The challenge
Most AI never makes the leap from a promising proof-of-concept to something that runs in production and earns its keep. The gap usually isn't the model - it's everything around it.
Stuck in pilot purgatory
A demo that works in a notebook is not a product. Without a deliberate path to production, promising pilots stall - the value stays theoretical while the budget is already spent.
The model is 10% of the system
Data pipelines, integration with your stack, security, the interface people actually use, the edge-vs-cloud call - the unglamorous 90% is what decides whether it ships and survives contact with real users.
Who runs it on Monday?
A deployed model decays. Data drifts, inputs change, accuracy slips. Without monitoring, retraining and an owner, a system that worked at launch quietly stops working - and no one notices until it costs you.
How we solve it
We take the validated use case and deliver it end to end - building alongside your team so they can run and extend it after we leave.
MVP build
We turn the validated PoC into a working MVP against your real data and real systems - the smallest thing that delivers the outcome, not a science project. Scoped to the KPIs the Sprint set.
Production hardening
Integration with your stack, the edge-vs-cloud architecture, data pipelines, security and CI/CD. The engineering that turns a prototype into something that holds up under real load and real users.
Launch & pilot
We ship it to real users and measure against the target KPIs, not vanity metrics. A controlled rollout that proves the value in production before you scale it across the business.
Run & improve
The ops layer: monitoring, alerting, retraining and support, so accuracy holds as your data shifts. We operate it for you, or hand it over once it's stable - your call.
Where Build & Run fits
Build & Run is the back two-thirds of the journey - Develop and Deploy. You arrive here once a Design Sprint, or your own evidence, says it's worth building.
AI Design Sprint
Should we build this?
MVP
Can it run in production?
Productization
How do we operate and scale?
Proven results
Validated ideas, built and running in production - and still paying. Two recent builds:
Not every build is a model. Sometimes the thing that moves the number is a single analysis or a report that settles the question - we build whatever the decision actually needs.
Where it applies
Develop doesn't mean one kind of thing. We build whatever the decision needs - and operate it when it needs operating.
A production ML model
Pricing, demand forecasting, anomaly detection, predictive maintenance - scoring or forecasting that runs daily and feeds a real decision, with the pipeline and monitoring to keep it honest.
A GenAI system
Assistants over your documents, extraction from PDFs, drafting and summarization - wired into the workflow where the work actually happens, not a standalone chatbot.
An agentic workflow
Multi-step automation where an agent picks tools and acts in sequence - reconciliation, analysis, routing - with people in the loop for the high-stakes calls.
Sometimes just an analysis
Now and then the answer is a single, rigorous analysis or report - no system to run. If that settles the question, that's what we build. No solution in search of a problem.
What you get
You get a working system in production - and the ability to run and extend it - not a prototype that needs a rescue.
We can operate and improve it as a managed service, or hand it over once it's stable - whichever fits your team.
Related solutions
Build & Run is where validated ideas ship. Here's what usually comes first.
