Our Technology

Inside our own engine.

Raw data goes in — from a file, a database, or a cloud store. Refined, ML-ready output comes out, forged through 25 deterministic steps. Watch each one work, in order, and pause on any step you want to read properly.

Phase I · Foundation  ·  Step 00 / 24
session
A named, timestamped workspace is created.
Inside the engine

Four phases, twenty-five modular stages.

I
Foundation
Steps 0–7
II
Strategic Insight
Steps 8–10
III
Core Transformation
Steps 11–18
IV
The Final Forging
Steps 19–24
Inside the engine

Four phases, twenty-five modular stages.

Every stage is an independent building block with its own safeguards and its own checkpointed result, and ready-to-open exports land at six points across the run — the timeline above is these 25 steps, running in order, on a loop. Use Pause and the step arrows to stop on any one of them and read it.

I
Foundation
Session, load, de-duplication, schema & correlation — a clean, understood starting point.
II
Strategic Insight
Causal inference, outliers & automatic target discovery — the engine learns what your data is about.
III
Core Transformation
Imputation, outlier handling, text & time-series features, generation, unification & evaluation — the work that decides quality.
IV
The Final Forging
Split, dimensionality reduction, balancing & held-out evaluation — ML-ready, measured, done.
Auto or manual

Same engine. You choose who signs off.

One choice, made once at the start — and honoured to the last step. Watch the same run in both modes.

Step 11 · impute numeric
Which fill strategy for age?
18.4% missing · skew 1.83 · normality rejected · strong dependency on 3 other columns
Nearest-neighbourengine recommends Median Mean Iterative regression
In manual, your answer is what the step applies — and what its audit record stores.
Auto — the engine applies its own measured recommendation and keeps going.
Auto
Runs unattended: at every judgement call the engine applies its own measured recommendation. Column removal and renaming are deliberate pass-throughs — nothing is dropped or renamed merely because nobody was there to answer.
Manual means manual
Identical engine, identical measurements — but it stops where a human answer genuinely means something. It cannot proceed with a column left undecided, your answer is checked against the options that step really supports, and what you were shown, what the audit record stores and what gets applied are the same value.
No undo? Gated harder
Destructive choices need an exact re-type — the column's own name, or DROP 1284 ROWS once the magnitude turns severe, precisely so it can't be typed by reflex. Anything that isn't an exact match means nothing destructive happens. Identifier columns are hard-blocked from removal outright — no override path exists.
Modular by design

25 tools, one price.

The 25 steps are independent, modular building blocks, not a black-box monolith. Each one is substantial enough to be a product in its own right:

Duplicate resolution Schema conformance Causal inference Outlier handling Imputation Text feature extraction Time-series feature extraction Leakage screening Dimensionality reduction Class balancing

Every one of those is a tool people buy, build teams around, or take a course in, on its own. Every stage here stands alone — its own safeguards, its own decisions, its own independently checkpointed result — and you get all 25 of them in one application, for one price.

Why it's built this way

Modular, deterministic, and yours.

Deterministic by construction
The same engine runs the same way every time, with a fallback at every step — reproducible, not probabilistic.
Private by design
We never collect, upload, or store your data — not a row, not a column name.
Six checkpoints, not one finish line
Ready-to-open exports land at six points across the run, so you can start working before the last step finishes — each one independently checkpointed.

See the engine run on your own data.

Every feature, all 25 steps, on one capped dataset — free.

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Obsith
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