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AI & Intelligent Systems · AI Agents & Intelligent Workflows

Put AI to work on a well-defined problem.

We explore AI assistants and workflow integrations where they can help people find information, prepare work or route tasks — without presenting uncertain output as fact or removing necessary human oversight.

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Illustrative diagram

Useful when

Knowledge is scattered across documents, the same questions arrive repeatedly, or routine preparation work slows skilled people down.

What we need from you

  • The task and what a good result looks like
  • Approved source documents or data access
  • Example questions or cases for evaluation
  • Your privacy, retention and approval rules

How it works

Controls around every AI step

AI is one step inside a controlled workflow. Nothing acts on its own without the controls you agree.

Illustrative diagram

  1. Approved sources

    Only the documents and data you sign off

  2. Model & logic

    Retrieval, prompts and rules for the task

  3. Evaluation

    Tested against real questions before and after launch

  4. Human approval

    Required where an error would be costly

  5. Audit trail

    Inputs, outputs and decisions logged for review

  6. Exception path

    Low confidence or out of scope

    Says what it can't confirm and hands over to a person

Choose the simplest reliable option

“AI agent” covers very different things. We start with the simplest approach that solves the problem and only add autonomy where it's safe and worth it.

  1. Deterministic automation

    Clear rules, no AI. Often the most reliable answer for predictable tasks.

  2. Retrieval-based assistant

    Answers questions from your approved documents and cites its sources.

  3. Tool-using agent

    Can look things up or prepare actions in your systems, within strict permissions.

  4. Human-approved workflow

    AI drafts or prepares the work; a person reviews and approves before anything happens.

What's included

  • Use-case discovery and feasibility
  • Source data preparation
  • Retrieval and answer design
  • Access controls and permissions
  • Tool integration with approval points
  • Evaluation sets built from real examples
  • Failure and hallucination handling
  • Observability: logs and monitoring
  • Cost and privacy review
  • Human escalation paths

Not included by default

  • Unrestricted access to business systems
  • Sending confidential data to unapproved providers
  • Claims of perfect accuracy

How we approach it

  1. 1Discover
  2. 2Scope
  3. 3Prepare data
  4. 4Prototype
  5. 5Evaluate
  6. 6Operate

How we evaluate before launch

  • A test set of real questions or tasks, with expected answers
  • Measured answer quality, including “I don't know” cases
  • Checks that sources are cited and correct
  • Permission tests: the agent can't do what it shouldn't
  • Cost per task estimated from real usage
  • A human fallback that's easy to reach

Internal knowledge assistant

Illustrative scenario · not a client case study

Before

  • Staff search shared drives and ask colleagues
  • Answers differ depending on who you ask
  • Experts spend time on repeat questions

After

  • An assistant answers from approved documents with citations
  • Unclear questions are routed to the right expert
  • Gaps in documentation become visible and fixable
Prototype · sample knowledge only

See the idea in working form

AI Support Assistant Demo

Ask questions against a small authored knowledge base. Answers cite their source — or say plainly when they can't help.

Try it

Works well with

Questions we're often asked

Can AI be perfectly accurate?

No. Good design makes uncertainty visible, cites sources and manages failures with a clear human path.

Does every business need an agent?

No. Sometimes a simple workflow or a better search is more reliable — we'll say so.

Can it work with our tools?

Feasibility depends on APIs, access controls, data quality and governance. We confirm these early.

Which AI models do you use?

We choose per project, based on accuracy on your examples, data-handling requirements, cost and where the provider processes data.

Identify a useful AI workflow

Share the context and we'll help define a sensible first step.

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