A complete strategy method covering framing, analysis, and the standard frameworks, tested before you see it. A senior team delivers it fast, working with an AI agent team. Strategy, AI adoption, and the build to back it up.
PVT runs a complete, disciplined method: twelve stages and the standard strategy frameworks, with every number that matters sourced and every answer tested before you see it. An AI agent team does the research, modeling, and synthesis, so the work is thorough and fast, without a big-firm price tag or a months-long wait.
We show you how the work gets done. The method is the product, and you can read all of it before you sign.
Every engagement is scoped to the decision in front of you. Most clients start small and fast with a Strategic Diagnostic, then go deeper only if it's worth it.
We frame the real problem, read the market and competition, set a hypothesis, and size the opportunity. A fast, credible outside view to anchor the decision.
The full method, start to finish: a board-grade recommendation, financial model, roadmap, and KPI scorecard. We war-game all of it before it reaches you.
An AI opportunity map, use-case prioritization, a readiness assessment, a model of the business impact, and a responsible-AI and data-protection plan. Where AI fits, and where it doesn't.
Ongoing advisory with access to our AI agent team: monthly decisions, live dashboards, and on-call war-gaming. A standing strategy function without the headcount.
PVT Consulting is for any company facing a real decision and an AI question at the same time, whether you're a small team that needs a fast, clear answer or a larger one with a board mandate. We qualify by the situation, not your size. Below is who we're for and who we'll refer elsewhere.
The best engagements start when something forces a decision: a new funding round, a competitive threat, margin compression, a board mandate on AI, a pending build-vs-buy decision, or a stalled internal strategy effort that needs an outside view to move.
If that's the moment you're in, you don't have eight weeks and a big-firm budget to spare. That's exactly the gap we were built for.
Underneath the method, every engagement runs in the same four phases, so you always know where we are and what comes next.
We learn your business (the decision, the goals, the constraints, the team) before we recommend anything.
We run the method: framing, analysis, hypothesis, choice. The result is war-gamed, sourced, and answer-first.
When the answer is AI, we build it and integrate it with your team in the loop. You get working software, not slides.
Training, feedback, and tuning. We're done when your team doesn't need us.
Every stage and framework is on the table before you sign, so you can check the rigor you're buying.
An AI agent team does the research and modeling, so a working interim view arrives well before the final readout, not in the back half of a quarter.
A reviewer panel checks logic, sources, and risk before work reaches you. We record what we predicted so it can be checked against what happened.
We say where AI fits and where it doesn't, and who we're for and who we're not. Our goal is your decision, not keeping our own people billable.
Responsible AI is how we work. Every recommendation starts with your goal, not with whatever tool is getting the hype this quarter.
Sometimes the answer is AI. Sometimes it isn't. Either way, you'll know why.
The fair question about a lean, independent practice is how the work gets checked before you see it.
Before any work reaches you, a panel of reviewers checks it for logic and structure, sources and accuracy, risk and responsible AI, and client fit. It's the discipline a senior partner would bring, applied every time. The panel advises, and a principal makes the call on every deliverable.
And we keep an accuracy ledger: we record our predictions so they can be checked against what happened.
Pedro spent 25 years producing live events, including live broadcasts and large-scale productions with multi-million-dollar budgets and audiences of 20,000 to 50,000, where the plan has to hold in real time.
He pairs senior operator judgment with hands-on AI engineering, so the strategy and the build come from the same person. He builds on Claude in production daily and is pursuing the Claude Certified Architect credential. You work with senior people from first call to final delivery, with no handoff to juniors you never met. More about the practice →
We specialize in implementations on Anthropic's Claude. Three reasons it's where we start most engagements.
Anthropic's safety-first design mirrors our commitments. The tool and the practice agree on what good looks like.
Reading documents, drafting, reasoning, analysis, coding assistance. This is the bread and butter of business AI work.
We build on Claude every day. We know where it works well, where it stumbles, and how to run it in production.
Not a Claude problem? We'll tell you. Sometimes the right answer is ChatGPT, Copilot, or no LLM at all.
Responsible AI here is not a policy document added at the end. It is six commitments built into the method, and you can hold us to each one.
We assess whether AI is the right tool before we build anything. Sometimes it isn't. You'll know either way, and why.
We don't use your data to train models. We use vetted vendors under terms that bar training on client data. We define a data-handling plan before any model sees your information.
Decisions that affect customers, employees, or money route through a human review step. We design the workflow so the human can actually intervene, not just rubber-stamp.
We tell you where the AI is unreliable, what it can't do, and what it costs to run. Before launch, in writing.
We document how the system works in language your team understands. If we leave, you can still explain how it works.
For anything that ranks, screens, scores, or recommends, we run a bias check and document the results. We flag risks before you ship, not after.