AI PRODUCT ENGINEERING

Most teams add AI to a product. We build products around what AI makes possible.

We design and build AI-native product features — recommendation engines, intelligent search, LLM-powered workflows — architected for production, not just for demos.

Talk to our team

Where are you right now?

If you're here

Start with

You'll get

You want AI in your product but don't know where it adds real value

AI opportunity audit

A prioritized map of where AI changes the product — and where it doesn't

You have a proof-of-concept that works in demo but not in production

Production readiness assessment

An honest diagnosis of the gap + a plan to close it

You're building a new product and want AI as a core feature from day one

Architecture design + build

An AI-native product designed to work before you have years of user data

You have user data and want to make your product smarter over time

ML feature development

Recommendation, personalization, or prediction features built into the product loop

Is this the right moment?

You're building a product where personalization, prediction, or intelligent search is a core part of the value proposition — not a nice-to-have.

Your team has tried to add AI features before and hit a wall when it came to making them work reliably in production.

You have data — or a clear path to collecting it — and you need an engineering team that knows how to turn it into product behavior.

You're starting from scratch and want to avoid the mistakes that come from bolting AI onto an architecture that wasn't designed for it.

Your current AI features are static or rule-based, and you need them to actually learn and improve over time.

You're under pressure to ship AI features, and you need a team that can move fast without cutting corners that will cost you later.

If you answered yes to any of these — let's talk.

How we work

Integrating AI into a product isn't a sprint. It's an architecture decision. The teams that get it wrong spend six months building something that works in a demo but fails in production — because they skipped the part where models need pipelines, pipelines need data, and data needs to be clean before any of it is useful. We've done this enough times to know where it breaks.

01

Understand the product

Before we write a line of code, we need to understand what the product does, who uses it, and where AI changes the equation. We ask why before we ask how.

02

Design for production

We design the architecture around production constraints — data availability, latency, cold start, model drift. Not around what's easiest to demo.

03

Build and ship

We build the feature, the pipeline, and the infrastructure it runs on. Everything is instrumented from day one so you can see what's working and retrain when it isn't.