Verdify

Services

Choose the next decision, not a menu of AI services.

Verdify helps organizations move from an unclear AI opportunity to a source-grounded, controlled, measurable workflow that improves through expert feedback and real outcomes.

Start with the Audit Discuss your workflow

Maturity path

Move forward only when the evidence supports the next stage.

The stages form one decision path. An organization can enter where its current workflow belongs, but it should not skip unresolved knowledge, authority, evaluation, or feedback questions.

  1. 01

    The opportunity is important, but the learning loop is unclear

    AI Learning Loop Audit

    Map the workflow, expertise, source knowledge, authority, risks, feedback, and outcomes before committing to an implementation path.

    Decision produced: A build, defer, or reject decision with the first workflow and evidence requirements made explicit.

    Start with the Audit
  2. 02

    The workflow depends on scattered or unreliable knowledge

    Knowledge Architecture

    Organize approved sources, owners, access rules, freshness, citations, and gaps so people and AI can work from a source layer the organization controls.

    Decision produced: A source-linked knowledge foundation that can support answers, review packets, reporting, and controlled workflows.

    Explore Knowledge Architecture
  3. 03

    One workflow is ready to become a learning environment

    Agentic Workflow Design Sprint

    Build a narrow workflow where AI can read, classify, summarize, draft, recommend, or route while human approval and system authority stay explicit.

    Decision produced: A working workflow or implementation-ready path with controls, telemetry, acceptance checks, and feedback capture.

    Run a Workflow Sprint
  4. 04

    The workflow is plausible, but the organization-specific evidence is weak

    Private Evals and Outcome Scorecard

    Define test cases, reviewer rubrics, operating metrics, known limits, and the evidence required for an expand, tune, hold, or stop decision.

    Decision produced: A repeatable evaluation and review cadence tied to the organization's actual work and outcomes.

    Define Private Evals
  5. 05

    The workflow is live enough to learn from

    Feedback Loop Operations

    Review exceptions, overrides, incidents, feedback, eval results, and outcomes. Tune the workflow and expand authority only when the record supports it.

    Decision produced: An operating cadence that compounds learning instead of treating launch as the finish line.

    Operate the Feedback Loop

See the path in one workflow

See the learning loop in three workflows.

Worked examples.

Compare how source-linked knowledge, bounded AI authority, reviewer approval, private evals, and feedback change across buyer response, support, and operations.

Compare Worked Examples Explore Workflow Patterns

Prefer to assess the workflow first? Browse decision resources and worksheets.

Buyer-response packet

Approved answers, source trails, domain review, and final release authority.

Read the example

Support triage and handoff

Queue classification, account context, internal handoff prep, and customer-facing safeguards.

Read the example

Operations signals and exceptions

Signal synthesis, exception routing, controlled recommendations, and disposition feedback.

Read the example

What does not change

Every stage protects the learning loop.

The organization owns the source layer

Approved sources, owners, access rules, corrections, and outcome records stay reusable as AI models and AI model providers change.

Authority stays explicit

AI roles, prohibited actions, approval paths, controls, and systems of record are designed before scope expands.

Outcomes decide what happens next

Private evals, telemetry, reviewer signal, incidents, feedback, and mission or operating outcomes guide each decision.

Microsoft-aligned teams

Already using Foundry, Copilot, Fabric, Entra, Purview, Defender, or Azure services? Verdify defines the organization-owned learning layer around one workflow while the customer-owned Microsoft stack remains the platform.

See the Microsoft-Aligned Practice

When to involve Verdify

Bring Verdify in when AI needs to become organization-owned capability.

Involve Verdify when an organization has useful AI activity, but the knowledge, feedback, evals, and workflow traces are not yet compounding into durable advantage.

Where does expert judgment create the most value?
What knowledge should the system own?
Which workflow should learn first?
What AI model output needs human review?
What private eval proves quality?
Which corrections become learning signal?
Can the organization switch AI models or AI model providers without losing expertise?
Map the Learning Loop

Have context ready already? Send the workflow directly through Contact.