IntelafyIntelafy™MAKING DATA INTELLIGENT Request a demo
05 · For Enterprise Data & AI Leaders

AI built on measurement —
not training.

trillions of predictions from one engine. No GPU fleet, no retrain cycles, no calibration tax. The Brain measures association directly with closed-form statistics — same data in, same model out, every time — and hands your supervised models stable, comparable, pre-calibrated features they can actually rely on.

The scale Honest boundaries
Trillions
Predictions, one engine
0×
Smaller footprint (156 GB)
0
GPU clusters required
0
Retrain cycles between refreshes
The engineering argument

The flaw is in the approach, not the remediation.

A trained stack must hold every fact as dense numbers a GPU can process — 32 times the mass of the same facts held as bits. Everything downstream inherits the weight: the GPU fleet, the serving stack, the retrain cycles, the calibration jobs. No amount of training removes stochastic drift; no calibration puts two models on one scale; no dropped column stays dropped when proxies rebuild it. Those costs recur because the flaw is structural.

Trained stack — recurring costs

  • Stochastic drift: retraining reshuffles scores for unchanged individuals
  • Per-model calibration: scores across models aren't comparable
  • Proxy reconstruction: dropped protected columns come back
  • ~5 TB dense-float footprint + GPU serving stack
  • Explainability via post-hoc approximation (SHAP / LIME)

Measured engine — structural properties

  • Deterministic: identical inputs, identical scores, for the model's life
  • One universal 0-to-1 scale across all tens of thousands of attributes
  • Source-level exclusion — protected data physically absent
  • 156 GB packed-bit footprint on commodity CPUs
  • Explainability by construction — named evidence per score
Property 03 · The Survey Bridge

trillions of predictions. No per-attribute models.

The bridge maps a large syndicated survey onto all hundreds of millions of people. Replicating this with supervised ML would mean training tens of thousands of separate models on data-starved samples — each overfitting, each producing non-comparable scores. One measured engine reaches them all.

DATA FOOTPRINT Trained stack · ~5 TB dense 5 TB Measured engine · 156 GB packed 156 GB · 32× smaller
FIG 1Packed bits vs dense floats — the same facts, 32× less mass, on commodity CPUs.
Honest about boundaries

Complementary, not replacement.

Intelafy is built for audience inference on structured consumer data — where explainability, comparability, stability, and auditability decide outcomes. It is not built for raw text, image, or audio classification; real-time next-click session prediction; or massive-scale recommendation engines. Your neural models own those lanes. Intelafy consumes their outputs as first-class signals and maps them onto a stable, comparable, population-scale fabric.

Integration

Feed Intelafy's stable, comparable features into your supervised point-forecast models as pre-calibrated inputs — and drop the drift, calibration, and proxy costs downstream.

USE IT FOR / NOT FOR
Built forNot for
Population-scale audience inferenceRaw text/image/audio classification
Psychographic & behavioral scoringReal-time next-click prediction
Regulated, auditable decisioningSession-level recommendation
Comparable feature generationUnstructured sequence modeling

Stop maintaining tens of thousands of models.

Replace them with one measured engine — and hand your data scientists features that hold still. Pilot a dedicated build on your file.

Request a technical briefing Read: Scores You Can Compare