About

Verdify turns AI ambition into systems a team can operate and defend.

Verdify is an evidence-led consulting practice for teams that want AI to help with real work without losing control of sources, approvals, systems of record, or operating judgment.

The company is built around a practical belief: useful AI is not a chatbot bolted onto the side of a business. It is a controlled system of knowledge, tools, logs, policies, approvals, and review loops.

Verdify Lab is the public proof environment behind that belief: The AI planning system proposes. Control layers constrain writes. Firmware controls. Telemetry verifies. Scorecards and lessons close the loop.

See the Live Lab Explore Knowledge Systems

Who you work with

A small team close to architecture, implementation, and evidence.

Verdify engagements stay close to the workflow owner, technical path, knowledge sources, review owners, and proof needed for a defensible decision.

Jason Vallery

Founder; applied AI and cloud infrastructure architect

Jason is an applied AI and cloud infrastructure executive who is still hands-on in the editor. He spent 13 years at Microsoft on Azure Blob Storage, serving as the Azure-side product owner for OpenAI and Microsoft AI production storage workloads at multi-exabyte scale during the frontier AI training era. He now leads cloud product strategy at VAST Data across hyperscalers, GPU cloud providers, and AI infrastructure partners.

Verdify is where that experience becomes practical for customers: forward-deployed architecture, source-grounded knowledge systems, controlled AI workflows, scorecards, and implementation plans that survive contact with real operations. Jason built and operates Verdify Lab, the public AI-run greenhouse proof environment, to make the pattern inspectable instead of theoretical.

Operating focus

Executive AI architecture and roadmap translation
Cloud-scale data and AI infrastructure
Source-grounded knowledge and retrieval systems
AI tools, MCP, telemetry, and review loops
Public proof-lab research and operating evidence

James Vallery

Implementation architect; CU Boulder computer science

James is a computer science student at the University of Colorado Boulder and Verdify's implementation partner. He works on the practical side of delivery: turning workflow maps, control matrices, source requirements, and scorecard ideas into buildable tasks, test cases, documentation, and review routines.

His role is intentionally close to implementation. James helps keep Verdify recommendations grounded in what a technical team can actually ship, instrument, hand off, and operate. He supports the proof lab, website, workflow examples, and client implementation planning while building the engineering judgment expected of a working software architect.

Operating focus

Implementation planning
Source and workflow instrumentation
Acceptance checks and documentation
Scorecard and evidence handoff
CU Boulder computer science

Why the lab exists

A real operating environment makes claims accountable.

Verdify's public proof environment lives in a Longmont greenhouse in Boulder County. The local setting is useful because weather, sensors, energy, water, hardware limits, and operator routines make AI planning answer to real conditions.

The commercial pattern is broader: teams often have scattered knowledge, high-stakes workflows, and AI pressure before their operating controls are ready. Verdify helps those teams turn messy evidence into AI systems with explicit sources, authority, telemetry, scorecards, and approval paths.

Company details

Company: Verdify AI Consulting

Contact: [email protected]

Primary site: verdify.ai

Public proof lab: lab.verdify.ai

Principles

AI should earn operational trust.

Authority

Humans and systems of record remain authoritative where consequences matter.

Action limits

Every workflow needs explicit allowed and prohibited actions.

Source control

Answers and evidence should trace back to approved records.

Scorecards

Claims should be tied to observed evidence, not adoption enthusiasm.

Restraint

'Not yet' is sometimes the best AI recommendation.

Verdify is a fit when AI needs to survive contact with operations.

Good fit when

You need a workflow audit before expanding AI authority.

You want source-grounded knowledge, explicit action limits, and scorecards.

You have operators, reviewers, systems, or control layers that must remain authoritative.

You value public proof, caveats, and known limits.

Not a fit when

You want generic AI hype or broad strategy theater.

You want AI to bypass review, controls, or systems of record.

You need a greenhouse automation product rather than a proof pattern.

You do not want to measure outcomes.

FAQ

Common buyer questions.

Why is the greenhouse part of the company story?

The greenhouse makes Verdify's operating philosophy inspectable. It is a real system where AI planning meets sensors, weather, hardware limits, resource costs, telemetry, scorecards, and known limits.

Does Verdify only work on greenhouse systems?

No. The greenhouse is the public proof lab. Verdify applies the same discipline to business workflows where AI needs approved sources, action limits, telemetry, scorecards, and human or system authority.

What makes Verdify different from a generic AI consultant?

Verdify leads with workflow reality: source quality, action limits, authority, telemetry, scorecards, exception review, and proof. The goal is not AI theater; it is a workflow a team can defend and operate.