Product services

AI product development Useful intelligence, engineered responsibly.

Design and build AI-enabled products where models, workflows, evaluation, and human oversight operate as one system.

Clean build · Fast delivery · Scalable foundation · Less burn

Built for

Teams with a real workflow to improve—not a vague requirement to add AI.

Intended outcome

A production AI experience with measurable quality, cost controls, fallback behavior, and a clear operating model.

What matters

Ship the useful part. Kill the rest.

01

Use-case and model feasibility

02

Evaluation before interface polish

03

Human review where risk requires it

What you get

Working outputs. No strategy confetti.

AI opportunity map
Data and privacy assessment
Prototype with representative inputs
Evaluation dataset and scorecard
Production workflow
Monitoring and cost dashboard
How it works
01

Define what good output means

02

Test models against real examples

03

Design the human and AI handoff

04

Monitor quality, latency, and cost in production

Two ways to work

Pay for the build.
Or bet with us.

Most founders bring a monthly budget and hire us to deliver. A few bring a vision strong enough for us to join the bet. Both models stay lean, direct, and accountable.

MODEL 01

Build + maintain

One monthly budget.
We ship and maintain.

You set the cash ceiling. We cut the scope to fit, ship working software every week, launch it, and keep it healthy. No hourly mystery. No hostage code.

  • Predictable monthly spend
  • Weekly working releases
  • Launch ownership
  • Ongoing maintenance
Get a build plan
MODEL 02

Virtual CTO partnership

Small retainer.
Shared equity. Long game.

For a small number of serious, long-horizon products, we join as the technical partner: roadmap, architecture, hiring, delivery, and scale. Lower cash. Real equity. Shared upside.

  • Virtual CTO ownership
  • Lean monthly cash
  • Aligned equity stake
  • Selective partnerships only
Pitch the vision
Straight answers

What you should know before spending money.

Do we need to train our own model?

Usually not at the start. Many products get better economics and faster validation from carefully selected hosted models, retrieval, structured context, and strong evaluation.

How do you prevent hallucinations?

The strategy depends on risk: constrained outputs, retrieval, validation rules, citations, confidence thresholds, and human review can all be part of the system.

Can AI features be added to an existing product?

Yes. We first isolate the workflow and data boundaries so the new capability can be tested without destabilizing the core product.

Enough research

The next useful artifact is working software.

Bring the workflow, idea, or delivery mess. We’ll cut it to the leanest credible build and tell you which partnership model fits.