It is arrested at the boundary between what engineering can build and what a risk committee will approve.
IDC finds roughly 88 percent of enterprise AI agent proofs-of-concept never reach broad production. RAND put the value-realization failure rate above 80 percent across more than 2,400 initiatives. The consistent finding is that the binding constraint is governance, operating model and infrastructure readiness — not the model.
Firm Mandate
Four functions must agree before an AI system moves from pilot to production in a bank, a hospital network, or a federal department: infrastructure, data science, risk frameworks, and business analysis. Most firms hold two of the four. The Big Four and strategy houses hold risk and business analysis but subcontract the engineering. The hyperscaler and integrator channel holds infrastructure and data science but treats governance as documentation produced after the build.
Neither can stand behind an end-to-end assertion. We hold all four, which is why we can make a claim neither can: that a single control identifier traces from your board’s risk appetite statement down to the compute partition a model executes on, and produces audit evidence on the way back up.
That claim is testable. You can ask us to demonstrate the trace on one of your own models in a first meeting. We would encourage you to ask the same of whoever advises you today.
The engagement is deliberately narrow and fixed in scope because open-ended advisory produces open-ended invoices and inconclusive answers.
Core Practice Disciplines
Enterprise AI failures rarely respect organizational boundaries. Our teams are staffed so no finding gets lost in the space between infrastructure, data, risk and finance.
Practitioners who have specified, procured and operated high-density GPU estates. Power, not silicon, is the binding constraint now — and almost no consultancy staffs for the conversation with the head of corporate real estate.
Engineers who run production model estates: retrieval architecture, embedding hygiene, evaluation harnesses, and the drift instrumentation that catches degradation before your users do.
Second-line practitioners fluent in the frameworks that actually get examined — and in the evidentiary standard a supervisor expects when they ask you to demonstrate control effectiveness.
Analysts who translate architecture into capital planning language: TCO decomposition, token economics, benefit realization baselines, and the board narrative that holds up under scrutiny.
What We Publicly Decline
Stating this plainly costs us a category of work that destroys margin and dilutes method. We would rather you know before the first call.
No time-and-materials bodies against a client backlog. It destroys the method and turns a practice into a rate card.
No hardware, license, or model margin. Independence is the product — the moment we take vendor margin, our capacity model stops being evidence.
Every proof-of-concept we scope has a written kill condition agreed before kickoff.
Where your governance regime requires independence between builder and assurer, we take one role and say so in writing. This costs us deals and wins us the regulated ones.
Confidentiality Posture
Assessment work requires access to your most sensitive technical estate. These commitments are written into every statement of work, not published as aspiration.
Working copies of any artifact containing client information are destroyed within 30 days of engagement close, with written destruction attestation issued to your sponsor.
Wherever technically feasible, analysis runs inside your tenancy on read-only credentials. Where extraction is unavoidable, scope and duration are agreed in writing first.
Client data is never used to train, fine-tune, or evaluate any Aegis model or tooling, and is never submitted to a third-party provider without documented authorization.
We maintain a conflicts register from day one and declare our role — first-line engineering or second-line assurance — per account, in writing. Institutions ask to see it.
