AI Pilot

What an AI Pilot actually is

Most "feasibility studies" in AI are slide decks: a competitive landscape, a vendor comparison, and a hand-wavy "yes, this should work." Ours are different.

In 4-6 weeks, we deliver:

• Working code running on a sample of your actual data — not a synthetic dataset, not a public benchmark.
• A quantitative evaluation against a success metric we agreed to in week 1.
• An architecture sketch that shows how the prototype becomes production, including the deployment pattern and compliance posture.
• A go/no-go recommendation with a real estimate for the full Build.

If "AI Pilot" doesn't translate at your organization, it's exactly the same thing as what your procurement team will call a feasibility study or a proof of concept. We use both terms interchangeably with customers; the deliverable is the same.

When this is the right engagement

• You have a specific use case in mind and need to validate that AI can actually solve it on your data.
• Your team has tried prompts in ChatGPT or Claude, and you want to know whether a real production system is feasible — and what it would cost.
• A vendor has pitched you, and you want a neutral, hands-on validation before committing.
• You need to convince a skeptic (a CFO, a CISO, a board member) with evidence, not narrative.

How it works

• Week 1: Use-case definition, success metric, data audit, compliance review
• Week 2-4: Hands-on prototype build on a sample of your real data
• Week 5: Quantitative evaluation against the success metric; iteration
• Week 6: Architecture sketch, full-build estimate, and go/no-go recommendation

What we deliver

You walk away with:

1. The prototype itself — code, configuration, evaluation harness — handed to your team.
2. An evaluation report showing how the prototype performed on real data against the metric we agreed to.
3. An architecture document showing how this becomes a production system inside your security boundary.
4. A full-build estimate with timeline, scope, and pricing options.
5. A documented compliance posture matched to your regulatory footprint.

Why we believe in hands-on feasibility

A slide deck can prove that something is theoretically possible. Only working code can prove it's possible on your data, with your latency budget, at your accuracy bar. Most failed AI projects we've inherited from other vendors fail because that step was skipped.

The original feasibility study said it would work. They never actually ran it on our documents.

How this differs from AI Feasibility & ROI

An AI Feasibility & ROI engagement is portfolio-level: it scores many use cases and produces an ROI case before any code is written. An AI Pilot is use-case-level: it takes one use case and proves (or disproves) it with working code on your data. If you're still deciding which problem to solve first, start with Feasibility & ROI. If you already know the problem, start here.

What happens next

About 80% of AI Pilots graduate into a full Custom AI Build plus Private AI Deployment. The remaining 20% end with a "no-go" recommendation — and that's the value: catching the dead-end at week 6 instead of month 6.