Know where AI belongs
before you deploy it.

Amorph tests AI on your real work, so you can see what works before you deploy.

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Evidence loop
Human judgementHistorical workReview effortAcceptancecriteriaOperating costMachine agency

The problem

A fast answer is not a finished one.

Many teams do not know what an AI implementation will look like in practice or how much it will cost.

What LLMs show
AI modelGenerated output
What Amorph measures
Real casesAI setupExpert reviewAccepted work
  • Quality
  • Review effort
  • Total cost
  • Control
  1. 01

    Use your work

    Test historical cases against the standards your experts already apply.

  2. 02

    Count the full cost

    Include the work needed to review, correct and redo each output.

  3. 03

    Choose the right split

    See where AI should lead, assist or stay out of the workflow.

The Amorph platform

From prototype to production, on evidence.

One controlled loop, from your historical cases to a deployment decision you can defend.

Controlled pre-deployment simulation

Replay the work before it reaches production.

Historical cases run through alternative AI configurations. Every candidate sees the same frozen evidence, so the comparison is fair.

Evidence setConfigurationsPaired runs
Stage viewAmorph / simulate
Stage outputComparable evidence from real work

Early partnerships

Before you deploy AI,
build the evidence.

If you are weighing up AI for expert work, we would like to hear what the decision looks like from where you sit.

Start a conversation