A complete strategy method — framing, analysis, the frameworks, pressure-tested — delivered fast by a senior team amplified by an AI agent firm. Strategy, AI adoption, and the build to back it up.
PVT runs a complete, disciplined method — twelve stages, the canonical strategy frameworks, every load-bearing number sourced and every answer pressure-tested before you see it. An AI agent team does the heavy lifting — research, modeling, synthesis — so the work is thorough and fast, without a big-firm price tag or a months-long wait.
We sell the machine, not the magic. The method is the product — and you can read all of it before you ever 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, end to end: a board-grade recommendation, financial model, roadmap, and KPI scorecard — war-gamed before it reaches you.
An AI opportunity map, use-case prioritization, a readiness assessment, an ROI model, and a responsible-AI and GDPR plan. Where AI fits — and where it doesn't.
Ongoing advisory with agent-firm access: 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. Being honest about fit is part of responsible work, so here's who we're for, and who we'll refer elsewhere.
The best engagements start at an inflection point: 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 follows the same four-phase rhythm — 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.
The method runs: framing, analysis, hypothesis, choice. War-gamed, sourced, and answer-first.
When the answer is AI, we build, deploy, and integrate with your team in the loop. Working software, not slides.
Training, feedback loops, optimization. We're done when your team doesn't need us.
Every stage and framework is on the table before you sign. You're buying rigor you can inspect.
An AI agent team does the research and modeling, so a credible interim view arrives in days — not the back half of a quarter.
A reviewer panel checks logic, sources, and risk before work reaches you. An accuracy ledger reconciles what we predicted against what happened.
We say where AI fits and where it doesn't, who we're for and who we're not. The goal is your decision, not our utilization.
Responsible AI isn't a buzzword — it's how we work. Every recommendation starts with your goal, not with whatever tool is generating hype that 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 whether it can really deliver serious work. Here's the honest answer — and the evidence behind it.
The same method and AI-first approach behind PVT built ShowOps.AI — an AI-native operations platform for large-scale live-event broadcast infrastructure: 13 modules, ~474 API endpoints, an AI-agent layer with per-tenant learning and human-in-the-loop review, and enterprise hardening underway (strict access controls, SSO/SAML + MFA, SOC 2 work).
It's a separate company — but it's the clearest proof that an AI-native approach can ship serious systems other shops staff with a full team.
Before any work reaches you, a panel of review checks — logic and structure, sources and accuracy, risk and responsible-AI, and client-fit — runs against it. It's the discipline a senior partner would bring, applied every time.
And we keep an accuracy ledger: predictions reconciled against outcomes, engagement after engagement. Most firms never measure whether they were right. We do.
Pedro spent 25 years producing live events — live broadcasts and large-scale productions, multi-million-dollar budgets, audiences past 50,000 — where the plan has to hold in real time. Then he built the software: he designed and shipped ShowOps.AI, an AI-native operations platform for live-event broadcast infrastructure, end to end on Claude.
Senior operator judgment paired 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 — and you work with senior people from first call to final delivery, 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 — the bread-and-butter of business AI work.
We build on Claude every day. We know where it shines, where it stumbles, and how to deploy 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 isn't a policy document, and it isn't bolted on at the end. It's six commitments built into the method — claims you can hold us to.
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 don't share it with third parties. 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. No black boxes. If we leave, you can still explain it.
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.