Put AI inside the products you already run
AI Integration & Custom AI Development
AI built into your existing software, or custom models and applications tuned to your data. Practical AI that runs in production, never just in a slide deck.
- Activationmeasured
- Cost/reqcontrolled
- Liftinstrumented
5%
The model
Calling it is the easy part, and the part everybody names.
95%
The product around it
- The data that grounds it
- The guardrails
- The evaluations
- The cost controls
In your app
No rewrite
Metric-led
Real lift
Agnostic
Any model
Instrumented
Measured
Enhance, don't rebuild
The model is 5% of the work. The product is the other 95%.
Calling an AI model is easy. Building a product that uses it well is not. The value lives in the software around it: the data that grounds it, the guardrails, the evaluations, and the cost controls.
We add AI to the features that genuinely benefit, ship them into your existing codebase and UI, and instrument them so you can see the lift.
Where it moves a metric
We add AI to the features where it lifts a real number, like activation, retention, or efficiency. Never just for the badge.
Into your stack, not around it
AI features ship inside your existing product and UI, where your users already are.
Measured and controlled
Instrumented so you can see the lift, with guardrails and cost controls in place.
6 ways AI gets into a product you already run
AI Integration for Existing Products & Platforms
AI features added to your current software, no rebuild required.
Custom LLM Applications
Applications built on commercial and open-weight models and grounded in your own data, far more than a chat wrapper.
AI Model Fine-Tuning & Domain Models
Models trained on your industry's data, for sharper accuracy and lower running costs.
AI Document Intelligence & Data Processing
Pull data out of documents, invoices, and forms, then classify it and act on it.
AI Personalization & Recommendation Systems
Recommendations and personalization that show each customer what they're most likely to want.
Predictive Intelligence & Forecasting
Forecasts for demand, revenue, and operations, built on your own history.
What changes once it is running
The difference shows up in operations. Here's what's true on the Monday after launch.
Where this lands hardest
The four situations this practice is asked for most often, and what it is actually doing in each.
Features that shipped into a product
What it connects to
It works inside the stack you already run. These are the connections this practice is built against most often.
Into your stack, not around it
AI features built into your existing codebase and UI, model-agnostic and measured, so the value shows up where your users already are.
- 01Ships into your existing codebase and UI
- 02Model-agnostic: commercial or open-weight models
- 03Guardrails, evaluations, and cost controls
- 04Instrumented so you can measure the lift
How an engagement runs
Four stages, in order, and exactly what happens in each.
You recharge, you don't rebuild
A better model goes into the plant you already have
We build model-agnostic, pick the right model for each feature, and keep you free to switch as providers and prices change, all without rewriting the product it lives in.
6 things that ship with it
An AI feature is easy to demo and hard to keep. These six things separate the two: the capability itself, the metric it was built to move, and the guardrails, measurement, and cost controls that decide whether it's still worth running in six months.
- AI features inside your existing product
- Capabilities that move a real metric
- Model-agnostic implementation
- Guardrails, validation, and review
- Instrumentation to measure the lift
- Cost controls that keep it affordable
Why bring this to us
Six commitments, each one something we actually do differently.
What people ask before they hand work over
Do we need to rebuild our product to add AI?
No. We integrate AI features into your existing codebase and UI, so the capability shows up where your users already are, without a rewrite.
Where should we add AI first?
Wherever it moves a metric you care about: activation, retention, support deflection, time saved. We start with the feature where the lift is real and measurable.
Are we locked into one AI provider?
No. We build model-agnostic, pick the right model for each feature, and keep you free to switch as providers and prices change.
How do we know it's actually helping?
Because we measure it. Each feature is tracked against the metric it's meant to move, so you can see the lift instead of assuming it.
The other eight practices
One senior team across the whole lifecycle, so the neighboring practice is a colleague, never another procurement exercise.
Let's talk
Running a large platform, shaping a first MVP, or getting a product ready for a funding round? Tell us where you are. We'll shape the process around it, and stay with you after launch.
