AI integration
Models wired into the work that pays off, chosen for the job rather than the headline, with a human reviewing anything that ships.
What it covers
- Models from Anthropic, OpenAI, and the open ecosystem
- Your own documents and data made available to the model safely
- Evaluation before rollout, so you know what it gets wrong
- Human review built into anything that goes to a customer
- Cost and latency measured rather than hoped for
- Fallbacks for when a provider is down or a result is poor
How it works
Find where it earns
We look for the tasks where a model genuinely helps, and say so plainly when the answer is that it does not.
Build and evaluate
Wired into the real workflow and tested against real examples, so you see the failure modes before your customers do.
Ship with a human
Anything customer-facing keeps a person in the loop, and the system is monitored for cost, latency, and quality.
Who it is for
Companies with a repetitive judgement task and a pile of their own material that could inform it.
Questions we get
Which models do you use?
Whichever fits the task, from Anthropic, OpenAI or the open ecosystem. The choice follows the job and the cost, not the headline.
How do we know it is accurate?
It is evaluated against real examples from your own work before rollout, so you see the failure modes before your customers do. Anything customer-facing keeps a person in the loop.
Is our data used to train a model?
No. Providers and settings are chosen so your material stays out of training, and the arrangement is agreed in writing before anything is wired up.