Build workflow

AI Product Build: From Idea to Production

Most AI products die between the demo and production: the demo works on five happy-path examples, then real inputs, costs, and edge cases arrive. Our build process front-loads the risky part (the AI behavior), wraps it in boring reliable software, and ships with measurement built in, so what launches is what was tested.

Typical timeline

3-8 weeks: one week to de-risk the AI core, the rest is shell, guardrails, and rollout

Stack

Claude (API) or the model that fits the workload · Next.js + Vercel · Supabase (Postgres, storage, auth) · n8n for surrounding automation · PostHog for product analytics and evals-in-production

What we need to start

  • · The job the product must do, and 10-20 real examples of inputs it will face
  • · Where it plugs in: your data sources, auth, and existing tools
  • · A definition of unacceptable output (compliance, tone, safety)

How it works

  1. 01

    Riskiest-assumption prototype

    Week one is spent only on the AI core against your real examples: can the model actually do the job at acceptable quality and cost? If not, we redesign the task or stop before you spend on the shell.

  2. 02

    Eval harness

    The examples become an automated eval set. Every prompt or model change runs against it, so quality is a number, not an opinion.

  3. 03

    Production shell

    Auth, data, queues, rate limits, retries, cost caps, and observability: the unglamorous 70% that makes the AI part dependable.

    Tools: Next.js, Supabase, Vercel

  4. 04

    Guardrails

    Input validation, output checks against your unacceptable-output list, human-review paths for low-confidence cases, and kill switches.

  5. 05

    Launch with measurement

    Ship behind a flag, watch real usage and failure cases in PostHog, tighten prompts against the eval set, then widen the rollout.

    Tools: PostHog

You get
  • ✓ The working product or feature, deployed on your infrastructure
  • ✓ An eval set + harness your team can extend
  • ✓ Cost model and usage dashboards
  • ✓ Runbook: failure modes, guardrails, and how to iterate safely
When NOT to use this
  • · The task has no tolerance for error and no human-review path; automation is the wrong shape
  • · You cannot provide real example inputs; we would be building against guesses
  • · A rules-based system solves it; AI would add cost and variance for nothing

Frequently asked

Why prototype the AI part before the product shell?

Because the AI behavior is the only genuinely uncertain part. If the model cannot do the job on real inputs at acceptable cost, no amount of UI saves the project, and it is far cheaper to learn that in week one.

Which model do you build on?

Whichever fits the workload and budget: we prototype against the eval set on more than one model and show you the quality-cost tradeoff before committing. Free-tier and open-weight models are on the table where they pass evals.

What happens when the model gets something wrong in production?

Wrongness is planned for: output checks catch rule violations, low-confidence cases route to a human queue, and every failure feeds the eval set so the same mistake gets harder to repeat.

Want this running in your business?

We build and run this workflow for clients.

Related services: SaaS MVP development · AI agent development · RAG pipeline development

Free weekly brief

Steal this workflow

Get new teardowns like this one by email: the steps, the tools, and the honest failure modes. No spam, unsubscribe anytime.