Services

AI & Automation

We build AI as a layer inside the screen your team already uses, not as a separate product. One metric matters: how much work came off a person's desk.

Overview

Most AI projects die in an impressive demo. The demo runs on clean data and one happy path; production is messy data and edge cases.

We start from the edge cases. When the model can be wrong, what happens when it is, and how a human steps in — all of that is designed first.

Scope stays small and grows on evidence: one workflow, a measurable time saving, then the next workflow.

How we run it

  1. 01

    Pick the flow

    We identify the single flow eating the most human hours.

  2. 02

    Baseline

    Today's duration and error rate are measured as the reference point.

  3. 03

    Pilot

    A narrow release with real users and a reversible rollout.

  4. 04

    Expand

    If the numbers hold, scope grows. If not, it stops.

Frequently asked

Does our data go to the model provider?

That is an architecture decision and it is yours. We can use enterprise API terms that exclude your data from training, run open models on your own infrastructure for sensitive fields, or mix the two. Which data goes where is written explicitly in the architecture document.

What happens when the model is wrong?

We build assuming it will be. Every flow has a confidence threshold, a human handoff point and a rollback path. We also keep an evaluation set: the same questions run before every release, and if accuracy drops the release does not ship.

What does it cost per month?

Token cost depends on volume and is measured with real traffic during the pilot. At the end of the pilot you get a monthly cost range in writing, together with how caching and model selection bring it down.

Related services

Do you have an idea, or a product that has stalled?

Let us scope it in a short call. In the first conversation we cover the technical approach and an estimated budget range.