OreML — The New Gold Standard for Tabular Data Preprocessing

Why OreML

It makes a data scientist's decisions for you — automatically.

1Zero-code, expert-guided

No code, no environment to assemble, no guesswork — OreML replicates a senior data scientist's decisions and guides you step by step, explaining each decision as it is made rather than after the fact.

2Your data stays yours

We never collect, upload, or store your data — not a cell, not a column name. Built for the privacy-critical: legal, healthcare, finance, defence. The licence does check in: OreML reaches our licence server when you activate and at least weekly after that, and keeps working offline for up to seven days at a time.

3One price, everything included

No tiers, no add-ons, no per-run charges, no compute credits — all state-of-the-art features in one price.

4Adaptive hybrid engine

A real hybrid system that adapts to both your hardware and your dataset size — it measures CPU-vs-GPU performance directly rather than guessing, and runs with an NVIDIA GPU (tested) or no GPU at all, on Windows or Linux.

5Analysis- & ML-ready before the end

Ready-to-use exports at six checkpoints across the run, not only at the finish. And the techniques that can quietly cost you accuracy are measured, not assumed: dimensionality reduction must beat your untouched features in paired cross-validation or it is rejected outright, and class balancing is judged before-and-after by an independent model that tells you in writing not to balance when it hurt.

6Built-in bias mitigation

Proactive safeguards against the statistical traps that quietly corrupt a dataset — leakage, identifier targets, trivial correlations, imbalance — measured and reported rather than assumed.

7Full process transparency — real-time log

No black box. A real-time log narrates exactly what OreML is doing to your data at every one of the 25 steps as it happens — complete visibility from raw data to finished output.

Deterministic, not "agentic"

AI agents still can't be trusted with your data prep.

The 2026 evidence is blunt:

LLM agents score under 20% on data-engineering tasks — DAComp benchmark (ICLR 2026, peer-reviewed); the best frontier model of the day topped out at 20.0%; data-analysis tasks under 40%.
Real-world text-to-SQL fails 80–85% of the time — "LLMs are probabilistic; databases require deterministic logic."
Non-determinism is irremovable even at temperature 0 — production multi-turn success can collapse to ~35% even at 95% benchmark accuracy.
68% of practitioners cap agents at ≤10 autonomous steps over reliability (306 practitioners, 2025).
Python is ~58% and rising (biggest single-year jump ever); 80% of data developers use pandas.

"AI agents hallucinate your pipeline. OreML runs it — the same way, every time, with fallbacks at every step. Reproducible by construction."

How OreML compares

Enterprise-grade depth, at an individual price.

With your data kept entirely private.

What matters to the buyer	OreML	Enterprise Cloud AutoML	Autonomous Data-Eng Platform	Cloud Notebook / EDA	Warehouse-Embedded ML	OSS AutoML Library	Spreadsheet
Zero-code, expert-guided	✓	○	○	✗code	✗SQL	✗code	○manual
We never receive or store your data	✓	✗	✗	✗	✗	✓	✓
No vendor holds a copy of your data	✓	✗	✗	✗	✗	✓	○
No setup — nothing to assemble	✓	—SaaS	—	—	—	✗	✓
Full 25-step prep → train in one tool	✓	○assembly	○	✗	✗	✗modeling only	✗
GPU-accelerated (NVIDIA, measured) — or CPU-only	✓	○cloud CUDA	○	✗	○managed	✗	✗
Built-in bias safeguards	✓	○	✗	✗	✗	✗	✗
Deterministic & reproducible (not LLM-agentic)	✓	○	✗agentic	○	○	✓	○
One all-inclusive price	✓	✗quote	✗	○per-seat	✗credits	free	○

Built to own tabular data preprocessing — deeper, faster, reproducible, end to end.

Under the hood

For engineers who want proof, not promises.

Leakage detection, not just avoidance

OreML follows the industry standard — and then a dedicated leakage screen and a provenance-aware leakage firewall measure, feature by feature, whether it actually leaked for your dataset. Columns that determine the target, near-perfect correlations and other targets' labels are dropped by name and by measurement — with a stricter threshold for any feature that saw the target while it was being built. It doesn't just avoid the easy mistake; it measures the hard one.

Three model-based checkpoints

Paired cross-validation (full vs dimensionality-reduced) and balanced CV (a gradient-boosted judge, before/after balancing) — model-based performance at three checkpoints.

Two routes, one result

The engine measures both of its internal compute routes and sends each step down whichever one proves faster — and both routes produce the same result on the same data, proven by differential testing rather than assumed. Speed is a routing decision; correctness is not.

Disk-safe by design

Free disk is checked before the first byte is written, and safe intermediate-artifact cleanup protects you from disk overload on large datasets.

What OreML is NOT

No fluff. No lock-in. No surprises.

✗

Not another meaningless AI app that nobody needed. The wave of chat-wrapped tools is built for the demo, not for the job: when agents were measured against real data-engineering work in 2026, the best of them finished under a fifth of the tasks — and returned a different answer on each run, with randomness turned off. Preparing data is not a place for a plausible guess. OreML is deterministic: the same data and the same settings produce the same decisions, every run, and it shows you its reasoning at each of the 25 steps instead of asking you to trust it.

✗

Not another spreadsheet look-alike.

✗

Not another "all-inclusive" tool that needs a lengthy tutorial — nor the weeks of courses, certifications and MOOCs those tools quietly assume you have already sat through. There is no query language to learn, no notebook to set up, no framework to pick and no 40-hour curriculum standing between you and your first clean dataset. If you understand your data, you can run OreML on day one — the expertise is in the software, not in the prerequisites.

✗

Not a general-purpose, academic-level solution.

✗

Not a free app with limited, mostly-unnecessary features.

✗

Not an app with a bulky price range and complex add-ons. One price. Every feature.

✗

Not another platform with 500+ connectors. Connector counts measure where data comes from — not what happens after it arrives. OreML reads what matters: local files across nine format families, databases, and cloud object storage — and then goes deep on the 25 steps between raw data and ML-ready output.

Why now

Most AI projects fail at the data — not the model.

$105.4B

Big-Data & Data-Engineering Services market, 2026 Mordor Intelligence

40%

of a data engineer's week goes on bad data Monte Carlo

~90%

of AI/ML projects depend on pipeline robustness industry estimate

~58%

Python adoption & rising; 80% of data devs use pandas

For data teams

Built for data engineers, analysts, scientists and ML engineers doing tabular preprocessing.

90% of AI depends on the data pipeline.
OreML gets it right.

Your data and time deserve better.

One dataset, zero commitment. Every feature included — for one price. Possibly the only data-preprocessing tool you'll ever need.

Start the free 1-dataset trial €49/month excl. VAT · no data cap