Improve an existing
AI feature.
Find where outputs fall short. Compare prompts, retrieval, and model customization against real examples and a baseline.
Discuss an AI diagnosticAPPLIED AI ENGINEERING
Make your AI
work in the
real world.
Turn inconsistent AI outputs into a workflow you can measure, improve, and ship.
Tell us what you’re buildingFor software teams, agencies, and enterprise projects.
FROM EXPERIMENT TO PRODUCTION
01
START WITH YOUR WORKFLOWReal inputs.
Real inputs.
A clear definition of good.
02 / FIND WHAT WORKS
Measure.
Compare.
Improve.
PromptingRetrievalFine-tuning
Evidence before deployment
Quality · Cost · Latency
YOUR DATA. YOUR CRITERIA. A CLEAR NEXT STEP.
Built around your workflowMeasurable acceptance criteriaDefined scope, direct collaboration
WHERE WE HELP
Close the gap between
a demo and a useful product.
Improve an existing
Bring engineering
to your AI projects.
Add focused implementation support for client work: data preparation, evaluation, integrations, and a documented handoff.
Discuss a delivery partnershipMove a defined
workstream forward.
Contract engineering for a bounded AI initiative, with milestones, acceptance criteria, and collaboration with your existing team.
Discuss a contract engagementA PRACTICAL FIRST ENGAGEMENT
Start with one task.
Leave with evidence.
An AI diagnostic gives you a baseline, a tested direction, and a clear recommendation before you commit to a larger build.
- 01
Define success
Choose one workflow, review the available data, and agree on the metrics that matter.
- 02
Test the options
Compare improvements on examples held back from development. Report what helps and what doesn’t.
- 03
Choose the next step
Get the findings, implementation plan, and tradeoffs. Scope deployment separately if the results justify it.
LET’S FIND THE USEFUL NEXT STEP
What’s getting
in the way?
Tell us about the feature, workflow, or project. We’ll follow up to discuss fit and a sensible scope.