Services
Plan · Build · Scale
One senior team,
from decision to production.
Strategy that has to survive contact with real data, systems that are engineered for real traffic, and a team that stays close once they run.
01
Plan
Plan.
Decide where AI pays off.
01
Discovery Sprint
Half a day with your leadership and domain experts. We map use cases, data and risk, and rank them by value and feasibility.
02
Strategy Blueprint
Two days to a roadmap, a business case and an architecture sketch your board can approve.
03
Data readiness review
What you actually have, what it is worth, and what it takes to make it usable.
02
Build
Build.
Engineer systems, not demos.
01
Custom AI systems
Models, retrieval and agents designed around your data, integrations, latency and cost.
02
Production Pilot
About four weeks to a pilot that is built to go live, with evaluation and monitoring from day one.
03
Data & ML engineering
Pipelines, feature stores and training infrastructure that keep working after hand-over.
03
Scale
Scale.
Keep it running and improving.
01
Production Review
A senior second opinion on a system you already run: reliability, cost, drift and security.
02
Run & evolve
On-call engineers, retraining cadence and a quarterly roadmap with your product team.
03
Team enablement
Pairing, documentation and training so your engineers own the system with confidence.
04
How we work
Four steps.
No hand-offs.
Step 01
Decide
Use cases ranked by value, data and risk. A clear go or no-go.
Step 02
Prove
An evaluation set and a thin vertical slice on your real data.
Step 03
Ship
Production engineering: latency, cost, monitoring, security.
Step 04
Own
Docs, training and a retraining cadence your team can run.
Next
Your pipeline
Bring us the
business problem.
We’ll tell you whether AI is the right move, and what it would take to make it work in production.