AI & Intelligent Systems

Get the AI pilot out of the demo and into the workflow.

We help decide which processes are worth automating with AI, then build them: retrieval over your own documents, assistants with proper access control, and agents whose actions a person can review.

AI strategy

From shortlist to first workflow

Three steps, each with a written output. You can stop after any of them and still have something useful.

Step 01

Choose where AI is worth it

We go through candidate processes with the people who run them. For each one we check whether the data exists, whether an error is cheap or expensive, and whether a simple rule would do the job.

Output: A ranked shortlist with the expected benefit, the main risk, and a success measure for each use case.

Step 02

Plan what has to change

Access rights, data quality, ownership, and who reviews the output. These decide most AI projects, so we plan them before the model.

Output: A 90-day plan with named owners, the data sources needed, and the first workflow to build.

Step 03

Build the first workflow

We build it with your team, test it against real cases, and put it in front of users. The results decide whether to expand, adjust, or stop.

Output: A working workflow, its evaluation set, and the monitoring needed to keep it honest.

If the question is bigger than AI, for example whether to replace the platform the AI would run on, we look at it together with Architecture & Advisory.

The problem

What we usually find.

Most organizations already have an AI pilot. Fewer have one in daily use. The pilot usually stalls on the same questions: which documents the assistant may read, who approves what it does, and how anyone knows the answers are still correct after the next model update.

What we do

Services.

  • Use-case selection: which processes are worth it, and which are better served by plain rules
  • Data and organizational readiness checks before any build starts
  • Retrieval systems that respect existing document permissions
  • Agent workflows with approval steps and narrow, auditable credentials
  • Evaluation sets built from your real cases, run on every change
  • Model and prompt versioning, monitoring, and cost tracking
What you get

Deliverables.

  • A ranked shortlist of use cases, each with an expected benefit, a main risk, and how success will be measured
  • A 90-day plan naming owners, data sources, and the first workflow to ship
  • An architecture document showing where the model sits and what it is not allowed to touch
  • A working first workflow with its evaluation set in your repository
  • A runbook for your team: what to monitor, when to escalate, how to roll back

One workflow in production, measured

We would rather ship one workflow with an evaluation set and an owner than five prototypes nobody runs. The second workflow then reuses the same retrieval, permissions, and tests.

Retrieval (RAG)Agent frameworksEvaluation suitesMLOps
Typical engagements

Where this usually starts.

Intelligent Process Automation

Invoices, claims, or support tickets that people copy between systems by hand. We automate the routine steps and leave the judgment calls to people.

How we approach it

AI-Driven IT Operations

Help for on-call engineers: pull together the relevant logs, metrics, and runbook for an incident, and suggest a next step.

How we approach it

Enterprise AI Applications

An assistant that answers from your own policies, contracts, or technical documentation, and only shows each user what they are allowed to see.

How we approach it

Need a decision before a build? See Architecture & Advisory

Contact

Have an AI pilot that stalled?

Tell us what it was meant to do and where it got stuck. That is usually enough to see whether it can be finished, or should be replaced with something simpler.

Get in touch