fileAI AI OCR - Classify, extract, enrich, and validate any file
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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.
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fileAI
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!
Netlify
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?
fileAI
@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.
Bio Calls by Cross Paths
Congrats on the launch! How do you handle different file types?
fileAI
@heypaus Thanks for the question and for stopping by - we like to think we handle them pretty well! Image, PDF, Spreadsheets, Doc files and more all supported with a healthy backlog of multimodal files to support going forward.
Good luck with your upcoming launch, as well!
Bio Calls by Cross Paths
@tim_prugar Thank you so much!
Love your product, wish you all the best :))
Congrats on the launch! We're building something similar with RapidScan.AI, and totally relate to the pain of turning messy files into clean, structured data. Love how fileAI simplifies it all into a single API call — looks super solid 👏
fileAI
@vishal_maurya03 Thanks, Vishal. Always good to see other folks in the community working on the same tough problem.
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?
fileAI
@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.
fileAI
@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
AI infrastructure has come a long way — but the input and data preparation layer is still one of the most underdeveloped problems in the stack.
We built fileAI to fix that — so dev teams don’t have to spend months stitching together brittle extraction tools, validating schemas, cleaning or fetching mechanisms just to get started.
This is our first public launch, and we’re excited to open up a system that’s already processed hundreds of millions of files for global enterprises — now available to anyone building with data and AI.
Can’t wait to hear your feedback and see what you create! 🙌
Stepfun Diligence Check
This is exactly what developers need right now – powerful, flexible data extraction without the usual headaches. Excited to see fileAI open to everyone. Well done!
fileAI
@tomtomw Thanks, Tommy! Appreciate your support. We aim to remove the usual headaches - without adding any new ones!
fileAI
I’m Tim. I lead Product and Engineering at fileAI.
fileAI has been one of the most interesting real-world product challenges I’ve worked on: how to make raw files useful, fast — without forcing teams to build a Rube Goldberg machine.
We designed the platform so you can parse, enrich, and verify data from a file with a single API call — and give less-technical users the same power through a configurable UI. Whether you're automating workflows, building agent pipelines, or feeding LLMs, it’s built to flex with the edge cases that break most systems.
Now that it's open, I’m excited to see what others do with it — especially teams who’ve walked away from file processing projects before because it’s just too hard.
If you're exploring it for a project, I'd love your honest take — whether it's feedback on functionality, edge case behavior, or a feature you wish it had. Always looking to make it better for customer use cases.
Really interested in the citation and verification layer — how does that work in practice? Do you get traceable confidence scores or just a pass/fail?
fileAI
@lily_dewitt Hey Lily - this is a big question we get from customers. In the LLM / VLM era it looks like lots of people are trying to figure out how to get some of the same assurance in outputs that they received previously from ML or OCR confidence scores. We help our customers have trust and auditability in the data we return in a variety of ways including cross-file citations, OCR validations, and even validations against a custom set of customer-specific validation rules.
Kandid
Incredible to see a unified platform that automates complex file workflows across every department. How customizable are the AI schemas can we fine tune or extend them for unique field types or industry reports before scaling?
fileAI
@pulkitgarg Hey Pulkit, appreciate the support. Flexibility and customization of the schemas is one of our main features. After fileAI suggests a schema you can add fields or tables, delete fields, or even bring in information from the internet or other files in your Drive. We want our users to have the flexibility to build a schema (or schemas!) that work for them on their files - nothing cookie-cutter.