One team, no handoffs
Automation usually dies in the gaps — between the AI vendor, the data team and whoever owns deployment. We cover all three, so there is nowhere for it to fall through.
What we do
Three disciplines, and what each one hands you.
The hard part was never the model. It is everything around it — the data, the approval step, the evals, and the moment it fails at 3am and someone has to understand why.
01
AI automation & workflow
We take the process that quietly eats a day a week and automate it end to end — running in your stack, with approvals where a human still needs to sign off.
What you get
- A working workflow deployed in your environment
- Human approval steps and full audit logs
- An eval suite that measures quality, not vibes
- A rollback plan and a runbook your team can operate
02
AI optimization
Systems that already run, made cheaper, faster and more reliable. Usually the win is unglamorous: routing, caching, batch sizes, and the calls you stopped making at all.
What you get
- A cost and latency audit with real numbers, before and after
- Model routing and caching tuned to your traffic
- A regression suite so quality does not silently degrade
- A written record of what changed and why
03
Software & data engineering
The product and the plumbing around the automation. Web apps, services, pipelines, and the deployment path that gets them live and keeps them there.
What you get
- Web and backend services, built and deployed
- Data pipelines with monitoring and alerting
- CI/CD and infrastructure as code
- Documentation and a handover that survives you leaving
How we work
Three commitments.
We say no to bad projects
Plenty of "AI opportunities" are a spreadsheet and a rule engine wearing a costume. We will tell you when that is what you have. It costs us a sale and saves you a quarter.
Production or it does not count
A demo proves a model can do something once. We are only interested in the version that runs on Monday morning without anyone watching it.