Capability brief · Doc IntelligenceCapability 03 of 14

Turn Every Document into Structured, Actionable Data -- in Seconds, Not Days

Combine state-of-the-art OCR with large language models to extract, classify, and validate data from invoices, contracts, claims, compliance filings, and any unstructured document -- at enterprise scale with human-grade accuracy.

Book a call with an engineer How it’s built

95%+

Straight-through processing rate on standard document types

80%

Reduction in manual data entry labor costs

< 4 sec

Average end-to-end extraction time per document

99.5%

Field-level accuracy with Human-in-the-Loop validation

01Overview

Document Intelligence

What it is

Document Intelligence is an end-to-end pipeline that ingests unstructured and semi-structured documents -- PDFs, scanned images, faxes, emails with attachments, spreadsheets, and handwritten forms -- and produces clean, validated, structured data ready for downstream systems. The pipeline chains optical character recognition (OCR), layout analysis, document classification, LLM-powered entity and field extraction, cross-field validation, and Human-in-the-Loop quality assurance into a single managed service. It handles the full spectrum from pristine born-digital PDFs to low-resolution mobile phone captures of crumpled receipts.

Why it matters

Enterprises still drown in paper-adjacent processes. A mid-market insurer may process 200,000 claims documents per year; a logistics company may handle 500,000 bills of lading and proof-of-delivery images; a healthcare system may manage millions of explanation-of-benefits forms. Each document that requires a human to read, re-key, and verify represents $3-$12 in direct labor cost and introduces a 2-5% error rate that compounds into downstream rework, compliance findings, and revenue leakage. Legacy OCR tools capture characters but miss context -- they cannot distinguish a policy limit from a deductible, an invoice line item from a remittance credit, or a contract amendment from a superseded clause. Document Intelligence closes that gap by applying language understanding on top of character recognition.

How Neume does it differently

Unlike legacy IDP vendors who sell a platform and leave customers to build extraction templates, Neume delivers document intelligence as a managed, outcome-based service. We own the entire pipeline from ingestion to validated output, absorbing the complexity of template creation, model fine-tuning, edge-case handling, and QA staffing. Our Human-in-the-Loop specialists operate as an extension of your operations team -- they do not just flag exceptions, they resolve them. We continuously retrain extraction models on your actual document corpus, meaning accuracy improves over time rather than degrading as document formats evolve. And because we charge on throughput rather than per-seat licensing, the economics scale linearly with your volume.

02Core capabilities

What this system can do.

Multi-Format Ingestion

Accepts documents from any source -- email attachments, SFTP drops, API uploads, cloud storage, scanner outputs, and mobile captures. Handles PDFs (native and image-based), TIFF, JPEG, PNG, HEIC, Microsoft Office formats, and raw email bodies. Automatic format normalization, de-skewing, noise reduction, and page splitting ensure consistent downstream processing regardless of input quality.

Intelligent Document Classification

Automatically identifies document type (invoice, contract, claim form, compliance filing, correspondence, etc.) and routes to the appropriate extraction pipeline. Classification models are trained on your specific document taxonomy and achieve 98%+ accuracy, eliminating the manual sorting step that typically consumes 15-20% of document processing labor.

OCR + LLM Hybrid Extraction

Chains high-fidelity OCR engines with large language models that understand document semantics, not just character shapes. The OCR layer captures every character, table cell, and checkbox; the LLM layer interprets context to map extracted text to the correct schema fields. This hybrid approach handles ambiguous layouts, inconsistent terminology, and multi-language documents that defeat template-based extraction.

Table and Line-Item Extraction

Purpose-built layout analysis identifies and reconstructs complex tables -- including those with merged cells, spanning headers, or no visible gridlines. Extracts individual line items with their associated quantities, unit prices, descriptions, tax amounts, and reference codes. Critical for invoices, purchase orders, medical bills, and financial statements where tabular data carries the majority of business value.

Cross-Field Validation and Business Rules

Extracted data passes through configurable validation rules -- mathematical checks (do line items sum to the invoice total?), referential checks (does this vendor ID exist in the master list?), format checks (is this a valid date, currency code, or policy number?), and semantic checks (does the contract effective date precede the termination date?). Exceptions are flagged for Human-in-the-Loop review rather than silently passed through.

Continuous Model Improvement

Every document processed -- and every Human-in-the-Loop correction -- feeds back into model retraining. Extraction accuracy improves week over week as the system learns your specific document formats, vendor naming conventions, and edge cases. Monthly accuracy reports quantify improvement trends and identify remaining extraction gaps.

03Architecture

How it’s built.

The Document Intelligence pipeline is structured as a series of composable processing stages, each independently scalable and observable. Documents flow through ingestion, preprocessing, classification, extraction, validation, and output stages. Human-in-the-Loop review is integrated as a parallel quality gate rather than a sequential bottleneck, meaning high-confidence extractions proceed immediately while low-confidence documents route to specialists.

Ingestion Layer

Receives documents from all configured sources, assigns tracking IDs, logs metadata (source, timestamp, file size, page count), and queues for processing. Handles deduplication, burst traffic management, and priority routing.

Multi-channel connectors (email, API, SFTP, cloud storage)
Format detection and normalization engine
Document deduplication and version tracking
Priority queue with SLA-based routing

Preprocessing Layer

Prepares raw document images for optimal extraction. Applies image enhancement, de-skewing, noise removal, binarization, and page segmentation. Splits multi-document files (e.g., a single PDF containing an invoice, a packing slip, and a BOL) into individual logical documents.

Image enhancement and de-skewing engine
Page segmentation and document boundary detection
Resolution normalization for mobile captures
Handwriting vs. print classification

Classification and Extraction Layer

The core intelligence layer. Document classifiers identify document type and route to specialized extraction models. OCR engines capture text; layout analysis models reconstruct spatial relationships (tables, headers, sections); LLMs map extracted text to structured schema fields using semantic understanding.

Document classification models (custom-trained per client)
Multi-engine OCR with confidence scoring
Layout and table structure analysis
LLM-powered field extraction and entity recognition
Schema mapping and normalization engine

Validation and QA Layer

Applies business rules, cross-field validation, and confidence thresholds. High-confidence extractions pass through automatically; low-confidence fields or rule violations route to Human-in-the-Loop specialists for review and correction. All corrections are logged for retraining.

Configurable business rule engine
Confidence threshold management
Human-in-the-Loop review interface
Correction tracking and feedback loop
Audit trail and compliance logging

Output and Integration Layer

Delivers structured, validated data to downstream systems via API, webhook, file drop, or direct integration with ERP, claims, and accounting platforms. Supports JSON, XML, CSV, and custom schema formats. Provides real-time status callbacks and batch reporting.

RESTful API and webhook delivery
ERP and core system connectors
Output schema mapping and transformation
Real-time processing status dashboard
Batch reconciliation and audit reporting

Integration approach

Document Intelligence integrates with your existing document inflows -- it does not require you to change how documents arrive. We tap into your current email inboxes, SFTP directories, or cloud storage buckets and deliver structured output directly into your systems of record. The integration is API-first, with pre-built connectors for major ERP, accounting, claims, and contract management platforms. Most clients are processing documents within 2-3 weeks of engagement start, with full production integration completed within 6-8 weeks.

04Cross-industry deployments

Doc Intelligence in production.

Deployment 01InsuranceClaims Document Processing

The problem

A mid-market P&C carrier processes 150,000+ claims per year. Each claim generates 8-15 documents -- first notice of loss forms, police reports, medical bills, repair estimates, subrogation letters, coverage verification requests. Claims adjusters spend 40% of their time reading, classifying, and re-keying document data into the claims management system instead of investigating and settling claims.

28% reduction in Loss Adjustment Expenses; $4.2M annual savings on a $500M book

How it works: Documents arriving via email, fax, and portal upload are automatically ingested, classified by document type, and routed to claim-specific extraction pipelines. Medical bills are parsed into CPT codes, diagnosis codes, provider details, and charges. Repair estimates are extracted into line-item parts and labor. Police reports are structured into incident details, parties involved, and narrative summaries. Extracted data is validated against claim records and auto-populated into the claims system. HitL specialists review low-confidence extractions and handle handwritten forms.
Outcome: Claims adjusters reclaim 40% of their time for investigation and settlement. Average claim cycle time reduced by 6 days. Data entry errors that previously caused 3-5% claims leakage are virtually eliminated.

Deployment 02Financial ServicesInvoice and Accounts Payable Automation

The problem

A regional bank's shared services center processes 80,000 vendor invoices per month across 14 subsidiaries. Invoices arrive in wildly inconsistent formats -- some born-digital PDFs, others scanned paper, many with handwritten PO references. AP clerks manually key header data and line items into the ERP, with a 4.1% error rate that causes duplicate payments, missed early-pay discounts, and month-end reconciliation delays.

87% straight-through processing; 4.1% error rate reduced to 0.3%

How it works: Invoices are ingested from email inboxes and a supplier portal. The system classifies each document (invoice vs. credit memo vs. statement vs. remittance advice), extracts header fields (vendor, invoice number, date, PO reference, total) and line items (description, quantity, unit price, tax, GL code). Extracted data is validated against purchase orders and vendor master records. Three-way matching (PO, receipt, invoice) is automated. Exceptions route to AP staff with pre-populated review screens showing the original document alongside extracted data.
Outcome: Straight-through processing rate reaches 87% for invoices with matching POs. AP headcount reduced by 12 FTEs through attrition. Early-pay discount capture increases by $1.8M annually. Month-end close accelerated by 3 days.

Deployment 03HealthcareExplanation of Benefits and Remittance Processing

The problem

A multi-site physician practice group receives 25,000 EOBs and ERA documents monthly from 40+ payers. Each payer uses a different format. Revenue cycle staff manually match EOBs to patient accounts, identify underpayments and denials, and post adjustments. The manual process introduces a 72-hour lag between payment receipt and posting, obscuring the practice's true cash position and delaying denial follow-up.

Payment posting accelerated from 72 hours to same-day; 2.4 point net collection rate improvement

How it works: EOBs and remittance advices are captured from payer portals, clearinghouse feeds, and paper mail (via scan). Document Intelligence classifies each document by payer and document type, then extracts patient identifiers, CPT codes, billed amounts, allowed amounts, adjustments, denial codes, and check/EFT details. Extracted data is matched to open claims in the practice management system. Underpayments and denials are automatically flagged with root-cause categorization (coding error, auth missing, timely filing, etc.) for prioritized follow-up.
Outcome: Payment posting lag reduced from 72 hours to same-day. Denial identification and categorization happens in real time rather than on a 2-week lag. Net collection rate improves by 2.4 percentage points, representing $3.1M in annual recovered revenue.

Deployment 04LegalContract Review and Clause Extraction

The problem

A corporate legal department manages 6,000+ active vendor and customer contracts. When evaluating M&A targets, assessing regulatory exposure, or renegotiating renewals, attorneys must manually review hundreds of contracts to identify specific clauses -- change of control, indemnification, limitation of liability, auto-renewal, data processing terms. A single due diligence exercise can consume 400+ attorney hours.

90% reduction in attorney review hours for due diligence; full contract portfolio searchable within 2 weeks

How it works: The contract corpus is ingested and each document is classified by contract type (MSA, SOW, NDA, lease, employment agreement). LLM-powered extraction identifies and categorizes key clauses, extracts critical terms (effective dates, termination notice periods, liability caps, governing law, assignment restrictions), and flags non-standard or high-risk language against the organization's playbook. Results are delivered as a structured database that attorneys can query, filter, and export.
Outcome: Due diligence contract review time reduced from 400 attorney hours to 40 hours of focused review on flagged items. Non-standard clause identification that previously took weeks happens in hours. The structured contract database becomes a permanent asset for ongoing portfolio management.

Deployment 05Logistics & Supply ChainShipping Document and Bill of Lading Processing

The problem

A third-party logistics provider handles 12,000 shipments per week, each generating 4-6 documents -- bills of lading, commercial invoices, packing lists, certificates of origin, customs declarations. Operations staff manually key shipment data from these documents into the TMS, creating a 6-8 hour lag between document receipt and shipment visibility. Keying errors (wrong weight, wrong HS code, wrong consignee) cause customs holds, delivery failures, and detention charges averaging $180K per month.

62% reduction in customs holds; $1.7M annual savings in detention and demurrage charges

How it works: Shipping documents are captured from carrier portals, email, and EDI feeds. Document Intelligence classifies each document, extracts shipment-level data (origin, destination, consignee, shipper, weight, piece count, HS codes, container numbers), and cross-validates across related documents for the same shipment. Discrepancies (e.g., weight on BOL does not match commercial invoice) are flagged before customs filing. Extracted data is pushed directly into the TMS and customs brokerage systems.
Outcome: Document-to-TMS lag reduced from 6-8 hours to under 15 minutes. Customs hold rate drops by 62% due to pre-submission validation. Detention and demurrage charges reduced by $140K per month. Operations team reallocated from data entry to exception management and customer service.

Deployment 06Compliance & RegulatoryKYC and AML Document Verification

The problem

A fintech lender onboards 3,000 small business borrowers per month. Each application requires verification of 8-12 documents -- government-issued IDs, articles of incorporation, beneficial ownership declarations, bank statements, tax returns, and business licenses. Compliance analysts spend 35 minutes per application manually verifying document authenticity, extracting entity details, and cross-referencing against sanctions lists and corporate registries. The manual process creates a 3-day onboarding bottleneck that directly impacts conversion rates.

Same-day onboarding for 70% of applications; 75% reduction in per-application review time

How it works: Applicant-uploaded documents are ingested, classified, and validated for completeness against the required document checklist. Identity documents are extracted for name, date of birth, document number, and expiration. Corporate documents are parsed for entity name, jurisdiction, officer and director details, and ownership percentages. Bank statements are extracted for account holder verification and cash flow indicators. All extracted entities are automatically cross-referenced against OFAC, EU sanctions, PEP lists, and corporate registries. Discrepancies and high-risk flags route to compliance analysts with pre-populated review screens.
Outcome: Per-application review time reduced from 35 minutes to 8 minutes. Onboarding cycle compressed from 3 days to same-day for 70% of applications. False positive rate on sanctions screening reduced by 45% through better entity extraction. Compliance team handles 3x volume without additional headcount.

05Comparison

Why not off the shelf?

Legacy OCR Tools (ABBYY, Kofax, OpenText)

Limitation

Template-based extraction that requires manual template creation for every document layout variant. When a vendor changes their invoice format or a payer updates their EOB layout, templates break and require IT intervention. No semantic understanding -- the system captures characters at coordinates but cannot interpret meaning, leading to brittle extraction that fails on layout variation.

Neume advantage

LLM-powered extraction understands document semantics, not just character positions. No templates to build or maintain -- the system adapts to layout variations automatically. When a vendor changes their invoice format, extraction continues working because the LLM understands that 'Amount Due,' 'Total,' and 'Balance' all refer to the same concept. Template maintenance cost drops to zero.

Cloud IDP Platforms (AWS Textract, Google Document AI, Azure Document Intelligence)

Limitation

Strong OCR engines but limited domain-specific extraction out of the box. Customers must build their own classification logic, validation rules, exception handling, and human review workflows. Requires in-house ML engineering talent to fine-tune models, manage training data, and maintain pipelines. The 'last mile' from raw extraction to production-grade structured data remains the customer's problem.

Neume advantage

Neume delivers the complete pipeline as a managed service -- we own classification, extraction, validation, exception handling, and human review end to end. No ML engineering hire required. We leverage best-of-breed OCR engines under the hood but add the domain-specific intelligence, validation logic, and HitL operations that turn raw character output into trusted, production-ready data.

In-House AI/ML Teams

Limitation

Building document intelligence in-house requires hiring OCR engineers, ML engineers, data annotators, and QA staff -- a team of 5-8 people costing $800K-$1.5M annually before infrastructure costs. Model development takes 6-12 months before production-grade accuracy is achieved. Ongoing maintenance, retraining, and edge-case handling consume 40-60% of team capacity permanently.

Neume advantage

Neume provides production-grade document intelligence within weeks, not months, at a fraction of the cost of an in-house team. Our models are pre-trained on millions of documents across industries and fine-tuned on your specific corpus. You get the output of a world-class ML team without the hiring, management, and retention challenges.

Offshore BPO / Manual Data Entry

Limitation

Human data entry achieves reasonable accuracy (96-98%) but does not scale economically. Per-document costs of $0.50-$2.00 become untenable at high volumes. Turnaround times of 24-72 hours create processing bottlenecks. Quality degrades during volume spikes. No structured audit trail or consistent validation logic.

Neume advantage

AI-first processing with targeted human review achieves higher accuracy (99.5%) at 60-80% lower per-document cost. Sub-minute processing for high-confidence documents eliminates turnaround bottlenecks. Quality is consistent regardless of volume. Every extraction carries a full audit trail with confidence scores, validation results, and reviewer actions.

RPA-Based Document Processing (UiPath Document Understanding, Automation Anywhere)

Limitation

RPA tools bolt document processing onto robotic process automation workflows. Extraction quality is limited by underlying OCR engines with minimal semantic understanding. Tightly coupled to specific UI workflows -- when the target application changes, the robot breaks. High maintenance overhead with fragile automation scripts that require constant updating.

Neume advantage

Document Intelligence operates as a standalone, API-driven service decoupled from UI-level automation. Extraction quality is powered by LLMs that understand document semantics rather than relying on pixel-level screen interactions. The service integrates at the data layer, making it resilient to application UI changes and composable across any downstream system.

06Implementation

What deployment looks like.

012-3 weeks
026-8 weeks
0312-16 weeks

012-3 weeks
Proof-of-value on a single document type within 2-3 weeks.
026-8 weeks
Production deployment for initial document types within 6-8 weeks.
0312-16 weeks
Full multi-document-type rollout typically completed within 12-16 weeks, depending on the number of document types and integration complexity.

Prerequisites

Sample documents (50-100 per document type) for initial model training and accuracy benchmarking
Target data schema defining the fields to extract for each document type
Access to document inflow channels (email inboxes, SFTP, portal, or cloud storage)
API credentials or access to downstream systems of record for output delivery
Designated business stakeholder to define validation rules and review accuracy reports

Deliverables

Configured and trained extraction models for each document type in scope
Integration connectors for document ingestion and structured data delivery
Human-in-the-Loop QA process staffed and operational, with SLA-backed turnaround
Real-time processing dashboard with volume, accuracy, and exception metrics
Monthly accuracy and throughput reports with improvement trend analysis
Runbook covering escalation procedures, model retraining triggers, and SLA definitions

Human in the loop

Human-in-the-Loop review is integral to the pipeline, not bolted on. Every extraction carries a confidence score. Documents or fields falling below configurable confidence thresholds route to trained specialists who review the original document alongside extracted data, correct errors, and confirm output. These corrections feed directly into model retraining. For sensitive document types (contracts, compliance filings), 100% human review can be configured regardless of confidence scores. Our HitL team operates across time zones to support near-real-time review SLAs, with typical turnaround under 30 minutes for priority documents.

07Security & compliance

Engineered for trust.

Data Encryption

All documents are encrypted in transit (TLS 1.3) and at rest (AES-256). Encryption keys are managed via customer-dedicated key management, with optional customer-managed keys (BYOK) for organizations with strict key custody requirements. Decrypted document content exists only in memory during active processing.

Access Control and Authentication

Role-based access control (RBAC) enforces least-privilege access across the pipeline. Human-in-the-Loop reviewers access only the documents assigned to their queue and cannot export or download originals. All access is authenticated via SSO/SAML integration with the client's identity provider. Multi-factor authentication is enforced for all operator and administrative access.

PII and PHI Handling

Configurable PII/PHI detection automatically identifies and redacts sensitive fields (SSN, date of birth, account numbers, protected health information) in logs, dashboards, and non-essential pipeline stages. For healthcare document processing, the pipeline operates within a HIPAA-compliant environment with signed BAAs, minimum necessary access controls, and PHI-specific audit logging.

Data Residency and Retention

Documents are processed and stored within customer-specified geographic regions (US, EU, or custom). Configurable retention policies automatically purge original documents and extracted data after processing, with retention periods aligned to client compliance requirements. Clients can enforce zero-retention policies where documents are deleted immediately after structured output delivery.

Audit Trail and Compliance Logging

Every document processed generates a complete, immutable audit trail: ingestion timestamp, classification result, extraction confidence scores, validation outcomes, HitL reviewer identity and actions, output delivery confirmation, and eventual deletion. Audit logs are retained independently of document data and are available for regulatory examination, SOC 2 audits, and internal compliance reviews.

SOC 2 and Industry Certifications

Neume maintains SOC 2 Type II certification covering the Document Intelligence pipeline. For regulated industries, the pipeline supports compliance with HIPAA (healthcare), GLBA and SEC 17a-4 (financial services), GDPR and CCPA (data privacy), and industry-specific document retention requirements. Compliance documentation and penetration test reports are available under NDA.

08FAQ

Common questions.

How many document types can the system handle, and how long does it take to add a new one?

There is no fixed limit on document types. Each new document type requires 50-100 sample documents for initial model training. From sample receipt to production-ready extraction typically takes 1-2 weeks, depending on document complexity. Simple, standardized documents (utility bills, standard invoices) can be production-ready in days; complex, variable documents (multi-section contracts, handwritten inspection reports) may take closer to 2-3 weeks.

What accuracy can we expect, and how is it measured?

Field-level accuracy typically reaches 93-96% for automated extraction alone, rising to 99.5%+ with Human-in-the-Loop validation. Accuracy is measured at the individual field level (not the document level) against a ground-truth dataset established during onboarding. We provide monthly accuracy reports broken down by document type, field, and confidence band, so you have full visibility into where the system excels and where it is still improving.

Can the system handle handwritten documents or poor-quality scans?

Yes. The preprocessing layer applies image enhancement, de-skewing, and noise reduction to improve legibility. Handwriting recognition models handle common handwritten elements (signatures, dates, check-box marks, margin notes). For heavily degraded documents, the system assigns low confidence scores and routes to Human-in-the-Loop review rather than producing unreliable output. Accuracy on handwritten content is typically 85-90%, compared to 95%+ on printed text.

How does pricing work?

Pricing is based on document processing volume -- a per-document fee that varies by document complexity and the level of Human-in-the-Loop review required. There are no per-seat licenses, no platform fees, and no charges for model retraining. This means costs scale linearly with your actual usage, and you are not penalized for adding more users or downstream integrations.

What happens if a document type changes format unexpectedly?

LLM-based extraction is inherently resilient to format changes because it understands document semantics rather than relying on fixed templates. Minor format changes (field repositioning, font changes, added fields) are handled automatically. Major format overhauls (complete document redesigns) may cause a temporary accuracy dip that triggers increased HitL review. Our team monitors accuracy metrics continuously and retrains models proactively when format drift is detected, typically restoring full accuracy within 3-5 business days.

Can we keep sensitive documents entirely on-premise?

Yes. For clients with strict data sovereignty or air-gapped environment requirements, Document Intelligence can be deployed in a hybrid architecture where OCR and extraction run within the client's infrastructure and only anonymized metadata is transmitted externally. Fully on-premise deployment is also available for enterprise clients. Both options maintain the same accuracy and SLA commitments as the standard cloud deployment.

Where this system ships

Insurance

Claims documents, ACORD forms, loss runs, policy endorsements, and underwriting submissions are the lifeblood of insurance operations -- all unstructured, high-volume, and perfectly suited to AI extraction.

Financial Services

Invoices, loan applications, KYC documents, tax forms, and regulatory filings generate massive document processing workloads where extraction accuracy directly impacts financial outcomes.

Healthcare

EOBs, clinical documentation, prior authorization forms, and medical records require document intelligence that handles PHI with HIPAA-grade security while extracting complex medical coding data.

Legal

Contract review, due diligence document sets, court filings, and regulatory submissions benefit from LLM-powered clause extraction and structured contract analytics at scale.

Logistics & Supply Chain

Bills of lading, customs declarations, commercial invoices, and proof-of-delivery documents flow in massive volumes where extraction speed directly impacts shipment visibility and customs clearance.

Next step

Document Intelligence is not a product you install -- it is an operational capability you deploy. Neume combines AI extraction technology with trained human specialists and continuous model improvement into a single managed service. We do not hand you a platform and wish you luck; we own the extraction outcome and are measured on accuracy, throughput, and the labor hours we eliminate from your operations.

The combination of LLM-powered semantic extraction with embedded Human-in-the-Loop operations is what separates Neume from both technology vendors and traditional BPOs. Technology vendors deliver tools without operational capacity. BPOs deliver labor without technological leverage. Neume delivers both -- AI that handles the volume and humans who handle the exceptions -- unified under a single SLA with a single accountable partner.

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