Under The Hood
In Your Hands
The Big Picture
Live Demo
90 seconds. A complete competency portrait.
Watch the engine score a student response in real time, with live signals.
See It Measure →
The Solutions
The Methods
The Foundations
Selected by California
Chosen To Design The State's Behavioral Health Performance Management System.
The BHSSA award — durable-skill measurement at state scale.
See The Work →
The Case
The Science
The Proof
The Proof
Real districts, real data, real names.
What partner districts saw in year one — in their own words.
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The Arc
The People
The Promise
From the Founders
We built this because the tools didn't exist.
A note on why two educators chose to build a measurement company.
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The Linguistic Fingerprint

Durable Skills
Leave A Fingerprint
In How Learners Speak

Our ML models don't look for the right answer. They look for the linguistic patterns that correlate with Grit, Growth Mindset, Curiosity, and Compassion — patterns no one can fake.

Explore The Patterns →
Pattern Families

Five Families Of Signal, One Authentic Voice

When learners narrate their own growth, the structure of their language carries measurable signal. Click any pattern to see what gets detected.

Select A Pattern Family
Correlates With
Language Mapped In Multidimensional Vector Space
The Architecture Beneath

How Each Attribute Is Read

IMPACTER's ML pipeline doesn't grade how well students speak. It measures the invisible architecture of growth: how learners organize time, link causes, and connect with others. Select an attribute to see its linguistic features highlighted inside real student voice.

From Voice To Insight

How Language Becomes Data

Every response climbs the same pipeline — from raw capture to insight an educator can act on. Hover a layer to follow the journey.

1 Raw Data 2 Structured Data 3 Processed Data 4 Actionable Data
Raw Voice → Actionable Data
Technical Validation

Readings You Can Defend

The fingerprint isn't a metaphor we hope holds up — it's a measurement model tested with multitrait-multimethod analysis and confirmatory factor analysis.

98%
Adjacent Accuracy
63%
Trait Variance
6%
Method Variance
.962
CFI
.041
RMSEA

Scores reflect the trait being measured — not the method measuring it. Trait variance (63%) dwarfs method variance (6%), and model fit (CFI .962, RMSEA .041) meets conventional thresholds for strong construct separation.

Go Deeper
The ML Model Card
The model that reads the fingerprint — architecture, training corpus, rater calibration, and per-competency agreement, all in the open.
Read The Model Card →
Go Deeper
Construct Validity
Why the readings are valid — how rubrics define skills, how skills define scores, and why these scores mean what we say they mean.
See The Validity Evidence →