Tabular prediction, delivered with its proof.
Drop in a table, a CSV or a database extract, and Tabaxiom turns it into a predictive model in one second: imputation, scoring, anomaly detection. Every evaluation ships with its verifiable audit proof. No training, no data scientist, no GPU.
A foundation model for tabular data
Just as large language models learned text, a Tabular Foundation Model learned the structure of tables across millions of examples. It predicts on your columns without retraining, it reasons in context from your few rows.
No training run to launch
The model reads your table and predicts. A new dataset means an immediate result, no hyperparameter tuning, no pipeline to maintain.
Strong where data is scarce
Where classical methods demand tens of thousands of examples, it performs from a few hundred rows, the regime of real business cases.
Local and on-premise
Runs on CPU, on your premises. The model saves without the training data, your data never leaves your infrastructure.
One model per table
Every table, every client, every event gets its own instant model, without multiplying pipelines or operating costs.
Calibrated and explainable
Calibrated probabilities, column importance, built-in quality control. Every prediction is auditable, not a black box.
Embeds anywhere
In your database (SQL function), your ETL, your CRM, your application, prediction where your teams already work.
Every evaluation comes with its proof.
Anyone can plug in a tabular foundation model, they are public. Our difference: auditable trust, an evaluation protocol that cannot be gamed, inherited from the most demanding quantitative finance. In healthcare, insurance, credit, anywhere a prediction carries consequences, it is the line between “works on my laptop” and “I can deploy and defend it”.
Sealed before computation
The hypothesis is timestamped and hashed before any training. No rewriting history after the fact, no picking the convenient result.
Purged time
We test on strictly posterior data, with an embargo. The future never leaks into the past, the number-one trap of fake backtests.
Anti-leak canary
We replay every test with shuffled labels. If the score still rises, there is a leak: the run is voided, not published.
Judged exactly once
One verdict, once, engraved. No “retry until it works”, the silent recipe behind most unverifiable results.
Calibrated probabilities
“30% risk” means 30%. You can size, provision, and decide on the number, not merely rank it.
Honest baseline
We always measure against the simple model to beat. No “+20%” out of thin air: the gain is real, quantified, reproducible.
Give it a table full of holes. Watch it fill them.
Drop in a CSV with empty cells, or run an example. This illustration runs 100% in your browser (nothing is sent) and gives a qualitative preview. It is not the Tabaxiom model: the real model runs server-side through the API, with its true accuracy and its evaluation proof.
Local illustration, not the full model. The real model runs server-side via the API.
Drag a CSV file here or
Predicted cells are highlighted. Live computation, no tricks.
One capability, one input, one output
No abstraction: for each verb, here is the real call and what comes back. Same engine, same evaluation proof every time.
→ Result
| client_id | risk | proba |
|---|---|---|
| C-4471 | churn | 0,94 |
| C-1180 | churn | 0,89 |
| C-8023 | stays | 0,12 |
Input, empty cells (∅)
| city | population | density |
|---|---|---|
| Bordeaux | 260 000 | ∅ |
| Lille | ∅ | 6 612 |
→ Output, completed in context
| city | population | density |
|---|---|---|
| Bordeaux | 260 000 | 4 629 |
| Lille | 232 000 | 6 612 |
A trade that looks normal field by field…
| instrument | qty | price | vs mid |
|---|---|---|---|
| OAT 10Y | 5 M | 98,10 | +0,02 |
| OAT 10Y | 5 M | 91,40 | −6,7 |
| source | name | id |
|---|---|---|
| Internal | ACME Corp. | FR0000120271 |
| Broker | Acme Corporation | ACME.PA |
→ Verdict
| same entity | confidence | proof |
|---|---|---|
| yes | 0,97 | a91f…3d |
These examples illustrate the target API contract (representative values); the signed proof/receipt field is on the roadmap. The only measured, sealed result on this site is the benchmark below, that is our honesty rule.
2,321 companies that truly delisted. A single verdict.
Hypothesis sealed before computation, time purged to forbid any leak, anti-cheat canary run, then measured. On real delisted companies, the foundation model beats gradient boosting, the industry yardstick.
| Model | AUC | Top-risk decile |
|---|---|---|
| Logistic regression (floor) | 0.704 | 1.0× |
| Gradient boosting (yardstick) | 0.743 | — |
| Foundation model (TabICLv2) | 0.779 | 4.3× |
The foundation model achieves the best AUC and captures 4.3× more delistings in its highest-risk decile, the number that matters when prioritising follow-up.
Verdict sealed, judged exactly once, non-replayable. The reproduction is published in the internal claims register.
Honest scope: price/volume signal only, adding fundamentals is the next step. 34,000 training rows is mid-scale, not the “big data” regime where a well-tuned tree can pull ahead again. The verdict covers “foundation ≥ gradient”, not “finished product”. An audited proof also states its limits.
From table to decision, across every industry.
The same technology, wherever there is a table and a target. 16 sectors, 83 documented cases. Expand a sector, then open the demo on a close example.
A CSV goes in. Calibrated predictions, and their proof, come out.
Drop in a table, name the target column, a model served in tens of milliseconds, with its validity metric and its evaluation proof. No data science required on the calling side.
/predictpredict one column from the others, classification or regression./imputefill a table's empty fields, column by column./anomalyflag aberrant rows, including multi-column anomalies./matchdecide whether two records are the same entity, dedup, reconciliation.Today: every evaluation includes its protocol, its baseline, its leak-test status and its diagnostics, which your compliance team can verify. On the roadmap: a signed, portable receipt on every API answer.
Available today on-premise / locally. The hosted multi-tenant API is in early access, request your key.
From your table to prediction in production
Which column to predict, which table, which business gain, scoping workshop.
CSV, SQL database, data warehouse: read-only connector, no migration.
The foundation model serves your predictions, calibrated, audited, no training.
API, SQL function, or embedded in your tool. Local, cloud or on-premise.
What we deliver
An audited prediction service, honest baseline, anti-cheat canary, real metrics. Not a black box.
What we get asked most
What is a tabular foundation model? ▾
A model pre-trained on millions of tables that predicts on your columns without retraining: give it a few example rows and it answers in context. Just as a language model learned text, this one learned the structure of tabular data.
Do I need a GPU? ▾
No. The model runs on CPU, locally or via the API. This site's illustration computes in your browser and is not the full model. No GPU to buy.
How is this different from XGBoost / AutoML? ▾
Not raw accuracy, the deployment model: zero training, one model per table, strong on small data, cold-start, served on CPU. You can retune a tree; you cannot give it these properties.
Does it really beat gradient boosting? ▾
On our sealed test (2,321 companies that truly delisted): AUC 0.779 vs 0.743, 4.3× in the top-risk decile. Hypothesis sealed before computation, judged once. See “Proof, not promise”.
Does my data stay private? ▾
Yes. Local, on-premise or via the API; the model saves without your data. The site's public illustration transmits nothing and is not the full model.
How much data do I need? ▾
A few hundred to a few tens of thousands of rows: the sweet spot. At millions of rows, a well-tuned tree can regain the edge, we say so.
What does “audited prediction” mean? ▾
Hypothesis sealed before computation, purged time, anti-leak canary, judged once, calibrated probabilities. Every published evaluation comes with its verifiable proof; a signed receipt per API answer is on the roadmap.
Can we try it on our own data? ▾
Yes: drop a CSV into the site demo for an instant preview, or contact us for an evaluation on your real data, measured honestly.
You have a table. You have a case.
Tell us what you want to predict, we show you the result on your real data, measured honestly, with its evaluation proof.