Docprocessing360
An AI-powered smart document processing platform that extracts, classifies, and routes business documents automatically — reaching 12,000 users in 60 days.
Project Snapshot
Businesses process thousands of documents every week — invoices, contracts, forms, reports — and most of it is still done manually. Someone opens the PDF, reads it, decides where it goes, and copies key fields into a spreadsheet. It's slow, error-prone, and doesn't scale. The founder needed a platform that could ingest any document, understand what it contains, extract the right fields, and route it to the right workflow — without human review.
We built Docprocessing360 as a full-stack AI SaaS platform with a three-stage processing pipeline: ingestion (PDF/image upload), extraction (GPT-4 powered OCR + structured field parsing), and routing (rule-based workflow engine). The platform ships with a no-code rule builder so non-technical teams can configure routing logic themselves. We built the full product in 4 weeks — including admin dashboards, team management, API access, and Stripe billing — and the founder launched to a waitlist of 3,000 on day one.
Outcomes that speak for themselves.
Every feature, shipped to production.
AI Document Extraction
GPT-4 powered extraction pipeline that reads PDFs, scanned images, and Word docs. Identifies document type, extracts structured fields (dates, amounts, names, references), and returns clean JSON.
Automatic Document Classification
The platform classifies incoming documents into pre-configured categories — invoices, contracts, purchase orders, KYC forms — with confidence scores and fallback to a human review queue.
No-Code Routing Rules Engine
Non-technical teams can build routing workflows with a visual rule builder. 'If document type = Invoice AND amount > $10,000, route to Finance Manager' — no code required.
Multi-Format Ingestion
Upload via drag-and-drop, email inbox integration, or REST API. Supports PDF, PNG, JPEG, TIFF, DOCX, and multi-page documents. Batch upload processes up to 500 documents at once.
Team Workspace & Roles
Organisation-level accounts with role-based access control. Admins manage templates and routing rules; operators process documents; viewers get read-only access to completed documents.
Human Review Queue
Documents with low extraction confidence are flagged and routed to a human review interface. Reviewers correct fields and approve — corrections feed back into the model to improve accuracy.
REST API & Webhooks
Full REST API lets engineering teams integrate Docprocessing360 into existing systems. Webhooks fire on document completion, routing events, and review queue additions.
Audit Trail & Compliance Logs
Every extraction, classification, routing decision, and manual edit is logged with timestamps and user IDs. Exportable audit logs support SOC 2 and ISO 27001 compliance needs.
Stripe Billing & Usage Metering
Usage-based billing tracked per page processed. Monthly credit bundles with overage billing wired through Stripe. Self-serve upgrade, downgrade, and cancellation in the customer portal.
How we went from brief to live.
Technical Discovery (Week 1, Days 1–3)
Mapped the full data flow — from document ingestion through AI processing to routing. Agreed on GPT-4 as the extraction engine and designed the JSON schema for extracted fields.
Design Sprint (Week 1, Days 3–5)
Figma mockups for the dashboard, document viewer, extraction results UI, rule builder, and team settings. Founder approved all screens in a single review session.
AI Pipeline Build (Week 2)
Built the core extraction pipeline — file ingestion, GPT-4 prompt engineering, structured output parsing, and confidence scoring. Validated against 200 real documents provided by the founder.
Frontend & Dashboard (Week 3)
Built the full React dashboard — document upload, extraction results view, routing rule builder, human review queue, and audit logs. All screens wired to the live API.
Billing, API & Integrations (Week 4, Days 1–3)
Integrated Stripe usage-based billing, built the public REST API with API key management, and added webhook delivery with retry logic.
Load Testing & Launch (Week 4, Days 4–5)
Tested the pipeline under 500-document batch loads. Fixed a concurrency issue in the processing queue. Deployed to AWS and handed over to the founder — who launched the same day.
Technologies powering this product.
Frontend
AI / ML
Backend
Infrastructure
We went from a manually run document process to a fully automated pipeline in 4 weeks. The extraction accuracy was better than I expected, and the no-code rule builder meant our operations team could configure it themselves without involving engineering.
Docprocessing360 Founder
B2B SaaS / Document Automation
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