Extraction & classification
OCR + LLM extract and classify any document against your schema.
85% less time per document
Service
Document Processing Automation & Document Intelligence
Turn high-volume documents into structured, decision-ready data — context-aware pipelines that run from client onboarding through the full data journey, not one-off AI chats.
Document processing automation — intelligent document processing (IDP) — turns large volumes of documents into structured, trustworthy data using a pipeline of OCR, LLM extraction against a defined schema, RAG grounding for traceability, and human-in-the-loop validation. Unlike chatting with an AI one file at a time, it is a context-aware system that carries information from client onboarding through the entire data journey.
The commercial impact
Onboarding → insight
Context carried across the full client data journey
OCR + LLM + RAG
Extraction grounded in the source for traceability
6+ industries
Legal, finance, insurance, accounting, pharma & research
Weeks
Typical time to go live, not months
Fixed-price
Scoped to outcomes, ROI agreed up front
Human-in-loop
Review on exceptions, full audit trail
What we automate
Where automation creates value
RAG grounding & traceability
Every output traces back to its source for trust and audit.
Accuracy you can defend
Human-in-the-loop & STP
Straight-through for clean cases, human review for the rest.
Scales without adding headcount
Document intelligence, not document chat
Uploading files into a chatbot does not scale. If you are an accountant with hundreds of clients, you cannot run thousands of separate chats to extract and reconcile information — context is lost the moment each conversation ends.
Real document intelligence builds and carries context. We engineer a "chain of thought" the system follows across the whole journey — from the moment a client is onboarded, through every document they send, to the decision the data ultimately supports. That context is what lets a CPA file taxes, evaluate strategy, and actually make sense of extracted information, instead of re-explaining the situation on every upload.
How our IDP pipeline works
We capture documents with high-fidelity OCR, classify them, then use LLMs to extract data against a defined schema for each document type. Every extracted field is grounded in the source via retrieval (RAG), so outputs are traceable and audit-ready rather than a black box. Low-confidence items route to a human-in-the-loop review step; high-confidence items flow straight through. The structured result is written into the systems you already run — ERP, DMS, or accounting software — with full audit trails and exception handling.
Where we have deployed document intelligence
Document intelligence has been a core Chronexa capability since day one, in production across legal, financial services, insurance, research, patent review, accounting, and pharma. We work under NDA, so we do not name clients — but the experience is real and hands-on: from reserve-study report generation combining OCR and AI, to document and matter workflows for a law firm, to extensive accounting and pharma deployments.
How it works
- 01Map the document journey
We map every step from client onboarding to the decision the data supports — the context your pipeline must carry.
- 02Build the pipeline
OCR + classification + schema-based LLM extraction + RAG grounding, tuned to your document types.
- 03Tune accuracy with human-in-the-loop
We validate against real documents and route exceptions to review until accuracy meets your bar.
- 04Integrate & run
Straight-through processing where confidence is high, written into your ERP/DMS/accounting stack with audit trails.
Why a custom build beats off-the-shelf
- A context-carrying "chain of thought", not stateless one-off chats that forget every session.
- Extraction tuned to your taxonomy and document types — not a generic template.
- Runs inside your environment with access controls — built for NDA and compliance requirements.
- Every field is traceable to its source document, so output is audit-ready.
Frequently asked questions
Isn’t this just ChatGPT for documents?
No. Chatting with an AI handles one file at a time and forgets context when the session ends. We build context-carrying pipelines that span a client’s entire data journey, so the system understands the situation, not just the page in front of it — and it scales to thousands of documents.
Can it handle hundreds of clients and thousands of documents?
Yes — that is exactly the point. Instead of manual chats, documents flow through automated pipelines with classification, extraction, validation, and routing, so volume becomes a strength rather than a bottleneck.
How accurate is it, and can we trust the output?
We extract against a defined schema, ground every field in the source document with RAG for traceability, and route low-confidence items to human review. Accuracy improves as the system sees more of your documents.
Our documents are confidential — how is that handled?
Pipelines run inside your environment with role-based access and audit trails; sensitive documents never leave systems you control. We routinely work under NDA.
Which industries have you done this for?
Legal, financial services, insurance, accounting, pharma, research, and patent review — among others. The same pipeline patterns apply across document-heavy industries.
What does it cost?
Engagements are fixed-price and scoped to the outcome. Every engagement is fixed-price with ROI targets agreed up front, backed by our 90-day ROI guarantee. Book a free audit for a clear price and ROI estimate.