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AI Enablement

Put AI to work on a specific task, with the right review.

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A good fit if

  • Leadership that wants people using AI well, not just experimenting
  • High-volume, repetitive back-office work
  • A need for clear rules on what AI can access and who checks its work
  • Owners who want to know where AI fits and where it doesn't

What we do

  • Roll out generative AI

    Training, shared prompt libraries, and secure access to AI models like Claude.

  • Build AI agents

    Tools that work from your connected data to draft proposals, summarize bids, match transactions, and flag exceptions for a person to review.

  • Set the controls

    A person checks the output before it's used. Each tool sees only the data it needs, actions are logged, and rule-based checks catch errors.

  • Start from existing tools

    Reusable agents, templates for data migration and integration, and tests drafted with AI and checked by our team.

Common uses

  • Proposal and bid generation
  • Financial reconciliation and issue flagging
  • AI-assisted data migration and integration testing
  • Document and contract analysis
  • Back-office workflow automation
  • AI training for the whole team
Illustrative example

Clear limits for every task

Each task is scoped: what the AI can see, what it does, and who checks the result before it's used.
Abstract illustration of three square panels in progressively enclosed frames, the last marked with an orange tab.

  • Bid qualification

    What the AI does
    Summarizes the bid package against your criteria
    Access
    Bid documents only
    Who reviews
    Estimator makes the go/no-go call

  • Expense reconciliation

    What the AI does
    Matches transactions and flags exceptions
    Access
    Read-only card and GL data
    Who reviews
    Accountant clears each exception

  • Proposal drafting

    What the AI does
    Drafts scope and exclusions from past proposals
    Access
    Approved proposal library
    Who reviews
    Project manager edits and sends
Sample tasks and controls for illustration. Access and review steps are set with your team for each task.

How it works

  1. Pick the task

    Start with specific, repetitive work where AI can help.

  2. Check the foundation

    Confirm the process, systems, and data the task depends on.

  3. Build with guardrails

    Set permissions, review steps, and validation before rollout.

  4. Train and improve

    Train the people using it, review the results, and refine.

Not sure where this would start for you? Walk us through how the work moves today.

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Client results

AI Enablement in practice

See all client work

What you keep

  • Tools and agents built for how your business operates
  • Prompt libraries and training for your teams
  • Written rules for what each AI tool can access and who reviews its work

Limits we set

  • A person reviews AI output wherever the task calls for it. Review steps are defined per task.
  • Each tool gets only the data and actions the task needs.
  • Actions are logged in an audit trail.
  • AI won't fix an unclear process or disconnected data. We address those first.

Questions

Where should we start?

With a specific, repetitive task. If the process, systems, or data behind it aren't ready, we get those right first.

How do you control what AI can do?

For each task we set what data the AI can see, what it's allowed to do, and who checks its work before it's used. Actions are logged, and rule-based checks confirm the results.

Which AI models do you use?

The rollout includes secure access to models like Claude, along with training and prompt libraries.

Related reading

Related services

Talk through your operations

Tell us which work takes too many hours today. We'll suggest where AI fits and where it doesn't.

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