About

Verdify turns human expertise into organization-owned AI advantage.

Verdify is based in Longmont, Colorado, and works with Front Range teams and Microsoft-aligned organizations that want AI to compound what their people know without surrendering control of sources, approvals, systems of record, IP, or operating judgment.

Verdify is built around a practical belief: useful AI is not a generic tool bolted onto the side of an organization. It is a cognitive loop between people and software: knowledge, AI models, workflow traces, feedback, evals, and outcomes improving together.

Verdify Lab is the public proof environment behind that belief: AI proposes. Controls constrain. People judge. Telemetry verifies. Outcomes become learning signal.

See the Live Lab Explore Services Explore Knowledge Architecture

Who you work with

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

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

Jason Vallery

Founder; applied AI and cloud infrastructure architect

[email protected]

Jason is an applied AI and cloud infrastructure executive who remains hands-on in architecture and implementation. Public biographies document more than 12 years at Microsoft, including service as Group Product Manager for Azure Blob Storage, and later leadership of cloud and service-provider strategy at VAST Data.

Verdify is where that experience becomes practical for customers: forward-deployed architecture, knowledge architecture, agentic workflow design, private evals, feedback loops, and implementation plans that survive contact with real operations. Jason built and operates Verdify Lab from Longmont in Boulder County to make the pattern inspectable instead of theoretical.

Operating focus

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

Public career sources

VAST Data speaker profile

James Vallery

Implementation architect; University of Colorado computer science

[email protected]

James studied computer science at the University of Colorado and works as Verdify's implementation architect. He works on the practical side of delivery: turning workflow maps, source requirements, control matrices, private evals, and feedback-loop 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 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
Private eval and evidence handoff
University of Colorado 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 learning loops are ready. Verdify helps those teams turn messy evidence into AI systems with explicit sources, authority, telemetry, private evals, feedback, and approval paths.

Organization details

Organization: Verdify

Base: Longmont, Boulder County, Colorado

Primary contact: [email protected]

Direct: [email protected] / [email protected]

Primary site: verdify.ai

Public proof lab: lab.verdify.ai

Principles

AI should make human expertise more valuable.

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.

Private evals

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

Restraint

'Not yet' is sometimes the best AI recommendation.

Talk with the people doing the work

Bring one workflow, the expertise behind it, and the decision you need to make.

Verdify will tell you whether an audit, source-knowledge pass, workflow sprint, or evaluation step is the honest next move.

Contact Verdify Discuss your workflow

Verdify is a fit when AI needs to become organization-owned capability.

Good fit when

You need a learning-loop audit before expanding AI authority.

You want source-grounded knowledge, explicit action limits, private evals, and feedback loops.

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 control product rather than a proof pattern.

You do not want to measure outcomes.

FAQ

Common buyer questions.

Why is the greenhouse part of the Verdify 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 organizational workflows where AI needs approved sources, action limits, telemetry, private evals, feedback, and human or system authority.

What makes Verdify different from a generic AI consultant?

Verdify leads with workflow reality: source quality, human expertise, action limits, authority, telemetry, private evals, feedback loops, exception review, and proof. The goal is not AI theater; it is organization-owned AI capability a team can defend and improve.