All servicesAI features engineered into the product, not bolted on beside it.
Assistants, agents, retrieval and prediction built inside your web and mobile products by the same engineers who build the rest, evaluated on your data, guarded in production, and measured after launch.
AI features we build into products.
Each one starts from an evaluation set, not a demo.
The engagement, step by step.
Define & measure
Agree the job to be done and build the evaluation set from real inputs.
Prototype on live data
A working slice inside the product, scored nightly, iterated with users.
Harden
Guardrails, fallbacks, cost limits and tracing: the part that makes it shippable.
Launch & tune
Staged rollout with human review, then a monthly cycle of eval review and model updates.
Deliverables, not decks.
Everything is handed over as code, files and documentation you own. Nothing depends on us staying.
- Evaluation set with client sign-off
- Model, prompt and retrieval design
- Production integration in your product
- Guardrails and PII handling
- Tracing, cost and quality dashboards
- Model-swap plan and handover
Where we've done this.
The problem we started from and the results the client measured.
DICOM teleradiology, from scan request to report
Facial-recognition payments, no cards or PINs
How is this different from your AI services pages?
This is the engineering practice that builds those capabilities into web and mobile products. The AI section goes deeper on each capability; this page is where product teams start.
Which model do you use?
Whichever scores best on your evaluation set at a cost you can run, built so it can be swapped later.
How do you stop hallucinations?
Grounding with citations, schema validation, confidence thresholds and human escalation for anything below them.
Need ai & ml engineering?
Tell us the problem. We'll come back within one business day with how we'd approach it.
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brighter path.
Tell us what you’re building. We’ll be in touch within one business day.