DocExtract
An intelligent document extraction platform that automates data capture from complex documents using AI.
90%
Reduction in processing time
99.2%
Extraction accuracy
15K+
Documents processed monthly
200hrs
Staff hours saved weekly
Overview
About this project
DocExtract is an enterprise-grade document intelligence platform built to eliminate the bottleneck of manual data entry. Organisations processing hundreds of invoices, contracts, and forms daily were drowning in repetitive extraction tasks that consumed skilled staff time and introduced errors at every step.
We designed and built a full-stack solution combining a custom-trained machine learning pipeline with a clean, intuitive web interface. The result is a platform that processes documents in seconds, learns from corrections, and integrates seamlessly with existing enterprise workflows via REST API.
Project Details
- Client
- DocExtract Ltd
- Delivered
- Mar 10, 2026
- Category
- TechnologyWebsite
- Technologies
- Next.jsPythonTensorFlowFastAPIPostgreSQLRedis
The Challenge
Manual document processing was time-consuming and error-prone, costing businesses significant resources.
The client's operations team was manually keying data from thousands of PDFs, scanned invoices, and structured forms every week. Accuracy hovered around 94%, meaning roughly 1 in 17 documents contained errors that propagated downstream into accounting and compliance systems. The process was entirely manual, non-auditable, and impossible to scale without proportional headcount growth.
Key Challenges
- AI-powered layout detection and field classification
- Real-time document review and correction interface
- Continuous model improvement from operator feedback
What we delivered
The Solution
Built an AI-powered extraction engine with a clean web interface for real-time document processing.
We developed a multi-stage extraction pipeline using TensorFlow for layout detection and field classification, combined with a fine-tuned OCR layer for handwritten and low-resolution inputs. A Next.js frontend provides a real-time review interface where operators can validate, correct, and approve extracted data before it flows into downstream systems. Every correction is fed back into the model, enabling continuous accuracy improvement.
Results
90% reduction in manual processing time with 99.2% extraction accuracy across all document types.
Within 90 days of deployment, the operations team reduced document processing time by 90%, freeing up over 200 staff-hours per week. Extraction accuracy improved from 94% to 99.2%. The platform now handles 15,000+ documents per month with a fully auditable trail, and the client has expanded usage to three additional business units.
90%
Reduction in processing time
99.2%
Extraction accuracy
15K+
Documents processed monthly
200hrs
Staff hours saved weekly
Our Approach
How we got there
Discovery & Audit
Mapped existing workflows, document types, and downstream system integrations to define the full scope of extraction requirements.
Model Training
Collected and annotated a training dataset of 5,000+ documents across all target categories to train the layout detection and field classification models.
Platform Development
Built the Next.js frontend and Python/FastAPI backend in parallel sprints, with continuous integration testing against the live document corpus.
Pilot & Iteration
Deployed to a single operations team for a 30-day pilot, gathered correction data, and retrained the model before full rollout.
Enterprise Rollout
Scaled the platform to the full organisation with SSO integration, role-based access controls, and dedicated onboarding support.
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