Core service
AI that is measured, not just demonstrated
An AI demo is easy. An AI system you can trust with real records for a year is a different piece of engineering — it needs evaluation against known-correct cases, a confidence threshold, and a defined path for the ones it should not decide alone.
The problem
What this usually looks like
You have a pile of unstructured input — emails, PDFs, forms, tickets — and someone reads each one to decide where it goes and what it says. It is slow, it is inconsistent between people, and it does not scale.
What you get
- The agent or pipeline, running in your own accounts
- An evaluation set of real cases with known-correct answers, and measured accuracy against it
- A confidence threshold, with low-confidence cases routed to a person
- Cost-per-run measured and documented, so the running cost is not a surprise
- Prompt and configuration under version control, not typed into a UI and forgotten
- A runbook covering what to do when output quality drifts
How the work runs
01
Collect real examples
Thirty to a hundred actual cases with the correct answer already known. Without these there is no way to tell whether the agent works or merely sounds convincing.
02
Build and measure
Build the pipeline, then run it against the evaluation set and report accuracy honestly — including where it fails and why.
03
Set the threshold
Decide the confidence level below which a human looks at it. This is a business decision about the cost of a wrong answer, so you make it, not us.
04
Run supervised
Every output reviewed for an agreed period. This both catches problems and builds the trust needed to reduce review later.
05
Hand over with a drift plan
Model providers change models. The runbook covers how to spot quality drift and re-run the evaluation set.
Stack
What we build it with
Honest limits
What this is not
- We do not build a customer-facing chatbot that answers freely about your business without guardrails and a documented escalation path.
- We do not put an unreviewed model decision in front of money movement, legal obligations or personal data.
- We will not claim an accuracy figure we have not measured on your data.
Systems we have already built like this
Every entry documents the business problem it solves, what it does and the architecture behind it. Not for sale — they are what we build for you, adapted to your systems.
Straight answers
How do you know the AI is accurate enough to trust?
By measuring it. We build an evaluation set from your real cases where the correct answer is already known, run the pipeline against it, and report the accuracy — including the failure modes. Any provider who gives you an accuracy figure without having run your data is quoting a benchmark, not a result.
Will our data be used to train a model?
No. We do not submit client data for training, and where personal data is involved a Data Processing Agreement makes that a contractual commitment rather than a promise. Which providers are used, and in which regions, is listed in the DPA annexes.
What does it cost to run, not just to build?
Model API usage is a per-run cost that scales with volume, and it is yours. We measure cost-per-run during the build and document it, so you can decide at what volume the economics still work. An automation that costs more to run than the labour it replaces is a bad automation, and we would rather find that out during the build.
Do we still need a person in the loop?
For anything consequential, yes — and that is a feature. Low-confidence cases route to a human, and the threshold is set by how expensive a wrong answer is in your business. Many clients reduce the review rate over time once they have seen the measured accuracy hold.
Describe the process, get an honest read
Thirty minutes, no obligation, and a written quotation if it is worth doing. Repomanta Information Technology Services issues every engagement in writing.