AI Systems Engineering
Make models, knowledge search, agents, controls, quality tests, and operating infrastructure dependable in production.
Make production AI reliable, secure, and measurable.
We build and repair the models, knowledge search, AI agents, action controls, quality tests, and operating infrastructure inside AI products. You get measurable quality, controlled actions, clear failure handling, and evidence of how the system performs.
Review your AI systemWhen to bring us in.
These problems usually mean the system lacks tests, controls, or production monitoring that can show what changed and whether it helped.
It answers confidently and wrongly.
There is no reliable knowledge lookup, safe refusal, or source people can check.
It worked in the demo.
It was not tested against enough representative cases from real use.
Nobody can say whether the change helped.
There is no fixed evaluation set, so changes cannot be compared reliably.
It costs more every month.
No routing, no caching, and no cost attached to a task type.
Security will not sign it off.
No threat model for prompt injection, tool permissions, or data boundaries.
What we build and what you receive.
Choose an area to see a representative handover artifact. The values are illustrative, but the documents, tests, controls, and operating records are the work we deliver.
We index and split documents, enforce access rules, rank relevant passages, cite sources, and define what the system does when it cannot find enough reliable evidence.
You receive Test query set · source trace · safe-refusal tests
Knowledge search trace
“Can I return a helmet I have worn once?”
- 0.91policy/returns.md#wornSafety equipment showing signs of use cannot be resold and is not returnable.cited
- 0.74policy/returns.md#windowThirty days from delivery, for unused goods in original packaging.
- 0.31faq/shipping.mdReturn postage is paid by the merchant on faulty items.
Below 0.40 nothing is cited. The system abstains and hands to a person rather than assembling an answer out of the third result.
A release should ship only when quality, safety, speed, and cost meet agreed thresholds.
How we assess, repair, and operate an AI system.
We first establish real tasks, failure cases, and measurable acceptance criteria. That gives both sides evidence for deciding whether to repair, rebuild, or leave the system as it is.
- 01
Assess
We read the system, run an adversarial pass, and build the first evaluation set out of your real tasks.
You receiveAn eval set and a risk map
- 02
Repair or build
Knowledge search, agents, tools, controls, and operating infrastructure engineered against that evaluation set.
You receiveA system with tests
- 03
Gate
Release gates wired into CI, so a regression blocks the deploy instead of surfacing in support.
You receiveRelease gates
- 04
Operate
Monitoring, incident support, cost and quality review, and operating instructions for your team.
You receiveOperating infrastructure you own
Choose the service that matches the problem.
AI Systems Engineering covers model behaviour, knowledge search, agents, quality testing, security, and operating infrastructure. Product Engineering covers the application; Business AI & Automation covers the operational workflow.
Show us where the AI system is failing.
Send one real example, the expected result, and what happened instead. We will identify the likely failure area and propose the smallest useful assessment or repair.