Every engagement starts with a fitness assessment. This one is the fitness assessment, on its own: an AI opportunity map, ranked use cases, a readiness assessment, an ROI model, and a responsible-AI plan — with a go/no-go recommendation for each use case, in writing.
Most AI assessments arrive with a foregone conclusion — you need AI, and here's the roadmap. This one doesn't. PVT maps where AI could genuinely move the needle, ranks it against what your organization can actually support, and says plainly where the answer is no.
A structured view of where AI can move the needle across your business — and where it plainly can't.
Every candidate use case ranked by value and feasibility, so you know what to do first and what to defer.
An honest look at your data, tooling, team, and process — are you actually ready to run this, today.
The business case, quantified — cost, expected return, and payback, not a hand-wave about "efficiency."
Governance before build: data handling, human-in-the-loop points, and named limitations, set up front.
A clear call on every use case we surface — proceed, wait, or don't. You decide with a full picture, not a hunch.
The assessment moves fast because an AI agent team does the scanning and synthesis legwork — but every recommendation is reviewed and pressure-tested before it reaches you.
We learn your business — the goals, the data, the tooling, the team, the constraints — before we name a single use case.
An AI agent team accelerates the scan across your operations, surfacing candidate use cases fast and at scale.
Each candidate is tested against your actual readiness — data, tooling, team, process — and priced out.
A prioritized roadmap and a live readout — ranked use cases, the ROI case, and a go/no-go call on each.
This is the strongest differentiator of the assessment, not a footnote to it. Most firms selling an AI roadmap have an incentive to find opportunities everywhere. PVT doesn't work that way.
Where AI isn't the answer, we say so in the deliverable — not as a hedge in conversation you can't hold us to later.
A broken workflow or a missing dataset is often the actual constraint. Fixing that beats bolting AI onto a bad process.
Sometimes the right tool is simpler than an LLM, or there already is one. We'd rather say that than sell you a build you don't need.
You'll know why, in writing, for every use case we look at — proceed, wait, or don't.
The responsible-AI plan isn't an appendix — it's part of the assessment. Your data never trains models. Human-in-the-loop by default. Limitations named in writing. A bias and fairness review for anything that ranks or scores. Aligned to GDPR. See our full Responsible AI commitments →
The same approach behind PVT built ShowOps.AI — an AI-native operations platform for large-scale live-event broadcast infrastructure, with a Claude-powered agent layer, per-tenant learning, and human-in-the-loop review across 13 modules and roughly 474 API endpoints.
It's a separate company we founded, not a client engagement — but it's the clearest proof that this way of working ships serious, production-grade Claude systems that other shops staff with a full team.
Tell us where you're considering AI. We'll map the opportunity, size the readiness gap, and tell you honestly — in writing — where it's worth building and where it isn't.
Let's talk