You don't always need a new system. Sometimes you have a specific, high-burden problem that needs a targeted, permanent fix — executed under human supervision, measured on results, closed when the problem is gone.
A walkthrough of what AI Solvers are, how they're scoped and built, and how three organizations permanently eliminated their most costly operational bottlenecks.
They're not always broken processes. They're known constraints — manually intensive, error-prone, too costly to ignore and too complex to fix without the right tools. Your best people are stuck inside them.
You don't need a new platform.
You don't need a multi-year transformation.
You need a precise solution — for the specific problem that's costing you the most.
An AI Solver is a custom-built system designed to permanently resolve a single, high-burden problem — using AI, automation, and data integration targeted precisely at the constraint.
The defining feature isn't the technology. It's the scope. We start with a problem that is specific, measurable, and causing real operational pain. We build exactly what's needed to eliminate it.
And it's not a months-long project. We assemble proven, off-the-shelf tools configured precisely around your constraint. We run the solution once, with human eyes on the execution. No need to engineer for future scenarios or build interfaces. We fix your problem and move on — which is exactly why it's so fast.
No bloated platforms. No multi-year roadmaps. A defined engagement and a defined outcome.
Three different industries. Three specific problems. Three permanent fixes.
A manufacturer acquired a new aircraft program — 100,000 parts with descriptions, zero manufacturing classifications. Routing was blocked. A team spent two months and covered only 10,000 parts. At that rate: a full year before operations could move.
90,000 parts classified with production-grade accuracy. Custom AI classification engine scoped, tested, validated, and executed in a single sprint.
Deep Dive - Coming Soon ↗A clinic network needed patients activated on their portal. Each activation required outreach, guided profile completion, and EMR activation — manual, slow, inconsistent. Adoption had plateaued at 33.9% with no clear path forward.
Portal adoption in 7 weeks. Automated patient outreach, guided onboarding, and EMR activation — end-to-end, without adding headcount.
Deep Dive - Coming Soon ↗A multi-site HVAC manufacturer needed 40 leadership KPIs documented across 7 departments and 3 manufacturing sites. Each site calculated metrics differently — or not at all. Leadership had no consistent baseline to manage from.
40 KPIs standardized across 7 departments and 3 manufacturing sites — calculation gaps identified, inconsistencies surfaced, single methodology defined and documented.
Deep Dive - Coming Soon ↗We don't start with technology. We start with the constraint. Every Solver moves through four stages — from bottleneck diagnosis to documented close.
Define the problem precisely — its frequency, cost in time and money, and what a solved state looks like. If it can't be measured, the scope isn't ready.
Architect a targeted system using the right combination of AI models, automation logic, data integration, and human review checkpoints — nothing more.
Before touching real data, we run pilots in a test environment and iterate until the output meets our quality bar. Only then do we execute on the actual problem — with human eyes on every step throughout.
We validate results against the original constraint, document what was done, and close the engagement. There's nothing to maintain or operate — the problem no longer exists.
Most deployments see payback within days to weeks — not quarters — because the problem is defined before we build. No discovery drift. No scope creep. You know what you're solving and what it's worth to fix it.
Both are part of the Sterling North approach — but they serve different needs. Understanding which one fits your situation is where every engagement starts.
| ✦ AI Solvers | AI Workers | |
|---|---|---|
| Scope | One defined problem or bottleneck | Sustained operational role across a function |
| Engagement | Bounded — defined outcome, clean close | Ongoing — continuous digital capacity |
| Deliverable | A problem that no longer exists | A digital team member embedded in your org chart |
| Time to Value | Days to weeks | Weeks to months, ongoing thereafter |
| Ideal When | The problem is known and the cost is clear | Repetitive execution workloads fill your team's capacity |
| Org Impact | Eliminates a specific burden permanently | Scales capacity without adding headcount |
Every Solver includes structured accountability from day one — more traceable than most manual processes. You always know what ran, what decided, and what the output was.
Every action is logged — what ran, when, on what data, and what the outcome was. Every decision is reviewable at any time.
Human-in-the-loop checkpoints and escalation rules are built into the architecture — not added as an afterthought.
Every run is documented — what was processed, what decisions were made, and what the output was. A clear record of how the problem was solved.
Defined inputs, outputs, and escalation rules. The Solver does what it was built to do — nothing outside that boundary runs without oversight.
I've spent 15 years at the intersection of operations, AI, and execution. I led AI transformation work at McKinsey before it was mainstream, then built and operated automated production workflows as the founder of a VC-backed network of tech-enabled medical clinics. As a partner at Cylad Consulting, I specialized in delivering AI-enabled solutions inside complex organizations. Sterling North is where all of that converges.
Describe your bottleneck below. Philippe will review it personally and tell you whether it's solvable as a Solver, what the approach would look like, and roughly how long it would take. No pitch. No commitment.
No spam. No automated responses. Philippe reads every submission personally and responds within 2 business days.
