Is your organization getting smarter — provably?
You have BI — it tells you what happened. You've bought copilots — they help individuals finish a task faster. Consultants answered a version of the question once, in a deck nobody reopens. Each owns a fragment, then stops. What no category has built you is the layer where the organization itself decides, learns, and gets more coherent over time — the question no budget line owns. You already own the substrate; you enter at the top.
Start the conversation ↓Trusted where the work is operational, not theoretical:
Intelligence became abundant. The bottleneck moved.
For a decade the scarce thing was machine intelligence. That era is over — it's embedded in every workflow your teams touch. The machine side is programmable and already inside your stack; the human side — how the organization frames, decides, and follows through — has no infrastructure at all. That asymmetry is where the advantage now lives, and AI doesn't fix it: it amplifies weak judgment at machine speed. A misframed problem becomes scaled, expensive, wasted execution. 4× faster is not 4× smarter. Three initiatives in, most enterprises have demos and decks and nothing durable — nobody checked the data, an agent was sold where plain software would do, the reasoning was a black box, the loop was never closed, nothing was owned. We do the deliberate opposite of each. Credibility, in a boardroom that has seen three failures, is what we decline to build.
See the pilot shapeWhere this is all going →Ninety days to a provable answer.
Book a 30-minute call — mostly us asking. We'll find the one decision family where frequency times consequence is highest, and whether a fixed-fee pilot can prove the layer for you.
A defined pilot — criteria written before the work starts.
The engagement is not open-ended. It is one decision family — the one where frequency times consequence is highest — in one division, over ninety days, with the success criteria agreed and signed before anyone touches a model. Fixed fee. Here is the shape.
You keep the keys from day one.
Documentation, run-books, admin access, and contractual exit rights are in the first contract — not a reward for renewing.
Miss the bar, and you owe nothing beyond the fixed fee — and you keep everything we built. We would rather deliver a clean “no” in ninety days than a two-year programme nobody will ever cancel.
What it looks like once it's a standing capability
Entry at the top: a function head owns a problem no vendor category answers — spot-quote pricing, one division, ninety days, inside their own tenant. Quote turnaround falls from hours to minutes; realized margin rises because pricers stop discounting into uncertainty; the divergence data reveals two branches systematically underpricing one lane. A routed model is swapped in a day after a price change — proving the architecture better than any slide. By year two, a decision fabric spans three decision families, the thirty-year veteran retires as an event rather than an emergency, and the keys — held from day one — are why the relationship expands instead of getting re-procured.
The systems, one at a time — decisions, and the people who make them.
The Substrate Check
Two weeks confirming your data can actually carry a decision system — records joined, outcomes linked back. The unglamorous step every failed AI initiative skipped.
The Ramp
New hires productive in weeks — and you know it, instead of hoping it. The role's knowledge structured on day one, understanding actually measured, and the gap between how the newcomer and the manager see the job surfaced in week one, not month three.
Evidence-Based Calibration
Reviews that run on commitments kept, not narratives told — the quiet performer finally visible, the eloquent no longer outrunning the effective. Everyone sees their own read; development routes into learning, not a drawer. It runs alongside your existing cycle first, and replaces it only when it wins.
Goal-to-Execution Coherence
The strategy was agreed in the boardroom — is it what's actually being built? Trace the chain continuously: how teams read the goals, whether commitments follow through, whether the work maps back. Drift surfaces in week two, not at the quarterly close.
Diagnosis, Never Surveillance
The people systems analyze work and workflows — never how “intelligent” a person is. Every read belongs to the person as much as to the organization. That reciprocity is the licence to operate, and we treat it as non-negotiable.
The Cadence
A monthly decision review per division — run by your people, on your data — and a quarterly cognition report to the executive: is the organization deciding better than last quarter, with numbers? The cadence is the product.
Most of it isn't an agent.
Most of the machinery is retrieval, rules, disciplined model use, and a qualified human on every consequential call. Agents appear only where multi-step work genuinely earns them — with evaluation and fallbacks. We say so plainly, in the boardroom.
Reasoning made visible.
No black boxes. Every recommendation shows its evidence and its logic; every automated step is on the record. When it's retrieval, rules, and disciplined model use — which is most of the time — we say so.
Sovereign by design.
Deployed inside your boundary. Identifiable data stays on models you control; only anonymized hard reasoning may call frontier models, under terms your legal team actually likes. Model choices are policies, not marriages — a provider swap is a day, not a procurement cycle.
Observe. Diagnose.
Improve.
One control plane for every AI call — inside your boundary.
Meld, In Your Tenant
The control plane every AI request flows through — tagged to the division, the person, the application, and the task before it reaches a model, with one dashboard on top. It does the unglamorous thing first: it explains the AI bill.
Routing By Policy
Identifiable data stays in-boundary on models you control; only anonymized hard reasoning may call frontier models, under terms your legal team actually likes. Budgets per division. The right AI for each task — chosen by the task, not the job title.
The Eval Practice
Test sets built from your own documents, so every model choice is evidence. Adopt what the frontier ships next quarter as a decision, not a project — model choices are policies, not marriages.
The Constellation
We reach for a product only when the need is real — ProductLens on the Recon engine, CanonStack, Docent, CogSi, PredInt — each standing on the substrate the last left, built in the YE Stack ecosystem and owned by you.
Your tenant. Your policies. Your exit rights — from contract one.
Start the conversation →Data Residency
Everything runs in your tenant, in your region, under your governance. Identifiable data never leaves your boundary — and deployments are shaped to the compliance regimes you answer to, with the audit trail to show it.
Role-Based Control
Who can configure, who can review, who can change the routing — controlled granularly, audited completely, with evidence trails built alongside our audit partner, FactumOS.
Model As Policy
Model choices are policies, not marriages. A provider swap — or a better, cheaper model — is a day's decision, proven on your own eval sets. Agents appear only where they genuinely earn it, with evals, fallbacks, and a human on every consequential output.
The Keys, Always
Documentation, run-books, admin access, and contractual exit rights. Your team co-builds from the second decision family onward — you could run it without us, which is why the relationship expands instead of getting re-procured.
Fixed-fee to prove it. Expand only on evidence.
The first ninety days are a fixed fee against pre-agreed criteria. Clear the bar and you scope the next decision family; miss it and you owe nothing beyond that fee — and you keep everything built. Because you already own the substrate, there is little platform cost: the value concentrates in the cognition layer, and it grows by climbing, not by re-procuring a platform.
Often the honest answer is “you don't need an agent here.”
That answer costs us nothing and is worth more than any upsell — it's why the recommendations you get from us are clean.
Value you can point at
Decisions that stop leaking margin — pricers stop discounting into uncertainty, commitments get made on today's facts. Know-how that stops walking out the door. Ramp time that halves, and is measured. Reviews that stop being theatre.
An AI bill that re-baselines
One control plane means a spend-to-value line finance can actually read. High usage is investigated in context, never auto-branded as waste — and the smaller, cheaper model gets recommended whenever it's enough.
Time-to-keys
How fast could you run it without us?
Exit-ability
Could you leave this quarter?
Capability per token
Value per unit of AI spend.
The cognition report
Are you deciding better than last quarter?
A firm whose model only works if you end up capable.
What we do doesn't sit on an existing shelf, so it's easiest to place by contrast. Every category around us has a business model that quietly needs you to stay dependent. Ours is the opposite — we operate only until handover, and we design every engagement to be exit-able, on purpose.
Every firm above needs the client to keep needing them. Ours only works if you don't stay incapable.
A straight conversation — not a demo, not a pitch.
Thirty minutes, mostly us asking. Even if we never work together, you'll leave with a sharper picture of where your organization's judgment is leaking — and what a first, low-risk step would actually cost.
Straight answers.
10 questionsThink we can help?
Tell us which decisions vary most across your organization, or which people system has the sharpest felt pain. We'll have a straight conversation about whether a ninety-day pilot can prove the layer — and say so on the call if you're not ready for it yet.