We Built This Because
From a frustrated observation to fifteen verification layers across 36 industries — all without your documents ever leaving your computer.
We built this because nobody else would.
Day One
It started with a simple question: what if you could strip sensitive data from a document before it ever left your desk? We picked a country, picked a document type, and started writing the rules. We had no idea how deep this would go.
Not Good Enough
Five days in, we ran a real insurance claim through the system. It found the email address and the phone number. It missed the policy number, the claim reference, and three people’s names. So we tore the whole thing apart and started over. The second version caught what the first one couldn’t.
Original Values Stay Local
Most redaction tools send documents to a server somewhere. We focused the first boundary on browser-local detection and redaction, so original sensitive values stay local during that step. This was the moment the product became what it is.
See our privacy architecture →50 Countries. One Tool.
A Tax File Number in Australia looks nothing like a Social Security number in the US or an NHS number in the UK. Every country has its own ID formats, its own regulations, its own edge cases. We built recognition for 50+ of them, because your documents don’t stay inside one border.
We broke it twice along the way. Worth it.
How Sure Is Sure Enough?
Is “123-456-789” a phone number, a case reference, or a government ID? A blunt tool just highlights everything and leaves you to sort it out. We added confidence scoring. Now the system tells you how certain it is about each detection, so you can make the call. Your judgement, informed by ours.
Teaching It What to Ignore
The system was catching too much. Street addresses in the middle of paragraphs. Common words flagged as names. “Victoria” the state, not “Victoria” the person. We spent weeks on over a thousand targeted improvements, teaching it the difference between sensitive data and ordinary text. We broke it twice along the way. Worth it.
Knowing, Not Guessing
A random 9-digit number is just a number. But a real Tax File Number follows a specific mathematical formula. We built validators that check whether an ID is structurally genuine, not just whether it looks like one. The difference matters. This single change cut false flags in half.
From 8 to 15: Building the Engine That Catches Everything
We started with eight verification layers. Tested against 90 million characters. Found the gaps no single pass could catch. So we built deeper — fifteen layers, each one earning its place through real-world testing.
See how our 15 layers work →3,000 Things It Recognises
Medicare numbers. BSBs. ABNs. Passport numbers. Licence plates. Medical record IDs. Insurance claim references. Internal case numbers. Each one researched against real documents, each one validated. The library quietly crossed 3,000 recognised data types.
The architecture changed. The promise didn't.
UK. Canada. New Zealand. Ireland.
An Australian medical referral is not a British one. A Canadian tax return has different identifiers than a New Zealand one. We added dedicated country configurations, each one tested against real document formats from that jurisdiction. Not textbook examples. The real thing.
Starting Over. Again.
Good enough wasn’t good enough. We redesigned the entire detection system from scratch. Industry-specific configurations. Smarter analysis. A completely new approach. The fourth major version. The one that finally works the way documents actually work.
36 Industry-Specific Engines
An insurance claim has different sensitive data than a legal contract. A medical referral has nothing in common with a construction invoice. We built 36 industry-specific engines: legal, healthcare, government, finance, HR, insurance, and more. Each one tuned for exactly the documents your team handles every day.
Explore all 36 industries →Back to the Drawing Board. One Last Time.
The AEGIS engine: fifteen verification layers, each doing one thing ruthlessly well. A contextual recognition layer for the patterns rules can't catch. And 36 industry-trained engines, each scoring above 0.997 F1.
Your data is yours. Period.
Thirteen milestones. Four rebuilds. One promise that never changed. Now it's yours.