AI readiness review
Where AI pays off in your systems, what data you have, what it will cost to run, and a sequenced plan.
AI Engineering
From pilot to production. Most AI pilots never ship. We build the parts that make them production systems: retrieval that is correct, agents that are bounded, evaluation that catches regressions, and privacy that survives an audit.
Problems we solve.
- A pilot that impressed the board but cannot be trusted with real customers.
- Answers that are fluent and wrong, with no way to measure how often.
- Data that cannot leave the country, the building or the device.
- Cost per query that grows faster than usage.
- A model bolted onto a .NET or JavaScript system nobody wants to touch.
- No one on the team who has taken an AI feature to production.
What we deliver.
Packages with a defined scope and a typical duration. Each ends with something you keep.
LLM feature build
Retrieval, agents and tool use integrated into your existing product, with guardrails and observability.
On-device and private AI
Models that run on the phone, on your servers or in your EU region, with no data leaving your control.
Evaluation and safety harness
Golden sets, automated grading and release gates so quality is measured, not assumed.
How it fits.
The stack.
- Claude and OpenAI APIs
- Open-weight models
- On-device inference
- Vector search
- RAG pipelines
- .NET
- TypeScript
- Python
Why ZoneTech
Proof, not promises.
ReadMyBaby runs its cry-analysis model entirely on the phone, and HomeZone's search understands plain-language property questions. Eight AI-native products shipped is our evaluation harness.
ReadMyBaby
Understand the cry. Track the day. Privacy-first AI on the device.
Start the conversation.
Tell us about the system and the constraint. We reply within two business days.
Contact ZoneTech