From Data Chaos to AI Clarity: The fileAI Story
Featured: Exclusive interview with Christian Schneider, CEO of fileAI which aims to be the Stripe for unstructured data
Hey everyone 👋
Welcome to another featured interview on my newsletter that highlights the story and lessons from bold founders tackling ambitious problems in the AI world.
Meet Christian, the Co-founder and CEO of fileAI — a data preparation platform that turns messy, unstructured data into clean, verified inputs for AI workflows. Instead of letting developers spend weeks wiring together OCR, prompts, and regex just to get basic data structure, fileAI users get clean, enriched, verified data ready for their AI pipelines in one simple API call.
After processing 500m+ files for enterprise clients like KFC and Toshiba, and achieving 28x better accuracy than AWS, Google, and OpenAI on real-world data preparation tasks, they've proven that the real bottleneck in AI isn't the algorithms — it's the data preparation layer. What started as frustration with a fintech client's messy data pipeline has become a mission to free AI developers from repetitive data cleanup so they can focus on meaningful innovation.
I was genuinely curious about his startup's backstory and journey so far, so I sent him a few questions that turned into this interview.
Here's the transcript of my interview with Christian. Enjoy!
1. Who are you and what's your background that led you to tackle the data preparation problem in AI?
My name is Christian Schneider, Co-founder and CEO at fileAI. I’ve spent the past 8 years building data workflows and AI products across startups and complex enterprises. Every time we tried to automate, we hit a wall where data was either not readily available, or was inaccurate. The result was a half-baked automated workflow that usually never really impressed anybody. That frustration is what led to fileAI.
2. Can you walk us through what fileAI does and which specific pain points it solves for developers building AI applications?
fileAI is a data preparation platform built for speed, trust, and flexibility. Devs can use our API to parse, understand, fetch and verify data all in one simple call. This includes super messy unstructured data such as long PDFs, emails, scans, handwriting, spreadsheets, and more. Meanwhile, non-technical users can use our no-code UI to process and QA files in bulk with zero setup.
Instead of wiring together OCR, prompts, or regex, you get clean, enriched, verified data out of the box—ready for your AI workflow or automation pipeline. We combine schema memory, multi-file reasoning, and explainability in a way that surpasses generic AI OCR and vector tools. Whether you're a founder shipping fast or an enterprise integrating at scale, fileAI meets you where you build. Combining predictive and generative AI components that remove hallucinations and produce deterministic results, fileAI is a necessity for anyone needing to build set-and-forget workflows.
3. How did the idea come about? What was the moment you realized data prep was the real bottleneck, not AI itself?
While building AI workflows for a fintech client, our models behaved exactly as designed, yet progress kept stalling. The root cause was always the same: scattered files that carried inconsistent tables, mislabeled fields, and restricted labels or tags. Every sprint drifted back to “fix the input,” and we eventually had a team of five engineers manually cleaning outputs just to keep the pipeline running.
That experience became our inflection point. We saw that algorithm performance was not the true constraint—the real bottleneck was upstream data preparation. So we paused, rewrote the pipeline from scratch, and set a higher bar: a system that could automatically understand, structure, and enrich raw files before they ever touched a model. The aim was no longer just “clean data in, clean data out,” but an adaptive data layer that frees teams to focus on meaningful innovation rather than repetitive cleanup.
4. You mention being "28x more accurate than AWS, Google, OpenAI, and tools like LlamaIndex" - can you share the story behind achieving this breakthrough?
We stopped trying to brute-force general models. Instead, we built agentic components that specialize in different tasks: schema detection, cross-file reasoning, verification, enrichment. Then we stitched them together into a single system with memory and explainability. Once we ran benchmarks on real-world examples, the difference was clear—our outputs needed over 95% less manual correction. We added zero-shot classification to make it ultra convenient for anyone who wants to start a workflow and doesn't want to start from scratch.
5. Can you share a few inflection points since fileAI's start—from initial concept to processing 500M+ files for enterprises like KFC and Toshiba?
Our first proof-of-concept processed 1M files in a few weeks for a legacy finance system. That gave us conviction. The big unlock came when a Japanese insurer used fileAI to run 10 years of accident claims through our API, without training a single model. From there, we went horizontal, proving the same system worked across logistics, insurance, F&B, and more. That’s how we hit 500M+ files processed.
6. What was your first major enterprise win, and how did it validate your approach to solving the data prep problem?
It was MS&AD Insurance Group, one of the largest insurers in Asia. They were using three different tools to handle scanned reports, photos, and structured data, and still needed humans to verify everything. We swapped in fileAI and had them live on production in under 2 weeks. That win showed us we didn’t just have a tool, we had a platform.
7. What are some non-obvious learnings you've had about the AI data preparation market that most developers don't realize?
First: accuracy isn’t enough—teams need explainability. They don’t just want the data; they want to know why it’s right. Second: unstructured data is often multi-file. Developers assume parsing one doc is enough, but real-world workflows often require reasoning across related files. We designed fileAI with explainability in mind from the start, and it’s been revolutionary.
8. As you scale fileAI, what's your vision for how it might change the AI development landscape when data prep is no longer a bottleneck?
Right now, devs spend weeks building fragile pipelines just to get structured input. When that’s no longer a problem, they’ll be able to focus entirely on logic, outcomes, and user experience. AI apps will become more reliable, more contextual, and way faster to build. We want fileAI to be the default layer under every serious AI workflow—like Stripe, but for unstructured data.
9. What's some hard-earned advice you'd share with other founders building AI infrastructure startups?
Don’t get distracted by shiny models. Most customers don’t care about benchmarks, they care about whether your output works in production. Also important is to build with real edge cases early. If you solve for perfect PDFs, you’ll get crushed the moment someone uploads a rotated scan in Korean with a coffee stain.
10. Where can people learn more about fileAI and try out the platform?
We just launched on Product Hunt: check us out here and try the API for free. If you’re working with messy files and building something big, we’d love to help.
That's a wrap on this week's featured interview — hope Christian's journey from frustrated data engineer to AI infrastructure pioneer sparked some ideas for how you approach bottlenecks in your own development workflow.
What can we learn from this story? Sometimes the most valuable startups don't solve the obvious problem — they solve the hidden constraint that everyone assumes is just "part of the job." Christian's team didn't build better AI models; they built the missing infrastructure layer that makes AI models actually useful with real-world data.
The lesson? Don't just optimize the sexy part of the stack — find the unglamorous bottleneck that's secretly holding everyone back. The best infrastructure plays often hide in plain sight, disguised as "just the way things work."
Thanks for reading!