The whole education market, in one place you can finally ask.

Vertical Intelligence is the queryable evidence layer for every public school district in the country: what it teaches, whether its students actually improved, the budget a switch is fundable from, the accountability pressure it's under, when its buying window opens, and who signs. Every dataset is joined by the same district key and computed from government data the vendor can't edit. Ask it in plain English from any AI assistant, and every number comes back traceable to its public source. Watch it run the questions a curriculum provider, a district, a funder, and an investor actually ask.

Try asking

Vertical Intelligence via your AI assistant · VI MCP
Nebraska Reading · Grades 3–5
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Independent government data Interval, never a verdict Backtested out-of-sample No discretionary intake

Illustrative demo · Ohio incumbents, California and E-Rate funding are live PILLAR data; estimates are intervals, never verdicts. Hover to pause.

The question the market can't answer

Every curriculum provider is flying blind into a displacement.

A Math or ELA platform wants to unseat an incumbent. The one fact that would win the room, whether student outcomes actually moved under the program already in the building and who can fund a switch, sits scattered across dozens of public sources, never computed, and never in the room when the decision gets made. So the market rewards the familiar logo. Vertical Intelligence pulls that evidence together and serves it into the decision.

01 · The spine

Treatment × Outcome × Covariate

District adoptions, independent government outcomes, and the controls for a fair comparison, all linked by district.

02 · The engine

Matched-cohort inference

Difference-in-differences with a parallel-trends pre-test. Every result an interval with its confound flag, backtested out-of-sample.

03 · The tools

170+ MCP tools

The whole layer exposed as plain-English tools. No SQL, no dashboard to learn, no intake form to lobby.

04 · Your assistant

Answered in the workflow

Any AI assistant, whether Claude, ChatGPT, or an internal copilot, queries VI right where the decision gets made.

What Vertical Intelligence knows

Not just whether a program worked. The whole picture of every district.

Vertical Intelligence connects what each district teaches, how its students are doing, the budget it has to spend, and the rules it has to follow. Everything is linked district by district, so a single plain-English question can draw on all of it at once.

Outcomes

Independent results

SEDA plus a NAEP-anchored owned layer (r=0.94). A national outcome ruler the vendor can't edit.

Adoptions

What's actually taught

District-level curriculum choices, parsed to publisher and edition. Real adoptions, not a state menu.

Funding

Federal and state, to the school

Title I-A, state categoricals, and per-school LCFF S&C, SENI, and ESSA spend. The budget a switch is fundable from.

Accountability

CSI/TSI and assessment

ESSA designations and proficiency across 51 jurisdictions. The regulatory pressure and the academic gap.

Poverty

Need and demographics

SAIPE poverty, enrollment, FRPL, and neighborhood income. The controls that make a comparison fair.

Procurement

Buying windows

Fiscal-year calendars, budget-approval months, PO blackouts, and the RFP window per state.

Readiness

Buying-readiness scoring

Need, ability to pay, pressure, and timing, weighted for durability. Who is actionable now.

Contacts

Decision-makers

The people behind the district, pulled from official directories, so the signal reaches a human.

51 jurisdictions · every dataset linked by the NCES district identifier · governed by a Spec → Guarantee → Test framework so the method can't be quietly re-weighted.

Who asks Vertical Intelligence

One layer, four rooms.

Curriculum providers

Win the displacement

Lead a switch with evidence, not a deck, and walk in with the district's own fundable budget.

"Where has the incumbent failed to move outcomes, and who can pay to replace it?"

Funders & philanthropy

Fund the public good

Measure every program, including your own portfolio, by a method you don't control.

"Which interventions actually move outcomes for the students we fund?"

District & system leaders

Decide on evidence

Bring decision-point evidence computed from your peers' real outcomes to the adoption table.

"Did this program work for districts like mine, under our conditions?"

Investors & diligence

Underwrite the claim

Independently verify an EdTech efficacy claim against government outcome data.

"Does the efficacy story hold up on data the company can't edit?"

Why it holds up

Capture-resistant by construction, and it carries load today.

The outcome variable is computed from government data the vendor never touches. The method is written as test-enforced invariants, so re-weighting the rubric is a red build. There is no "submit your product" door to lobby. And it is real: the treatment-outcome-covariate spine runs across seven states on a national panel right now.

7
Treatment states
15,591
Outcome districts
51
Jurisdictions
170+
MCP tools
r=0.94
Owned layer vs SEDA
OR
NE
NM
OH
TN
MA
RI

The treatment spine: seven states.

District-level adoption data is public only where a state's own law or initiative created it. VI ingests it where it exists, on a national outcome ruler that covers every district in the country.

Named incumbents (OH, TN)Adoptions (NE, NM, OR, MA, RI)

Reward Follows Legibility

The full argument and the proof of existence behind this demo, in a response to AEI's The $30 Billion Question.

Read the response

Ask the education market what works.

See Vertical Intelligence run your category, your states, and your incumbents, in plain English on independent data.