Compliance middleware for regulated AI

AI you can audit, not just trust.

Bhala spots a sensitive trait — like race or religion — strips out its influence, and hands you a plain receipt an auditor can read. It runs on top of the AI you already use.

Request a compliance briefing See the evidence

Start where you sitCompliance & risk →Trust & Safety →Engineering →

How it fits

4–6.5×

cleaner bias removal than the tools most teams use today

Every decision

comes with a plain, signed receipt an auditor can read

Dozens of languages

including many that mainstream AI overlooks

How it fits

Two halves of one job.

You keep the AI you have and add the proof you need.

The regulation you have to satisfy

Rules that now demand proof, decision by decision, that a protected trait didn't tip the outcome:

→The EU AI Act
→US fair-lending rules
→New York City's hiring law
→The Digital Services Act

The middleware that satisfies it

A portable, independent layer that sits between the model and every decision — so anyone can inspect it. It spots the sensitive trait, strips out its influence, and signs a plain receipt, without touching the AI underneath.

No rebuild, no retraining, no switching vendors — the same layer travels with you across models and providers.

What you can finally do

Four things ordinary AI simply can't give you.

The AI everyone else sells can't be inspected, corrected, or explained. Bhala can.

Tell it what to do — and know it did it

Every time

same instruction, same result

Most AI treats your instruction as a suggestion, and you hope for the best. Bhala follows the instruction exactly — the same way every single time — and shows you, in writing, that it happened.

Fix one bias without breaking everything else

4–6.5×

cleaner than the usual fix

Older tools that remove a bias tend to damage everything around it — like erasing one word and smudging the whole page. Bhala removes the bias and leaves the rest intact, several times more cleanly. Every fix comes with a receipt showing exactly how little else changed — the evidence an EU AI Act or fair-lending audit asks for.

Catch what keyword filters miss

Even in disguise

coded · sarcastic · adversarial

Ordinary filters match words, so hate that's misspelled, coded, or dressed up walks right past them. Bhala reads the structure of the meaning instead — it flags disguised hate and clears the counter-speech that condemns it. Live in production today.

Runs where the big AI can't go

Fully offline

ordinary computers · your data never leaves

Runs on everyday computers, with no connection to the outside world. Hospitals, courts, banks, and governments that can't send data to the cloud can run Bhala entirely in-house — so sensitive records never leave the building.

The proof

What it means for you — in plain terms.

Every claim here is backed by testing on public, independent data. The full technical numbers and methods live on our benchmarks page; this is what they mean in practice.

Fixing bias

Several times cleaner

Removes a sensitive trait's influence several times more cleanly than the tools most teams use today — the surrounding meaning stays intact.

Proof for regulators

A receipt every time

Each result comes with a plain, signed receipt — so an auditor sees evidence, not a locked door, and can check it themselves.

Catching hate

Even in disguise

Flags hate and slurs even when they're coded, sarcastic, or dressed up to slip past ordinary keyword filters. Live in production today.

Dependable

The same, every run

The same input gives the same result, over and over — a reliable answer you can stand behind, not a one-off lucky test.

Wide coverage

The traits that matter

Reliably spots protected traits like race, religion, disability, and nationality — the categories fairness rules actually name.

Global reach

Dozens of languages

Understands dozens of languages, including many that mainstream AI overlooks — built to serve far beyond English, not just the languages big models favor.

See the full numbers and how we tested

The problem

The transparency gap.

Almost every modern AI is opaque. It gives you an answer, but it can't tell you why — and you can't change one part of its behavior without rebuilding the whole thing from scratch.

Your compliance team needs to show, decision by decision, that a protected trait didn't drive the outcome. Your engineers can't get that out of an opaque model. And the gap is only growing as the EU AI Act, New York's hiring law, Michigan's insurance rules, and the EU's online-safety act all take effect in 2026.

Bhala closes the gap. It sits on top of the AI you already run and gives you a dial for each sensitive trait — so you can see it, turn it down, and prove you did. Same accuracy, plus control you can actually show an auditor.

See the evidence

Why now

The rules are catching up with opaque AI.

The EU AI Act's obligations for high-risk systems take effect in August 2026. Regulators increasingly want proof, on every decision, that a protected trait didn't tip the outcome — and a record an auditor can read. Ordinary AI can't produce any of that.

Bhala gives compliance teams exactly what the rules now ask for: the ability to point at a sensitive trait, remove its influence, and hand over a plain receipt that proves it — on the systems you already run.

Working today

Software that spots and removes sensitive-trait bias on top of the AI you already run, catches hate speech even in disguise, works across dozens of languages, and hands you a plain receipt for every decision — no rebuild required.

See the evidence, then talk to us.

Every claim on this page is backed by testing you can check yourself. Start with the evidence, or tell us the governance problem you need to solve.

See the evidence Talk to us