Solutions · Enterprises

Remove dataset bias before the regulator finds it. Keep your model's performance. Keep the evidence.

Your governance stack documents AI risk. Rosa is the instrument that actually fixes the data underneath it, and hands you the Article 10 evidence in the same run.

Start free Book a dedicated PoC

UK processing for the trial · No model rewrite · No credit card

The compliance leader's bind

Tools that document, not fix

GRC platforms log the bias finding. The dataset stays biased. The finding stays open.

Fine exposure

EU AI Act penalties reach EUR 35M or 7% of turnover at the top tier; data-governance failures for high-risk providers carry up to EUR 15M or 3%.

Audit prep in weeks

Evidence is assembled by hand, after the fact, every time an auditor asks.

Remediation unstaffable

Debiasing data properly is a research project your team cannot absorb alongside delivery.

One motion: remove the bias, produce the evidence

  • Audit findings close. Rosa removes the bias at source; the finding does not stay open as "monitored".
  • Audit prep collapses to a job run. Every run emits the immutable Run Manifest and PDF report. Evidence on demand, not by archaeology.
  • The model keeps performing. No fairness/accuracy trade-off: in the controlled experiment, fairness was added and accuracy improved.
  • No re-engineering. Rosa outputs a clean CSV. Any model, any stack, no wrapper libraries in your training code.
  • A clear residency story. Trial processing in the UK (eu-west-2, London, EU-UK adequacy); a dedicated instance in your own region for production data.

The proof your data scientists will check

Lead with the two answers that matter

0.747 vs 0.626 downstream R² with Rosa plus a model vs the model alone (0.569 with a parity constraint): fairness added, accuracy improved. R² measures how well predictions fit reality; higher is better. Controlled credit-risk experiment · methodology
bit-for-bit your data's distributions preserved in training output (float64 precision), so aggregates stay true and models stay calibrated Validation suite · methodology

Then let the team verify it themselves: Test 1 runs the Apple Card scenario in the browser, end to end, with the manifest to inspect.

The objections, answered straight

Will it degrade our model?

The controlled evidence says no: R² 0.747 with Rosa plus a model, against 0.626 for the model alone and 0.569 with a parity constraint. The honest scope: residual bias and downstream effects are dataset-specific, which is exactly why the free trial exists. Prove it on your data before anything is committed. See the validation page.

Where does our data go?

The free trial is processed in the UK (AWS eu-west-2, London), covered by the EU-UK adequacy decision. A dedicated PoC runs a private Rosa instance in your own region: your data never crosses a boundary you have not chosen.

Is this production-ready, and who is behind it?

Judge the product, not the headcount: a live portal and API, an eight-tool MCP (Model Context Protocol) server, immutable manifests on every run, published validation figures, and a dedicated-instance model for production data. Rosa is operated independently on technology with years of research behind it. The free trial means none of this asks for trust up front.

How much integration effort?

Rosa reads a CSV and returns a debiased CSV plus evidence. There is no SDK to wire into your training code and no model surgery. The one structural commitment: a model trained on debiased data should receive debiased data at inference, so Rosa becomes a stage in the pipeline, continuously evidenced.

Your team can prove it this week, free.

Start free Book a dedicated PoC

Free trial on the shared UK instance. Dedicated PoC in your own region, caps lifted.