Deployments
Software used in daily operations.
Education, healthcare revenue cycle and waste management. Three industries that look nothing alike, running the same underlying problem: a core operation held together by spreadsheets, paper and phone calls.
Production delivery experience
Real systems, without invented AI results.
We are building our first published AI optimization benchmarks, so we do not relabel earlier software projects as AI savings case studies. These deployments show the delivery discipline clients are buying: work inside real operations, against real data, with ownership handed back.
How we deliver
Built inside your systems. Owned by your team.
We measure before changing anything, work in your repository and cloud, prove every improvement against your real traffic, and hand over the code, evidence and runbooks.
- We measure before we change anything
- Nothing is optimised until it is instrumented. Cost per call, latency and quality are on record before the first change, so the improvement is a comparison and not an assertion.
- We work in your systems
- Your repository, your cloud, your accounts. Nothing is built in a private environment of ours and handed over at the end.
- Savings are metered, not modelled
- The number we report is the one on your provider invoice after the change, on the same traffic. We do not ship a spreadsheet projection and call it a result.
- No fix ships without an eval
- Every routing, caching or model change is compared against the current setup on your own cases. If quality drops on the cases that matter, the change does not go out.
- We hand it over and leave
- Code, IP, runbooks and system knowledge transfer to your team. The engagement is designed to end.
Every workload metered. Ten days. Fixed price.
Find out what your AI should actually cost you.
Tell us roughly what you are running, or that you are not sure what it costs. That is the usual answer. If it is a fit, we start with a ten-day audit and you keep everything we build in it.