EquAI Factory

Delivery proof

Proof should look operational

Inspect the delivery artifacts, review steps, and change control behind the first company-controlled workflow before anyone asks for broader rollout.

See if your workflow is a fit
  • Hosting setup before build
  • Change control before expansion

What you can inspect

Sample outline

Controlled workflow package

Real proof comes from inspectable delivery assets, not abstract AI claims.

Workflow map
Trigger, AI step, checkpoint, output
Deployment frame
Hosting setup, model choice, access model
Evaluation frame
Success criteria, misses, overrides, sampling
Decision log
Reviewer action, policy reference, timestamp

The proof model stays narrow: one workflow, one deployment setup, and visible control from day one.

Proof hierarchy

Use the right proof

Start with concrete delivery proof: scope, review path, and change control.

Proof 01

Strongest

Outcome proof

Approved anonymized workflow results are the strongest proof.

  1. Proof 02

    Architecture and delivery proof

    The hosting setup, evaluation plan, model choices, and human-review steps make the delivery model inspectable.

  2. Proof 03

    Controls and change record

    Approval steps, retest paths, and a change log show how the workflow stays governed after launch.

Inspectable artifacts

What the buyer should be able to review

The first pilot should create real evidence a buyer can actually inspect.

  1. Artifact 01

    Workflow decision memo

    The first workflow, the two owners, what stays inside company control, and why this pilot should exist now.

  2. Artifact 02

    Week 2 architecture pack

    Hosting setup, model choice, tuning plan, and success criteria before implementation expands.

  3. Artifact 03

    Pilot launch pack

    Hosting setup, human review step, backup logic, and operator handoff before the pilot carries real work.

  4. Artifact 04

    Monitoring and change log

    What changed, who approved it, what was retested, and how quality, review load, and cost moved after launch.

Control in practice

What keeps the workflow under control

Controls are clear before launch and visible after go-live.

  • Control 01

    By launch

    • The hosting setup, backup path, and human review flow are defined
    • Operators know how they will use it
    • Quality, review load, and throughput measurement is set up

  • Control 02

    After launch

    • Monitoring covers quality, latency, cost, and reviewer load together
    • Mistakes and overrides feed an improvement loop
    • Model and policy upgrades follow a documented change path

  • Control 03

    Human approval built in

    • Explicit approval and escalation points
    • Operator override with feedback capture
    • Documented human review steps before launch

  • Control 04

    Evaluation before expansion

    • Acceptance thresholds before rollout
    • Issue taxonomy for misses and false positives
    • Live sampling after go-live

Keep reading

Next step

Bring the workflow to review

Use proof to narrow scope, then review the workflow or score its fit.

See if your workflow is a fit