Documents that read themselves.
We build AI that ingests contracts, invoices, forms, PDFs, and unstructured archives, extracts what matters, applies your business rules, and routes the result to the right system or human. Audit ready by default.
Beyond OCR.
Documents that act.
Old school IDP extracts fields. We build systems that understand the document, apply your rules, and trigger the next workflow, all without losing the audit trail.
Intelligent Extraction
Field level extraction that handles tables, signatures, handwritten notes, and arbitrary layouts, even when the document format changes.
Contract Analysis
Clause classification, risk flagging, deviation detection from your master terms, surfaced in a redline review your legal team can act on.
Knowledge Base Q&A
Ask questions across thousands of documents and get cited answers, RAG done right, with hallucination guardrails and source links.
Multi Format Ingestion
PDF, Word, scanned images, email attachments, web forms, and structured exports, one pipeline, predictable output schema.
Workflow Triggers
Extracted data routes to the next system automatically, ERP, CRM, ticket systems, or a human reviewer when confidence drops.
Audit Ready by Default
Every extracted field, decision, and routing action is logged with provenance, versioned, queryable, and exportable to compliance.
Six document piles, and what happens when they read themselves.
Every operations problem has a document pile underneath it. These are the six we get handed most, with the honest numbers on what each one costs while it stays manual.
Accounts Payable That Pays for Itself
The benchmark data is public and brutal: best in class AP teams process an invoice for $2.78 while everyone else pays $12.88, and close in 3 days instead of 17. The gap is extraction, matching, and routing, which is exactly this system. Add the early payment discounts you stop missing and the late penalties you stop paying, and AP automation is usually the fastest payback in the entire AI catalog. The approval chains and policy checks downstream run through our operations automation practice, so the invoice does not just get read, it gets resolved.
Contracts Nobody Has Time to Read
A paralegal abstracts a twenty page contract in about an hour, which is fine until there are four hundred of them in a renewal cycle or a data room. We build contract analysis that classifies clauses, flags deviations from your master terms, and surfaces the risks in a redline your legal team acts on, so the humans read the 5 percent that needs judgment instead of the 100 percent that needs patience. Due diligence, renewals, and vendor paper all follow the same pipeline.
Claims, Applications and Onboarding Packets
Lending files, insurance claims, KYC packets, patient intake: multi document bundles where a skilled person spends thirty minutes verifying what a pipeline can verify in seconds. We extract across the whole packet, cross check the documents against each other and your rules, and route only the genuine discrepancies to a human, which is how review workloads drop from nearly half of all files to a few percent.
The Archive You Have Been Afraid Of
Every company has one: decades of scans, handwritten forms, and formats nobody supports, holding data everyone has agreed to live without. The math on this changed recently. Handwriting recognition jumped from roughly half accurate with traditional OCR to about 95 percent with modern multimodal models, which means the archive that was genuinely impossible in 2023 is a scoped project in 2026. If you priced this before and walked away, the price and the ceiling have both moved.
Ask Your Documents Anything
Policies, runbooks, past proposals, product specs: thousands of documents your team searches by asking whoever has been there longest. We build retrieval systems with cited, source linked answers and hallucination guardrails, so the answer to "what does our contract say about termination" comes with the clause, the page, and the file. The structured output also feeds your data platform, turning the document pile into queryable business data rather than a smarter search box.
Logistics and Trade Paperwork
An international shipment drags fifty plus documents behind it, bills of lading, customs declarations, proofs of delivery, at a manual cost of $25 to $40 each, and customs cycles measured in days. Document AI cuts those processing costs by well over half and turns 48 hour customs preparation into hours. For trade heavy businesses, this pile is not an efficiency project, it is working capital.
Extraction Is the Easy Half.
Plenty of tools can pull fields from a PDF. The value is everything after: validating against your business rules, matching across systems, routing by confidence, and triggering the next action with an audit trail. That downstream half is agent work, built on the guardrails our enterprise AI agents practice ships as standard, and it is the difference between a data extraction demo and a process that runs itself.
Any Language Means Any Language.
Invoices in Urdu, contracts in Arabic, shipping forms mixing three scripts on one page. Our pipelines handle multilingual and low resource language documents natively rather than as an afterthought, backed by our dedicated multilingual AI practice. If your document pile crosses borders, the system should not stop at English.
From paper stack to structured data.
We start with your hardest documents, not your easiest. If the system handles those, the rest is downhill from there.
Document Audit
We sample your real document corpus, including the messy edge cases, and define the output schema your downstream systems need.
Pipeline Build
Multi stage extraction: layout aware OCR, structured field parsing, validation against your business rules, and confidence scoring.
Human in the Loop
Low confidence extractions route to a review UI. Reviewer feedback feeds the next training round, accuracy improves week on week.
Production & Scale
Throughput tuning, drift monitoring, and connectors to your downstream systems. We hand over runbooks and stay on for tuning.
The per-document math, and why 40 percent of these projects miss.
Document AI has the cleanest business case in enterprise AI, which makes the failure rate embarrassing. Here are the real costs, the real payback, and the reasons projects miss it.
A focused pipeline for one document type, one destination system, and human review on exceptions typically runs $15,000 to $40,000. Complex builds spanning multiple document types, cross document validation, and several downstream integrations run $40,000 to $100,000 and beyond. The cost drivers are the messiness of your documents and the number of systems the output must land in, never the page count, which is why our first step is sampling your real corpus, ugly scans included, before anyone quotes anything.
Processing costs at the infrastructure level run cents per page, typically $0.10 to $1.00 per document all in, against $5 to $25 for the same document handled manually. At volume the arithmetic writes itself: an organization processing 5,000 invoices a month spends around $1.2 million a year manually and under $200,000 automated. Every proposal we write includes your projected per document cost next to your measured manual cost, because in this category the business case is genuinely just subtraction.
Because they were scoped on the easy documents and judged on the hard ones. The recurring failure pattern: the vendor demos on clean PDFs while your real corpus is skewed scans and vendor formats nobody warned the model about, nobody plans the exception workflow so low confidence documents pile up in a queue with no owner, and the extracted data lands in a spreadsheet instead of the ERP, recreating the manual step downstream. Our process attacks each one deliberately: we audit your worst documents first, design the human review loop before go live, and treat downstream integration as part of the build rather than a phase two that never comes.
Honestly, usually not, and the companies that treat it purely as a headcount play tend to regret the scoping. What actually happens is the role inverts: teams that spent 80 percent of their time on data entry and 20 percent on exceptions flip that ratio, and the freed hours go to the work that was always being skipped, vendor negotiations, discount capture, dispute resolution, and the month-end analysis nobody had time for. The savings are real, but they show up as throughput and error cost, not usually as empty desks.
A focused single document type pipeline goes live in 3 to 6 weeks, including a parallel run where the system and your team process the same documents, and we compare results before switching over. Enterprise builds with multiple document types, and integrations run 2 to 3 months. The parallel run is non-negotiable in our process, because it is where the accuracy claims stop being a slide and start being your data.
You do, and one part of that matters more than people expect. Every correction your reviewers make in the human-in-the-loop interface trains the next round, which means your team is building a proprietary asset: a system tuned to your documents, your vendors, and your edge cases that no off the shelf tool ships with. That accumulated tuning, along with the pipeline code, prompts, and extracted data, lives in your accounts and repositories. If we part ways, the system keeps learning from your reviewers without us.
Our discovery call has a price of admission: ten of your ugliest documents. We will process them in front of you and show you the confidence scores, because in this category the demo should be your data or it is theater.
Questions about
Process & Document AI
Those are OCR engines, they extract text. We build systems that understand the document, apply your business rules, and trigger the next workflow. We often use Textract or Azure Document Intelligence as one component inside a larger pipeline.
Contracts, invoices, purchase orders, KYC docs, claims forms, government filings, scanned archives, multilingual records, handwritten forms, across PDF, Word, image, and email attachment formats.
Field level accuracy of 95%+ on clean inputs, 85 95% on messy real world documents. We instrument confidence per field and route low confidence cases to human review, so the system gets better over time without hidden errors.
Yes. We deploy into private cloud or on premise environments, with PII redaction, encrypted storage, and full audit trails. See our on premise AI offering for regulated workloads.
Wherever you need it, your ERP, CRM, data warehouse, ticketing system, or a structured database we build for you. We do bidirectional integration with all major enterprise systems.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Send us a sample of your hardest documents and we'll show you what's possible, without the sales theater.
info@croncore.com