This is why we're building
Invaro Toolkit.

The gap we saw

Honestly, we stumbled into this problem by accident. We were working on a completely different project when we hit a wall trying to extract data from bank statements. What should have been a simple task turned into weeks of frustration with OCR tools, regex patterns that broke on edge cases, and PDFs that seemed designed to resist automation.

The real challenge became clear quickly. Native PDFs with selectable text worked fine until you hit a scanned PDF. Then you needed OCR. But wait — some PDFs were scanned copies of already-scanned documents, so the quality was terrible. Others had weird rotations, watermarks, or were photographed with phones instead of properly scanned. Each type needed completely different handling.

The more we talked to other developers, the more we realized everyone was fighting the same battle. Smart people were spending days reinventing the same document parsing wheel, over and over. It felt like such a waste of talent and time.

What we're building

We're not trying to revolutionize anything. We just want to solve this one problem really well. We want to build something that just works.

It's simple: you send us a document, we send back clean, structured data. No setup, no training models, no wrestling with different file formats. We handle all the messy parts so you can focus on building the stuff that actually matters to your users.

Why we care

We've seen talented developers spend months building document parsers that should have taken days to integrate. Time that could have been spent shipping features, fixing user-facing bugs, or solving the core problems their businesses actually need solved.

When teams can skip the document processing headache entirely, they ship faster. Their products get better. Their users get value sooner. That's the real impact we're after — not just saving developers time, but helping them build better products.

How we build

We're pretty obsessive about accuracy because we know how frustrating it is when automated tools get things wrong. Every failed parsing job gets reviewed. Every edge case becomes a test case. We probably spend too much time on documents that represent 0.1% of use cases, but those edge cases matter.

We also talk to our users constantly. Not in a formal survey way, but real conversations about what's working and what's not. Some of our best features came from developers telling us about their specific pain points.