Every organisation is buying AI. Almost none of them can name who will actually carry it.

AI budgets are being approved faster than AI capability is being built. The spend lands on licences and tooling; the failure lands on people. A rollout stalls not because the software is wrong but because nobody knew, before the money moved, which employees would drive it, which would quietly ignore it, and which would raise the objection that turns out to be correct.

Leaders currently answer that question with intuition, a show of hands, or a usage dashboard that only reports the past. There is no standard, portable measure of AI-adoption work style — the way there is for personality, engagement or aptitude. That absence is the market.

AIRE scores four dimensions — Awareness, Initiative, Rigor, Execution — using a 41-item forced-choice and Likert design with attention and consistency checks. The dimension pattern resolves to one of nine AIRE types, from champions who pull a team forward to verifiers whose scepticism is the reason a rollout does not blow up in audit.

The interpretive layer is the differentiator. The same four scores read differently for a superintendent, a charge nurse and a dean, so every profile is written per industry and per role: what this person should be given, what to stop asking of them, and a 30/60/90 plan. Design lineage and citations are published openly at About & Methodology, with intended-use limits at Fair use.

This is not a deck-stage concept. The assessment, payment, results, employer console, faculty console, cohort reporting, PDF and share output, agent-facing API and admin tooling are all shipped and running in production. Early usage, as of August 19, 2026, spans 120 invited people, 81 completed assessments, 20 organisations and 13 faculty accounts. Treat those as build-and-pilot volume, not commercial traction — a portion is internal and design-partner activity, and we will not dress it up as anything else. Completed assessments are not paying customers: the homepage counter shows all-time sittings including free and internal pilot activity, while paid conversions are reported separately. Verified revenue detail is in the data room.

The strategic point is that these are not three products. One scoring engine, one content library, one report assembler. A new vertical is content, not engineering — which is why 15 of them exist already.

AIRE sits at the intersection of three established budgets, all of which are being reallocated toward AI capability right now.

• Corporate training and development (L&D). A mature global spend measured in the hundreds of billions annually, with AI-skills programmes now the fastest-moving line inside it (Training Industry; LinkedIn Workplace Learning Report).
• Talent assessment. A multi-billion-dollar category built on exactly this motion — a validated instrument, sold per person, renewed on a cycle (SHL, Hogan, Gallup, Predictive Index as the reference set).
• Enterprise AI adoption spend. Global AI investment at hyperscale, with widely reported failure rates on transformation programmes traced to people and change management rather than technology (McKinsey; Boston Consulting Group's (BCG) often-cited finding that roughly 70 % of the difficulty is people and process, 20 % technology, 10 % algorithms).

Bottom-up is the sharper way to see it. Assessment is per person, and the population is every knowledge and frontline worker in a market that already buys pre-hire testing. A single mid-size employer is a few-thousand-dollar first order and a recurring one, because re-measurement after training is the point of the instrument, not an upsell bolted onto it. A university department is a per-student order every term. Sources and the full sizing build-up are in the data room.

• Re-measurement is native. A profile is a snapshot of a moving thing. Organisations that train against a baseline come back to measure the delta, and the product tracks rounds per person by design.
• Content depth is the moat. Nine AIRE types are easy to copy. Nine AIRE types written correctly for 208 roles across 16 industries, with 30/60/90 plans that read as though written by someone who has stood on that job site, is years of work.
• Data flywheel. Every completion improves norms, and vertical norms are the thing an enterprise buyer eventually pays for — benchmarking against their industry, not against a generic population.
• Distribution through institutions. Students assessed in a classroom carry the vocabulary into employers who then buy it. Faculty seats are cheap customer acquisition for the enterprise motion.
• Agent-ready. The platform exposes an authenticated MCP server and public agent endpoints, so AI assistants can trigger and read assessments directly — a distribution channel most incumbents cannot serve.
• IP. Patent pending on the method; the instrument, AIRE type system and content library are protected works. See Patents.

• Norms and validation. Publish vertical norm tables and complete the criterion-validity work that converts AIRE from a well-designed instrument into a cited one. This is the single highest-value unlock for enterprise procurement.
• Enterprise readiness. SSO, HRIS and ATS integration, admin hierarchy for multi-site organisations, and procurement-grade security review.
• Training marketplace. The report names the gap; the obvious next dollar is the targeted intervention that closes it, then re-measurement to prove it closed.
• Verticals and languages. New industries are content work, and localisation opens the same product to non-English workforces.
• Channel. Consultancies, staffing firms and change-management practices resell an instrument they currently improvise.

Validation. Independent criterion validity is not yet published. Until it is, sophisticated enterprise buyers will discount the instrument, and we would rather you price that in than discover it.

Regulatory. Any hiring use falls under employment-law scrutiny including NYC Local Law 144 and comparable rules. Our stated position is that AIRE is a development and deployment instrument, not a stand-alone selection screen, and the product says so to every employer.

Incumbents. Established assessment vendors have distribution and could add an AI-readiness module. Our answer is depth and speed — role-level content in 15 verticals, and an agent-native surface they are structurally slow to build.

Concentration and stage. Early revenue is small and pilot-weighted. We are not presenting it as a growth curve.

Take the assessment before you talk to us — it is free, it takes eight minutes, and the product argument is easier to make from inside it. Then look at a sample team report and the vertical library.

The data room — financials, unit economics, cohort retention, pricing, cap table, IP filings and the sizing build-up with sources — is shared under NDA on request.

This page is an overview of a business and its market. It is not an offer to sell or a solicitation of an offer to buy any security, and it contains forward-looking statements that may prove wrong.