About

Let AUTOMATA write your reports — so you don't have to.

Valentine Labs is the company. AUTOMATA is the product. It started when one school psychologist got bored of report writing, realized it was not what he wanted to spend his time on, and taught himself — spreadsheet, then formulas, then code, then apps — to make the computer do it. It is still tested on real reports before anything ships.

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Valentine Labs

What we believe about a technical report.

Valentine Labs Valentine Labs makes AUTOMATA
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The thinking is the job. The typing isn't.

Interpreting a profile, weighing it against history and observation, deciding what it means for a student — that is professional judgment and it stays with the professional. Moving numbers from a score report into a table and from the table into sentences is not judgment. It is transcription, and transcription is where errors live.

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A report is a legal document.

People sign their names to these. Eligibility decisions, services, and sometimes litigation rest on them. So a printed percentile from the publisher always beats a computed one, a descriptor always comes from the publisher's own band table, and a missing value is left blank and flagged rather than filled with something plausible.

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Nothing runs without a yes.

Every automated check produces a proposal, not an action. You see what would change and approve it or reject it. Identifying data stays in your files. The system drafts on de-identified records and the report is re-assembled on your side.

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Founder

Kevin Valentine

School psychologist. PhD. Builder. He writes the reports this product was built for — and he got bored enough of writing them to build the thing that writes them for him.

PracticeSchool psychologist, Mira Monte High School, Kern High School District — special education assessment and eligibility
CredentialCalifornia Pupil Personnel Services (PPS) credential
EducationPhD, National University, JFK School of Psychology and Social Sciences — research on face perception and the cross-race effect

Kevin Valentine is a practicing school psychologist in California's Kern High School District, where his caseload runs the full range of special-education evaluation: initial assessments, triennials, re-evaluations, and the eligibility meetings that follow. Every one of those ends in a report, and every report is built from the same raw material — score PDFs from a dozen publishers, rating scales, prior evaluations, and records — retyped, by hand, into tables and prose.

Somewhere in the middle of yet another triennial, he got bored. Not of the students, and not of the assessment — of the report writing. Retyping scores that were already printed on a PDF, rebuilding the same tables, re-explaining the same descriptors. He realized it was not what he wanted to be spending his time doing, and decided to make the computer do it instead.

He started with a spreadsheet and learned as many shortcuts as he could. Formulas came first: descriptor bands, so a standard score could never be labeled with the wrong range; percentiles, with the rule that the publisher's printed value always wins. Then the formulas were not enough, so he learned to write code — extractors that read the score reports directly, so nothing was ever typed twice. Then the code was not enough, so he learned to build apps. The result was tested the only way that matters — on his own caseload, on reports that were going to be signed and filed — and it became AUTOMATA, Valentine Labs' first product.

His PhD research at National University's JFK School of Psychology and Social Sciences was a quantitative study of face perception and the cross-race effect: how accurately people recognize faces from outside their own group, and what predicts it. It is a different subject from report writing, but the same discipline — measurement, error, and the difference between what a number says and what a person concludes from it — runs through both.

Why the "A"

The mark is an A drawn as a pen nib and a circuit trace: A for AUTOMATA, and the two things it joins together — the practitioner's hand and the machine that does the typing.

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How it got here

From one caseload to a company.

BoredomOne more report, one more evening retyping numbers that were already on a PDF. The realization that this was not what the job should be.
The spreadsheetEvery shortcut he could learn. Descriptor bands and percentile formulas for every instrument on the caseload, checked against the publishers' own tables, with a self-test that fails loudly when a band drifts.
CodeWhen formulas ran out, code. Score reports from WISC-V, WJ IV, WRAML-3, PH-3 and others read directly from the publisher's PDF into one structured data sheet, with the page each number came from.
AppsWhen code ran out, an app. Narrative, tables and score graphs composed from the data sheet against the practitioner's own template, every data point discussed in light of the suspected disability, with score-specific recommendations tied to eligibility.
The verifierEvery claim checked before the practitioner sees it. Mismatches flagged, never fixed silently. Nothing overwrites a report without a yes.
AUTOMATA, by Valentine LabsThe same pipeline, productized and offered to other psychologists, clinicians and evaluators — and next, to engineering, compliance, laboratory and research reporting, where the problem is identical.
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