AI agent consultancy

AI agent consulting for small teams

We help UK consultancies and professional services firms choose a useful workflow, test it on real cases and put it into production. Pick the engagement that matches where your team is today.

Consulting packages

Before a pilot

AI workflow roadmap

Decide which recurring task is worth tackling first.

  • A workshop to map recurring tasks and the staff time they take today
  • A shortlist ranked by expected value, data readiness and effort
  • A recommended first pilot with success measures, access needs and a named reviewer

3 weeks

Workflow pilot

Build and test one research or operations workflow for one team.

  • Read-only access to up to two supported data sources
  • A working deployment in an agreed test environment
  • An evaluation on 20 cases, with run logs and a scorecard
  • The code in your repository, a runbook and a handover session

After a successful pilot

Production rollout

Put a tested workflow into day-to-day use in your environment.

  • Deployment and agreed integrations in an environment you control
  • Access permissions, human review steps and a fallback to the manual process
  • Monitoring for run failures and model costs, with agreed output quality checks
  • A runbook and walkthrough for the people who operate it

1 week

Framework setup

Give your developer a working foundation to build on.

  • A framework recommendation based on your workflow and hosting
  • A runnable starter with one model provider, a sample tool and a test
  • Setup instructions and a walkthrough for your developer

Hands-on training

Team workshops

Give your team practice reviewing and maintaining agent workflows.

  • A session built around an agreed workflow and representative examples
  • Exercises in checking evidence, handling failures and escalating uncertain outputs
  • A checklist for operators or developers to use after the session

per month

Ongoing support

Get help maintaining a workflow after handover.

  • Up to four hours a month
  • A review of errors, output quality and model usage costs
  • Agreed fixes or small improvements within the time allowance

Common starting points include research briefs with citations, internal knowledge searches and document intake with human review.

Workflow pilot

Three weeks from scope to handover

  1. Week 1

    Scope the workflow

    Choose the task, agree what a usable result looks like and measure the time the manual process takes.

  2. Week 2

    Set up and build

    Select the framework, connect the agreed sources and build the workflow in a test environment.

  3. Week 3

    Evaluate and hand over

    Test 20 representative cases. Review acceptance, staff time including human review, and model cost per run.

Explore a research brief workflow or read our guide to testing and releasing agents.

Before we start

Practical questions

What should we bring to the pilot?

A process owner, a named reviewer and representative examples. We start the three-week timeline once we have the agreed source access and test environment.

Which framework will you use?

We choose a framework with your team based on how the workflow runs, which tools it needs and who will maintain it. Options include PydanticAI and LangGraph.

Where does the workflow run?

The pilot runs in an agreed test environment using approved access. Your team keeps the code in its repository and pays its model and hosting providers directly.

What happens after the pilot?

We review the scorecard with your team. If the workflow meets the agreed criteria, we can plan the production rollout around your systems, reviewers and support needs.

Independent consultancy

About Calina Labs

We agree how to measure results up front. Then we test difficult cases and hand over code your team can maintain.

Read our engineering notes

Have a workflow in mind?

Bring a recent example, its monthly volume and the time it takes today.

Get in touch