What we do and how it works
Three services, each designed for a different point in a mid-market company’s AI journey. They can be bought separately or in sequence — a sensemaking engagement sometimes leads to pilot steering, but neither is a gate for the other. If you already know what you want to build, we can start there.
AI Sensemaking Engagement
Most AI decisions go wrong because no one pressure-tested them before the investment was committed. The AI Sensemaking Engagement is built for exactly that: not a vendor demo, not a generic AI maturity framework, but an honest read of what a specific AI decision means for your operations — a vendor pitch to evaluate, a use case to assess, a pilot to green-light or stop — with a clear recommendation on what to do.
What this is
An honest, structured read of what a specific AI decision means for your operations. We work through what the technology actually does, whether the evidence matches the claim, whether it fits your operation and your data, whether your operation is ready to take it on, and what you'd need to see before committing. Scoped with you before we start, so the work stays on the decision you actually need to make. The output is a recommendation you can act on, backed by a written assessment.
When to use it
Use it when you have an AI decision you can't yet judge: a vendor or colleague has pitched something and you need to know if it's real for you; a use case is on the table and you want an honest read before committing; a pilot is being proposed and you need to decide whether to green-light or stop it. You don't need a broad strategy review — you need a decision worth getting right.
What you get
A recommendation you can act on, backed by a written assessment covering: the decision being evaluated; what the technology really does versus what's claimed; how it fits your operation and your data; what you'd need to see before committing; and a clear recommendation, with the reasoning written out. Yours to act on, or to take to your board — and, if you decide to move, the foundation for a pilot steering engagement.
What it does not cover
ML model development, vendor selection as a pre-committed answer, a company-wide AI strategy or audit, or ongoing project management after the assessment is delivered.
You come away with a clear read of what this AI decision means for your operations — and a recommendation you can act on: move forward, wait for better evidence, or decide it isn't for you.
AI Pilot Steering
In most technology pilots, scope drifts once development starts. Governance erodes under operational pressure. Stakeholder communication fades as day-to-day priorities take over. Production-readiness criteria are set too late — or never. AI pilots inherit all of these. They also fail for a reason that is specific to AI adoption and often the decisive one — the human factor: process redesign, role shifts, building trust in algorithmic outputs, and training the people whose work the system changes. What we bring to a pilot is leadership discipline applied to both dimensions — the governance and the people-readiness work that determines whether a pilot survives contact with your operations.
What this is
Leadership for your AI pilot, from scoping through to production handoff. Whether the gap is governance structure, AI-experienced oversight on an initiative that already has a project manager, or a stalled pilot that needs to be unstuck, we work from where you are. We provide: project governance structure, vendor and tool evaluation support, pilot scope definition and success criteria, progress tracking and risk management, stakeholder communication, and production-readiness review. Your team or a technical partner handles model development and integration.
When to use it
You are running an AI pilot, planning one, or watching one stall. You have internal technical capability or a technical partner for the model work. You may have a project manager in place, or no dedicated leadership at all. What you need is AI-experienced leadership to govern the initiative from scoping to production without it derailing.
What you get
A running AI pilot in production, or a documented decision to stop with the reasons clearly stated. Includes project documentation, a governance framework, and a lessons-learned report. The engagement runs through production handoff — what comes after is yours to operate, or a separate engagement to scope.
What it does not cover
Model development and integration (handled by your team or a third-party technical partner). Ongoing system operations after handoff.
Your AI pilot reaches production — or you get a documented, clearly-reasoned decision to stop before spending more.
AI Automation Sprint
For a small, well-scoped automation — a document-processing pipeline, an internal knowledge chatbot, a workflow trigger — the Automation Sprint takes it from business case to a validated, working build in 2–4 weeks: you see it run against your own data, solving your problem. The build is yours — documented and explained — for your team or a technical partner to harden and take to production. You know it works before you commit to operating it.
What this is
A 2–4 week engagement where we scope the business case and build a validated, working automation using AI-assisted development — applying pre-built AI capabilities (LLM APIs, automation platforms, low-code AI tools) to a specific, bounded business problem. Not ML engineering from scratch. The build runs against your actual data and is fully documented. Your team or a technical partner then hardens, secures, and deploys it to production.
When to use it
You have a specific, bounded automation in mind. The scope fits what one person can build in 2–4 weeks. You want a validated, working build you can hand to your team or a technical partner — not a plan for a larger project. You want to know it works before you commit to the full cost of production operations.
What you get
A validated, working build you have seen run against your own data. Includes: documentation of what was built, a deployment guide covering what your team or a technical partner needs to take it to production, and a productionization roadmap covering hardening, security, and monitoring considerations. You own the output entirely once the engagement is complete — fully documented and explained for whoever takes it to production.
What it does not cover
ML engineering from scratch. Production deployment, ongoing hosting, or post-handoff operations — the build is validated and documented; production stays with your team or a technical partner. Multi-month builds — if the scope is larger, the right path is Pilot Steering with a technical partner. Client-side infrastructure changes.
You see the automation work against your own data before you commit to the cost of running it in production.
The right next step
If the right service is not yet clear, the discovery call is the right next step. We will tell you honestly whether any of the three fits your situation — and if none does, we will say so.
Book a discovery call