fileAI gives developers structured, zero-shot data from any file. Built for LLMs and AI agents, our AI OCR transforms unstructured files into clean, enriched, and validated data, ready for downstream automation via configurable UI, API or MCP.

When we started fileAI, the bottleneck wasn’t AI — it was the messy, manual work still required to prepare data before AI could do anything useful.

We built fileAI to solve that: a way to extract, enrich, and verify structured data from files in a single call. No templates. No brittle rules. Just clean, fit-for-purpose output.

Our public API and platform combine a powerful classification engine with AI schema logic — so developers can parse any file, enrich them across systems, and get zero-shot cited, structured data ready to flow directly into agents, LLMs, or downstream automation.

What’s under the hood:
Single-call data transformation — From raw file to clean, verified zero-shot output
AI schemas — Customisable, enrich with cross-file context, Internet search, APIs, or MCP
Built for LLMs — Output is structured, consistent, and orchestration-ready
Trusted at scale — Used by KFC, Toshiba, MS&AD and 400M+ files processed
Fast and flexible — Self-serve, pay-as-you-go, and zero setup required

This is the same infrastructure that powers enterprise automations in finance, insurance, logistics, and legal — now open to every developer. Can’t wait to see what you build with it.

Happy to answer any questions and hear your feedback!

Hey PH fam! 👋

I’m pumped to share fileAI with the global dev community today! 🚀

As someone who’s watched countless AI projects crash and burn, I can tell you the problem is NEVER the AI itself – it’s the soul-crushing data prep work that kills momentum before you even start.

We’ve all been there: spending 80% of our time wrestling with messy PDFs, inconsistent formats, and brittle extraction pipelines just to feed our models clean data. It’s the invisible productivity killer that no one talks about.

fileAI completely eliminates this pain.

Instead of building complex extraction pipelines for every file type, you get ONE API call that transforms any messy file into perfect, structured data ready for your LLMs and agents.

What makes fileAI a game-changer:

→ 28x more accurate than AWS, Google, and LlamaIndex

→ Zero-shot extraction (no templates or training needed)

→ Works with ANY file type out of the box

→ Enriches data with cross-file context and web search

→ Built for enterprise scale (trusted by KFC, Toshiba, MS&AD)

→ Self-serve with pay-as-you-go pricing

It’s honestly like having a data engineering team that never sleeps. The kind that turns your messiest files into production-ready datasets in seconds, not hours.

This is the same infrastructure powering enterprise automations in finance, insurance, and logistics – now available to every developer who’s tired of data prep hell.

Ready to turn your biggest AI blocker into your biggest advantage?

The team - Clare, Christian and Tim - are here to hear your feedback and answer any Qs! 🔥

@thisiskp_ Happy to see you on the leaderboard, KP! :)

Question for the makers: Say if a user's use case is accounting, how does fileAI handle exceptions, such as mismatched invoices or unusual ledger items?

@thisiskp_ @rohanrecommends Hey Rohan, great question because exception handling is a tricky problem. The fileAI platform has the capability to group, match, and compare invoices to find exceptions or atypical items either via cross-file validation or validation against a set of pre-defined customer validations. Every business is different, so an "unusual ledger item" at accounting firm A my look very different than on at restaurant chain B. That's why we prioritize flexibility and control for our customers - and give them the freedom and flexibility to craft those validations with natural language prompting.

Congrats team, very impressive product!

I’m a big fan of quick proof of concepts plus strong scaling, and I like the focus on trust in the data.

It's nice seeing an AI company with a well thought out value prop and its own models, not just another GPT wrapper.

Though what happens when someone builds a better model?

@adamj13 Hey, Adam - thanks for your support of the launch! Our big north star is model flexibility and portability. Within the platform, our customers can choose from a variety of fileAI models that are optimized for their target languages and use cases. We're hyper-focused on the real-life tasks and challenges our customers see, and run a constant training, tuning, deployment, and deprecation cycle with our models to preclude drift and deliver best-in-class capabilities. Honestly, we love seeing the newest and best come out because it gives us a great opportunity to benchmark ourselves.

@adamj13 @tim_prugar AND we allow config to access off-the-shelf foundation models, so if you have a preference or mandate for a reason other than performance, you can opt for a model of choice. With new models being released constantly, it’s hard to know what’ll be available in a few weeks — let alone a year from now. We like to think of it as future-proofing for AI :)

fileAI

Classify, extract, enrich, and validate any file