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Controlled AI MVP Sprint

Build one AI workflow that is useful, controlled, and measurable.

A Controlled AI MVP is Verdify's build path for Verified AI: a scoped workflow where AI can read, summarize, classify, draft, recommend, or route while approved people, policies, or systems remain authoritative.

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Evidence from the lab

A controlled MVP gives the agent a useful role, not unrestricted authority.

In the greenhouse, the AI agent can write tactical intent through approved paths; the ESP32 still owns physical enforcement. A client MVP should follow the same discipline: narrow AI capability, explicit control layers, logged outcomes, and a clear stop or expand gate.

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What transfers to a sprint

Build one narrow workflow path instead of a broad autonomous system.
Keep irreversible, customer-facing, regulated, or system-of-record actions outside AI authority unless explicitly approved and reversible.
Test prohibited-action cases, approval paths, logs, and scorecard metrics before rollout.

Candidate workflows

A sprint should build one controlled workflow, not a grab bag of AI ideas.

These are good sprint candidates when source access, review ownership, prohibited actions, and scorecard metrics are clear enough to test.

Enterprise diligence response copilot

Assemble approved security, architecture, product, and policy answers into a procurement packet while legal, security, and executive owners approve anything customer-facing. AI may not invent posture or send buyer responses.

Cleantech pilot-to-procurement evidence pack

Turn pilot data, assumptions, safety notes, deployment plans, and diligence answers into a buyer-ready packet without letting AI overstate results, certify savings, or make customer claims.

Support-ticket triage

Classify, route, summarize, and draft internal escalation prep while agents approve external messages and closure.

Quality document assembly

Read approved sources, draft evidence packets, flag missing records, and preserve required review authority.

Retailer compliance drafting

Summarize claims, chargebacks, retailer context, and response options without making account commitments.

Field-service exception review

Group telemetry, work orders, and technician notes so reviewers can decide what requires action.

Sprint structure

The sprint moves from controls to working evidence.

The output is a narrow workflow that can be inspected, tested, handed off, and either expanded or stopped based on scorecard results.

Week 1

Confirm workflow, source access, acceptance criteria, action limits, and approval design.

Week 2

Build the first controlled path: read, classify, draft, recommend, route, or prepare evidence.

Week 3

Run test cases, capture failures, tune prompts or workflow logic, and define logs.

Week 4

Review scorecard, handoff notes, rollout risks, and the expand / hold / stop decision.

Before build

The control model is part of the scope.

A controlled MVP does not start by asking what the model can do. It starts by deciding what the workflow can safely allow.

What AI can read from approved systems and documents.
What AI can draft for internal or reviewer-facing use.
What AI can recommend and who approves exceptions.
What AI can execute, if anything, and whether that action is reversible and approved.
What AI must never touch in the first implementation.
What gets logged and which scorecard metric determines expansion.

A sprint works when one workflow is ready for controls.

Good fit when

You already know the workflow to test.
The workflow has repeatable inputs and reviewable outputs.
A human or system of record can remain authoritative.
Success can be measured with a simple scorecard.

Not a fit when

You want a broad enterprise AI platform before choosing a workflow.
You want AI to take unapproved irreversible action.
The source systems are inaccessible or undefined.
There is no owner for review, exceptions, or rollout.

FAQ

Common buyer questions.

What makes the MVP controlled?

The sprint defines what AI may read, draft, recommend, execute, and never touch before implementation. It also defines approval paths, logging, exceptions, and success criteria.

Will the sprint ship production software?

The sprint builds a narrow working workflow or implementation-ready MVP. Production rollout depends on risk class, systems access, security review, and scorecard evidence.

What actions should usually stay out of the first sprint?

Irreversible, regulated, customer-facing, unsafe, or high-consequence actions should remain prohibited until the workflow has stronger controls and evidence.