actual inputs, not toy examples. You see outputs and quality scores side by side.

  3. 03

    BuildWeeks 2–5

    The production feature, guardrails, metering and UI, deployed to a preview URL you can use throughout.

  4. 04

    Launch and tuneWeeks 5–6

    Production release, monitoring, and a tuning pass on the first real traffic.

Engagement shape

  • Fixed-scope project priced from the written spec, so the price is agreed before the build starts.
  • We can work inside your repository and conventions, or build a standalone product we hand over in full.
  • You own all code, prompts and accounts from day one.

Stack

  • Gemini
  • Claude
  • OpenAI
  • Next.js
  • FastAPI
  • Supabase
  • pgvector
  • Zod
  • Pydantic
  • Vercel
  • GCP
PROOF

Where we've done this before.

  • Trade-aware AI, validated structured output and a branded PDF pipeline. A complete AI product for contractors.

  • Brand extraction from a URL, validated 30-day calendars, quality guardrails and AI usage metering.

QUESTIONS

AI Product Development FAQs.

  • Usually the cheapest one that passes your evaluation set. For templated, structured generation that's often a fast tier like Gemini Flash. For complex reasoning over long context it may be Claude or a larger Gemini model. We test on your real inputs, compare cost per task, and design the code so you can switch providers without a rewrite.

  • Yes. We work in TypeScript, Python and Ruby codebases and can integrate into an existing Next.js, NestJS, Express, FastAPI or Rails app. We follow your conventions and data model rather than introducing a parallel stack.

  • By constraining what it can produce and checking what it does produce. Structured output limits the shape, retrieval grounds the content in your data, validators reject anything outside the schema, and for high-stakes outputs we design a human review step into the flow.

  • Model choice based on measured cost per task, prompt and context trimming, caching where inputs repeat, and per-plan quotas enforced in the database before each call. Usage is logged per request so you can see cost by feature and by customer.

  • Yes. Everything we write, including prompts, schemas and evaluation sets, lives in your repository and accounts from the start.

OTHER SERVICES

Often paired with.

  • AI Agent Development

    LangGraph agents with tool calling, human-in-the-loop handoff, monitoring and fallbacks, built to run unattended in front of customers.

  • SaaS MVP Development

    2–6 week Next.js + Supabase + Stripe MVPs with auth, billing, the core workflow and launch-ready SEO.

  • Web Development

    Next.js marketing sites and web apps with fast Core Web Vitals and technical SEO built in, not bolted on.

Talk to us about ai product development.

Share the problem, the people who have it, and where you are today. We'll reply with questions and an honest read on scope and fit.

SSttaarrtt  aa  pprroojjeecctt