AI Advice From People Who Ship, Not Just Present

Maps your business processes, identifies where AI genuinely improves speed, cost, or quality, selects the right tools for each case, sequences builds by return on effort, and hands over an executable roadmap — including what to skip.

Price, speed, and skin in the game. Sprints run weeks, not quarters, at fixed fees, and because we also build, our recommendations carry implementation risk we'd have to eat ourselves — a firm that never ships can recommend anything without consequence.

No — the roadmap is deliberately written so you don't have to, specific enough for your team or any competent developer to execute. Around a third of consulting clients build fully in-house.

It's usually an asset. Skeptical teams ask the right questions and refuse toy projects — the actual failure mode is mandated enthusiasm with no metric attached, not healthy skepticism.

The sweet spot is businesses with roughly 10–500 people and real process volume — enough repetition for automation to matter, small enough that decisions happen quickly.

Sprints run as a fixed fee for a defined scope — no open-ended discovery retainers. The specific number depends on the size and complexity of the operation being mapped.

Typically three to four weeks from discovery through the decision workshop, not quarters — the entire point is a fast, fixed-scope sprint rather than an extended, open-ended engagement.

It's the set of AI ideas evaluated and explicitly rejected, with documented reasoning — half of the value of honest consulting is stopping a business from building things that won't pay off, not just recommending things that might.

Yes — a risk and governance review assessing data-handling exposure and relevant compliance considerations is built into the opportunity scoring for every candidate build, not addressed only after something ships.

This is common. The sprint typically reframes the question from "which tool" to "which process," identifying what's actually worth automating and whether the existing purchase should continue, be repurposed, or be cancelled.

By sequencing scored opportunities for early, visible, measurable wins — a follow-up automation or reporting agent, typically — because early proof buys the organizational patience deeper builds will need later.

Whatever fits the specific case — Claude, GPT models, Gemini, plus the automation layer connecting them to your systems — with no commission-driven bias, since we're not resellers for any single platform.

A working session with your leadership team to pressure-test the roadmap directly — challenges are welcomed, and every answer is documented, so the final plan has already survived scrutiny.

The sprint format is built for businesses with real process volume — solo founders usually benefit more from a lighter advisory conversation, which we're also equipped to have.

Larger enterprises with existing AI teams often need narrower, more specific service lines — automation or agent development — rather than a full opportunity-mapping sprint from scratch.

Each opportunity is attached to a specific number — hours saved, response time cut, revenue recovered — with the underlying assumptions shown explicitly, not hidden behind a confident summary figure.

That's a legitimate, useful outcome — knowing an operation isn't yet ready for a particular AI investment prevents wasted spend, and the assessment will say so directly rather than manufacturing a recommendation to justify the engagement.

Yes — it's written specifically to be executable by your team or any competent builder, not structured to require Optiv for implementation.

We'll say so directly if a problem is better solved by a process change or a hire than by an AI build — the roadmap follows the actual data, not a predetermined conclusion that AI is always the answer.

A mapped assessment naming the highest-leverage AI opportunity available in your operation, along with its expected payoff, delivered within 48 hours.