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Delivery Models

AI Strategy & Advisory

Working through where AI genuinely fits in your systems and where it doesn't, with the cost and consequence of each candidate use case made explicit before any of it gets built — not after.

AI Strategy · Use-Case Evaluation · Advisory

What it is

Not every workflow is a good fit for AI, and the cost of finding that out after building something is much higher than finding out before. This advisory engagement works through your candidate use cases honestly — including telling you when a use case isn't a good fit — before committing engineering time to any of them.

How it works

  1. 01
    List the candidate use cases

    Every workflow you're considering applying AI to, gathered without pre-filtering for what sounds impressive.

  2. 02
    Evaluate fit and failure cost honestly

    For each one: is the pattern well-defined enough for AI, and what does a wrong answer actually cost if it reaches something that matters?

  3. 03
    Prioritize by real impact

    Which use cases are worth building first, ranked by actual return and risk — not by which one is easiest to demo.

  4. 04
    Leave you with a roadmap

    A concrete sequence of what to build and why, usable regardless of who builds it.

Benefits

  • An honest fit assessment, including use cases we'd tell you not to build
  • Failure cost made explicit before any engineering time is spent
  • A prioritized roadmap, not just a list of possibilities

Frequently asked

Will you tell us if AI isn't the right fit?

Yes — that's the point of an honest advisory engagement, not a sales conversation for a build we'd rather sell you.

Does this engagement include any implementation?

No — it's strategy and prioritization only. Implementation is a separate, scoped engagement once you know what's actually worth building.

Also under Delivery Models

Systems Readiness Review

An audit of what you're actually running today, before any AI or integration work starts.

Ongoing Managed AI Support

Monitoring and maintaining an AI system after it ships, instead of letting it degrade quietly.

Fixed-Scope Product Builds

A defined piece of software, scoped and quoted after discovery, delivered at a fixed cost.

Proof-of-Concept-to-Production Handoff

Rebuilding the parts of a prototype that don't survive contact with real data, load, or users.

Documentation & Handover

Written documentation of what was built and why, so your team can extend it without calling back.

Not sure this is the right fit yet?

A scope call is a lower-commitment way to find out before anything gets built.

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