Document Intelligence & Classification
Turn unstructured documents — contracts, statements, forms, claims — into clean, structured, validated data your systems can act on. Built to an accuracy bar, in production, in weeks.
Manual document operations don’t scale in a regulated enterprise
High-volume document workflows in finance, healthcare, and insurance are still largely manual — slow, expensive, and inconsistent. The usual "AI" fix is a demo that works on ten clean PDFs and falls over on the real, messy corpus. Getting to a system you can trust in production is the hard part.
Accuracy you can’t verify
Without an evaluation harness, "it looks right" is not a number a risk team can sign off on.
The long tail breaks pilots
Edge-case layouts, scans, and languages are where naive extraction quietly fails.
Integration is the real cost
Extraction is easy; landing validated data in the systems of record, with audit trails, is the work.
Extraction, classification, and validation — evaluated to a bar
We build document pipelines that extract, classify, and validate at scale — on modern LLMs with batch processing and context caching for cost, wrapped in an evaluation harness so accuracy is a measured number, not a hope. Validated output lands in your systems with a full audit trail.
Extraction & Classification
Pull structured fields and route documents by type across mixed, messy, real-world inputs.
Evaluation Harness
Every model change is scored against a labelled set, so accuracy is measured before it ships.
Multi-Format Ingestion
PDFs, scans, images, and email attachments — including the awkward long tail.
Validation & Human-in-the-Loop
Low-confidence cases are flagged for review instead of guessed, keeping quality auditable.
Batch & Context Caching
Engineered for cost at volume, not just a slick single-document demo.
Systems Integration
Validated output flows into your ERP, core, or workflow tools with an audit trail.
What you get
Throughput without headcount
Process the full corpus continuously instead of hiring for peaks.
Accuracy you can sign off
A measured accuracy bar your risk and compliance teams can stand behind.
Faster cycle times
Documents that took days move in near-real-time.
Auditability
Every extraction and decision is logged and traceable.
Lower unit cost
Batch and caching keep per-document cost sane at enterprise volume.
Production in weeks
A working, integrated pipeline in a quarter, not a year.
Recently shipped
Real, in-production work. Clients are described by sector and scale, per NDA — we don't publish numbers we can't stand behind.
A production document classifier processing 5,000+ documents/month on Gemini Pro (batch API + context caching), built to a ≥95% accuracy target and live in 8 weeks.
Bank-statement automation across 13 banks, integrated in ~3 months and live with production users on a recurring monthly workload.
Have a document backlog that won’t scale?
How we work
Feasibility in weeks, production in a quarter — not a nine-month science project.
Step 1: Feasibility
Test the real corpus, define the accuracy bar and ROI, and give you an honest go/no-go — typically in weeks.
Step 2: Build & Evaluate
Develop the pipeline and score it against a labelled set until it clears the bar.
Step 3: Deploy
Ship to production, integrated with your systems of record and monitored.
Step 4: Operate & Improve
Watch accuracy in production, handle drift, and expand to new document types.
Let's Start Your AI Journey
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Location
Sheridan, WY, USA • Barcelona, Spain • Lisbon, Portugal
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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