fileAI
fileAI automates enterprise document processing and data extraction via API.
fileAI is an AI-powered file processing platform that automates the extraction and enrichment of data from unstructured documents (PDFs, spreadsheets, emails, handwritten forms) at enterprise scale. The platform combines proprietary ML and vision language models to deliver deterministic, hallucination-free results with 28x better accuracy than AWS, Google, and OpenAI on real-world data tasks. It serves enterprises across insurance, finance, QSR, and professional services who need to automate document-intensive workflows without manual data wrangling.
Problem solved
Enterprise teams waste significant time and resources manually extracting, cleaning, and enriching data from unstructured documents, causing delays in claims processing, invoice handling, and other critical workflows.
Target customer
Mid-market to enterprise companies processing high volumes of unstructured documents (insurance claims, invoices, financial records, certificates of analysis); particularly insurance, QSR, and financial services verticals.
Founders
C
Christian Schneider
CEO & Co-Founder
Investment banking and corporate finance background from McKinsey and Delivery Hero; previous co-founder of DishDash; BS in Economics and Finance from Goethe University Frankfurt.
C
Clare Leighton
COO & Co-Founder
Drives global scale and enterprise partnerships at fileAI; background in scaling enterprise operations.
Funding history
Seed
Unknown
September 2021
Led by Antler Vietnam
· Unknown
Series A
$14M
February 2025
Led by Illuminate Financial
· Antler Elevate, Insignia Ventures Partners, Heinemann Group
Total raised:
$26M
Pricing
Freemium self-serve tier at $0/month with core features; usage-based pricing charged per page processed; Enterprise tier with custom pricing including custom-trained models, unlimited schemas, private cloud/on-premise deployment, 99.9% SLA, and dedicated account manager.
Notable customers
Mitsui Sumitomo Insurance Group (MSIG), MS&AD, DirectAsia, Toshiba, KFC, Pizza Hut, Ernst & Young, Nippon Paint, Little Farms
Integrations
Box, HubSpot, AWS, Google Cloud, multiple import/export integrations via API
Tech stack
jQuery (JavaScript libraries)
HubSpot Chat (Live chat)
Webpack
Open Graph
HTTP/3
Outbrain (Advertising)
Linkedin Insight Tag (Analytics)
HubSpot Analytics (Analytics)
Hotjar (Analytics)
Google Analytics (Analytics)
Datadog (Analytics)
HSTS (Security)
Google Font API (Font scripts)
jQuery CDN (CDN)
Google Hosted Libraries (CDN)
Cloudflare (CDN)
HubSpot (Marketing automation)
Google Tag Manager (Tag managers)
Webflow (Page builders)
Amazon Web Services (PaaS)
Segment (Customer data platform)
Website
Competitors
Automattic
Broader automation platform; fileAI specializes in deterministic file understanding and data preparation.
Box
Content management platform; fileAI focuses on AI-powered data extraction and enrichment from files.
Airtable
Database and workflow platform; fileAI targets the specific problem of unstructured data preparation for AI pipelines.
Veryfi
Narrower focus on invoice and receipt processing; fileAI handles any file type at enterprise scale.
Nanonets
Custom model training platform; fileAI combines pre-built accuracy with custom model options.
UiPath
Broader RPA platform; fileAI is specialized for intelligent document understanding and data extraction.
Why this matters: fileAI represents a rare moment where a specialized AI tool has proven measurable, quantifiable advantages over established incumbents (28x accuracy improvement over AWS/Google) on a genuinely high-value problem (document automation). With $26M raised, $14M Series A in Feb 2025, and proven ROI at major enterprises (60% time savings at MSIG, 82% efficiency gains elsewhere), fileAI demonstrates that data preparation—not algorithms—remains the real bottleneck in enterprise AI adoption.
Best for: Enterprise teams processing 100K+ documents annually who need accurate, automated data extraction without building custom OCR and ML pipelines; particularly insurance claims, invoice processing, and document verification workflows.
Use cases
Insurance Claims Processing
MSIG reduced claims processing time by 60% and improved fraud detection within the first month of deploying fileAI. The platform automatically extracts claim data from multiple document types (forms, receipts, medical records) with deterministic accuracy, eliminating manual review bottlenecks.
Invoice and Expense Automation
Little Farms reduced invoice processing time from five days to three minutes by deploying fileAI across accounts payable workflows. The system extracts invoice line items, vendor details, and amounts with 82% efficiency improvements over manual processing.
Certificate and Document Matching
Nippon Paint saved 70% of processing time and redeployed 10% of staff by automating Certificate of Analysis matching and invoice reconciliation with Box integration. The platform handles multi-page document correlation and complex data validation without manual audit.
Alternatives
AWS Textract
Cloud-native OCR service; fileAI achieves 28x better accuracy on real-world data and includes data enrichment and schema memory, not just text extraction.
Google Document AI
Google's document processing API; fileAI combines multi-file reasoning and explainability to eliminate hallucinations, with better accuracy on enterprise documents.
Rossum
Document AI with human-in-the-loop; fileAI emphasizes deterministic results and self-serve automation with less manual review required.
Instabase
Low-code document processing platform; fileAI optimizes for out-of-the-box accuracy with API-first architecture and minimal setup.
FAQ
What does fileAI do? +
fileAI is an AI-powered platform that automatically extracts, enriches, and cleanses data from unstructured files (PDFs, spreadsheets, images, handwritten documents) for use in business processes and AI pipelines. It combines proprietary machine learning and vision language models to deliver deterministic, hallucination-free results with 28x better accuracy than major cloud providers on real-world data tasks.
How much does fileAI cost? +
fileAI offers a free self-serve tier with core features, then usage-based pricing charged per page processed. Enterprise customers receive custom pricing that includes custom-trained models, unlimited schemas, private deployment options, 99.9% SLA, and a dedicated account manager.
What are the main alternatives to fileAI? +
Top alternatives include AWS Textract (cloud OCR, less accurate on complex documents), Google Document AI (similar capabilities, less specialized), Rossum (requires more human review), Nanonets (custom model training focus), and UiPath (broader RPA platform rather than specialized data extraction).
Who uses fileAI? +
fileAI is trusted by enterprises including Mitsui Sumitomo Insurance, Toshiba, KFC, Pizza Hut, Ernst & Young, and Nippon Paint. Target customers are mid-market to enterprise companies processing high volumes of documents in insurance, financial services, QSR, and professional services who need to automate claims, invoices, and document verification workflows.
How does fileAI compare to AWS Textract or Google Document AI? +
While AWS and Google provide reliable OCR, fileAI achieves 28x better accuracy on real-world enterprise documents through proprietary hybrid ML+VLM models combined with schema memory and multi-file reasoning. fileAI also includes data enrichment and deterministic results without hallucinations, whereas cloud OCR services require additional processing layers to handle complex documents.
Tags
document processing
data extraction
OCR
AI automation
enterprise workflows
unstructured data
intelligent document understanding