AI automation services for businesses
Applied AI on your own data, scored against a regression set before anything reaches a customer.
The hard part of AI automation is not getting a model to do something impressive once. It is knowing whether it is still doing it correctly next week, on inputs nobody anticipated, after somebody changed the prompt.
That is an evaluation problem before it is a model problem. A process you are willing to automate is one where you can state what a correct output looks like and measure how often you get it — and if you cannot state that, the honest answer is that the process is not ready to automate, whatever a demo suggests.
So we build the harness first: a scored set of real cases, a threshold, and a refusal path for the inputs the system should decline rather than guess at. Then we automate, and you can see what it costs you in errors.
What the work includes
Retrieval over your documents
Answers grounded in your own SOPs, contracts and product material, with the citation attached so a human can check the source.
Evaluation harnesses
A regression set of real cases with known-good answers, scored on every change. Without one you are not iterating, you are guessing.
Guardrails and refusal
Explicit boundaries and a system that declines cleanly when it is outside them. A confident wrong answer costs more than no answer.
Workflow and agent automation
Multi-step processes with human checkpoints where the cost of being wrong is high, and without them where it is not.
Integration into what you already run
Into the CRM, the helpdesk, WhatsApp or the internal tool — automation nobody has to open a separate tab for.
Deployment in your own cloud
Where data residency or confidentiality requires it, the whole thing runs in your account rather than ours.
Find a process worth automating
Volume, a statable correct answer, and a tolerable cost of error. Processes failing any of the three get named as such — that conversation saves more money than the build would.
Build the evaluation set first
Real cases with known-good answers, agreed with the people who currently do the work. This is the artefact everything else is measured against, and it is built before any automation ships.
Automate to the threshold
Build, score, iterate until it clears the bar you set — and put a refusal path under everything below it, so the system escalates rather than improvising.
Watch it in production
Scores tracked on live traffic, drift surfaced, and the regression set growing as new failure modes appear. The harness is not a launch gate, it is how the thing is operated.
What it is built on
Questions we get asked
How do we know the AI is not making things up?
Because you measure it. Every answer is grounded in retrieved source material with the citation attached, and the system is scored against a regression set of real cases on every change. Hallucination stops being a worry and becomes a number you watch.
Does our data get used to train a model?
No. The work runs on your data through retrieval, not fine-tuning, and where confidentiality requires it the whole system is deployed in your own cloud account. Provider terms are set to exclude training.
Which processes are actually worth automating?
Ones with enough volume to matter, a correct answer you can state, and an error cost you can live with. Judgement calls with no agreed right answer are the ones that look like good candidates in a demo and fail in production.
What does it cost to run?
Per-token model costs are usually the smaller line; retrieval infrastructure and human review of the escalation path are typically the larger ones. We model the running cost during scoping, because an automation that costs more than the people it replaces is worth knowing about early.