About VVG
One accountable lead.
Systems we keep running.
Verity Vantage Group is an AI engineering and operations firm. We build agentic systems that grow revenue and margin, and we keep them running. Dr. Jerry A. Smith leads every engagement and is accountable for the result; specialist AI agent teams do the work.
Meet the agentic engineering studio
Dr. Jerry A. Smith
I created VVG to do the work I enjoy most: understanding a difficult business problem and engineering a system that helps people solve it.
My background spans Navy nuclear engineering and carrier aviation, a PhD in Computer Science, enterprise AI leadership, and hands-on software development. Before VVG, I led AI work inside consulting and technology firms.
Today, I lead VVG’s work building and operating agentic systems in the workflows that drive revenue and margin: quoting, cash, document-heavy review and decision records. I work directly on architecture, code, evaluation and operation. The person understanding your problem stays involved in building the solution.
AI development tools are part of my process. Engineering judgment, review, and responsibility for the result remain mine.
Explore the engineering workThe agentic engineering studio
One accountable engineer.
Many ways to think.
A small practice can take on substantial work when the right minds work in parallel. At VVG, human judgment directs specialist teams of AI agents across research, architecture, design, engineering, adversarial review, innovation, and cloud deployment and monitoring on AWS and Azure.
Meet the teams
Different perspectives. Shared purpose.
Ten named teams of AI agents, assembled around the work.
Select a team to meet its specialists and see the handoffs.
ForgeCode & integration
“Make it work. Make it inspectable.”
Builds the working increment: application code, agent workflows, data connections, and the glue between existing systems.
Implementation · Integration · Code review
- Works from
- A scoped engineering task, repository context, and agreed interfaces.
- Brings back
- A reviewable change, a working example, and notes on limitations.
Does the actual behavior match the brief, including failure paths?
Passes the implementation to Sentry for an independent look.
Different model families.
A shared engineering discipline.
- OpenAI / GPT
- Anthropic / Claude
- xAI / Grok
Teams can draw on multiple model families.
Agent assignments and tools are selected for the task, context, and data requirements. Client information goes only to the enterprise services described below.
Client data
Your information stays
under your control.
More agents means more care about what they see. These commitments apply to every engagement, and each one is confirmed in writing before any client material is received.
Enterprise models only
Client information is processed only through Amazon Bedrock or Azure OpenAI, under enterprise terms that exclude training on customer data, in U.S. regions unless the client agrees otherwise. Other model families work only on public or non-client material.
You decide what agents see
We agree in writing what information agents may process. Any category can be excluded, and Jerry reviews excluded material directly.
Scoped access
Agents reach client systems only through access the client grants for the engagement, limited to the work.
Traceable work
Agent activity on client material is logged, so every output can be traced to its inputs.
Returned or deleted
At the close of an engagement, client materials, indexes, and logs are returned or deleted, with written confirmation.
How the studio works
Parallel effort.
Deliberate checkpoints.
- 01
A clear brief
Jerry defines the problem, boundaries, and what a good result looks like.
- 02
Teams in parallel
Each team divides its brief among specialist agents. Independent tasks move together, with shared context and clear handoffs.
- 03
Adversarial challenge
Crucible makes the case against the proposed result. Sentry turns findings into repeatable checks. Agreement alone is not proof.
- 04
Innovation throughout
Origin looks across the work for novel methods and reusable patterns, capturing candidates and their provenance for Jerry to review.
- 05
Integration & evaluation
Bring the pieces together and check the actual workflow against representative cases.
- 06
A human release decision
Harbor and Stratus prepare the AWS or Azure release. Jerry and the client review the evidence, resolve open issues, and approve deployment.
- 07
Deployment & monitoring
The cloud team verifies the rollout, monitors health and performance, and investigates alerts. Relay carries the operating knowledge into handover; people guide incident response and recovery.
Less waiting between ideas and working software.
That is the advantage: explore options, build increments, and challenge the result in tighter loops. The pace depends on the problem. The responsibility stays with me.
Scout, Meridian, Forge, Prism, Sentry, Relay, Crucible, Origin, Harbor, and Stratus are creative team names for groups of AI agents. Jerry assembles and directs the teams for each engagement; their size, specialist roles, models, and tools vary with the work.
Working together
Clear responsibility from the first conversation.
One accountable lead
Jerry leads the work, reviews it and answers for the result. Specialist agent teams build and test in parallel, and you review working increments together.
A bounded engagement
Agree on the workflow, dependencies, acceptance criteria, fee, and responsibilities. Expand the scope when the evidence supports it.
We operate what we ship
Monitoring, fixes and operating procedures are part of the engagement. When you want to run it yourself, the handover includes what your team needs.
Research behind the work
Memory, reasoning, and reliable agent workflows.
My research explores how agents retain context, combine different perspectives, and check their work. I use those ideas where they solve a concrete engineering problem and evaluate them against the task at hand.
Read about the researchFor consulting and technology firms
VVG also works within larger delivery engagements. If your team needs someone to own a defined AI implementation, integration, or remediation workstream, we can scope that responsibility and the handover together.
Discuss a delivery partnershipStart with one workflow.
Tell me what your team does today, where the work gets stuck, and what a useful result would look like. We will use a short call to identify a sensible next step.