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Document Extraction
Every document you own is sitting on value you can't access.
Document extraction turns static files — PDFs, scanned images, forms, contracts, invoices, medical records — into structured, queryable, actionable data. Not by hand. Not at a pace that bottlenecks your team. Automatically, at scale, against documents that arrive in the format they were always going to arrive in.
Athena AI builds these systems to run on your infrastructure. Your documents stay in your environment. Your extracted data feeds your systems directly. No SaaS subscription with per-page pricing that compounds as your volume grows.
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What you actually get
Document extraction isn't OCR. It's a layer of structured intelligence that sits between raw files and the systems that need their contents.
Outcome
What it means
Structured data from unstructured files
Invoices become line-item records. Contracts become clause libraries. Forms become database rows. PDFs become searchable, filterable datasets.
Reduced manual processing headcount
A trained extraction pipeline processes hundreds of documents per hour without errors, fatigue, or variability. The headcount you spend on document handling redirects to higher-value work.
What document extraction actually does
Most people think of document extraction as 'the system reads the PDF.' That's a quarter of the problem — and the easy quarter.
The hard parts are what comes after reading: understanding what kind of information you're looking at, knowing where the relevant fields are regardless of layout variation, tolerating scan noise, and producing output structured enough to flow into the systems that need it.
The difference between reading and extracting
Reading gives you
Extraction gives you
"Net 30" somewhere in a PDF
payment_terms: "Net 30" mapped to invoice ID 88421
The difference matters because downstream systems — ERPs, EHRs, claims platforms, contract databases — need structured records, not text files.
The five things an extraction pipeline has to do
An extraction pipeline is a sequence of steps, each handling a specific part of the problem. Here's what happens, in order, for every document:
Extraction is five layers. Each layer is engineered against your document taxonomy, accuracy requirements, and downstream integration surface. Substitution decisions are explicit and documented.
Reference deployment
Metric
Target
Reference Hardware
Field-level accuracy (structured docs)
> 93% F1 on held-out test set
CPU sufficient for most pipelines; GPU accelerates high-volume OCR
Processing throughput
200–400 pages/min per worker
On-premise GPU server or 4-core CPU worker
End-to-end latency (single doc, p99)
< 8 seconds
Synchronous API; async batch pipeline decoupled
A model that performs in a proof-of-concept is not a production system. The work that determines whether an extraction pipeline is healthy six months in happens here — in monitoring, drift handling, security, integration, and the operational practices that quietly keep accuracy where it needs to be.
Integration surface
Capability matters less than how it plugs in. Our default integration surfaces:
Synchronous API: REST and gRPC. POST a document, receive structured JSON within the latency SLA. OpenAPI spec shipped with every deployment.
Async batch pipeline: Kafka or MQTT queue-based ingestion. Documents submitted to a queue, processed at configured throughput, results written to downstream sink.
Data formats: JSON Schema-validated field extractions, CSV export for bulk analytics, COCO-compatible annotation export, audit-trail records in structured log format.
Downstream sinks: Data warehouses (Snowflake, BigQuery, Redshift, on-prem Postgres / ClickHouse), ERP / EHR / CLM systems via REST API, SIEM (Splunk, Sentinel, Elastic), BI tools.
Human review interface: configurable review queue surfacing documents requiring human attention alongside extracted fields, confidence scores, and the specific validation failures that triggered escalation. Reviewer corrections feed back into the training loop.
SDKs: Python, TypeScript, C++. Example clients and integration templates shipped with every deployment.
The integration surface is a first-class deliverable, not documentation written after the model ships.
MLOps and drift handling
Drift detection as a continuous SLI
Per-document-type accuracy is monitored continuously. Field-level F1, confidence score distributions, and validation failure rates alert when they drift past threshold — before quality issues surface in downstream data.
Scheduled retraining
Retraining cycles tied to drift signals and ground-truth feedback loops. Active learning (model surfaces low-confidence documents for human labeling) or customer-side annotation pipelines. New document types trigger a classification model update cycle before they reach extraction.
Versioning and rollout
Models versioned, signed, rolled out via canary deployment — new model runs in shadow on a subset of documents, field-level accuracy compared against production, promotion gated on regression checks. A model that improves average accuracy but degrades on a specific document type doesn't promote.
Error rates you can measure
Human data entry runs at roughly 1–4% error rates you rarely catch until they cause a downstream problem. Automated extraction surfaces its own confidence scores — you know where to apply human review before the error propagates.
No per-page SaaS billing
Cloud document AI charges per page, per call, per API hit. At 50,000 documents per month across a multi-year horizon, the math turns against you. Owned systems amortize.
Data stays in your environment
No document leaves your network unless you choose to send it. Critical for healthcare records, legal documents, financial data, and any regulated content.
Where this deploys
Document extraction is one capability with many shapes. Same underlying system, different document types and downstream questions.
Legal & Contracts. Clause extraction, obligation tracking, key date identification, counterparty normalization across contract libraries spanning thousands of files.
Healthcare & Clinical. Medical record abstraction, prior authorization processing, clinical trial data extraction, insurance claim parsing. Compatible with HIPAA, PHIPA, and PIPEDA.
Insurance. Policy document parsing, claims intake, loss run extraction, underwriting questionnaire processing.
Logistics & Supply Chain. Bill of lading extraction, customs documentation, shipping manifest parsing, supplier invoice reconciliation.
Government & Public Sector. Permit applications, regulatory filings, benefits intake, freedom of information request processing.
Why Athena AI
Private deployment by default.
We don't run a SaaS platform. We ship engineered systems to your hardware — edge, on-premise, hybrid, or fully air-gapped. You own the system. You own the data. You own the upgrade path.
Cross-vertical experience.
The same underlying extraction architecture serves legal contracts and hospital discharge summaries. Vendors who've deployed across multiple document types have hardened their stack against layout variability that single-domain specialists haven't seen.
Engineering-grade implementation.
Extraction systems that perform in a proof-of-concept and degrade in month three are a well-known failure mode. We instrument accuracy continuously, monitor for distribution shift, and retrain before your team notices missed extractions in downstream data.
Reference work
Project
What We Built
Result
AnglerVision
Document and media classification pipeline. Automated metadata extraction across multi-format archives.
74% reduction in manual processing time. Structured output feeding directly into production analytics.
[Healthcare client — NDA]
Medical record abstraction for clinical trial data. Multi-field extraction from heterogeneous discharge summaries.
91% field-level accuracy on held-out test set. Human review queue reduced by 68%.
Ready to see what this looks like on your documents?
A discovery call is a one-hour technical conversation against your actual document population. We don't pitch — we benchmark.
[Book a Discovery Call →]
Want more detail? Continue to [How It Works →] or jump straight to [Architecture →].
1. Ingest
Accept documents in whatever format they arrive — PDF, TIFF, JPEG, DOCX, email attachment, fax scan, API payload. Normalize to a consistent internal representation. Handle multi-page documents, rotated pages, poor scan quality, and mixed-format batches without requiring pre-processing by the sender.
2. Classify
Identify what type of document this is. An invoice, a consent form, a bill of lading, an NDA. Classification determines which extraction rules and models apply downstream — misclassify and everything after is wrong.
3. Locate
Find the relevant fields within the document. Where is the invoice total? Where is the patient date of birth? Where is the governing law clause? Layouts vary by vendor, annotations appear in unexpected places, tables span multiple pages.
4. Extract
Pull the actual values from located fields. Normalize to consistent formats — dates to ISO 8601, currency to a standard decimal, names to a structured form. Handle abbreviations, shorthand, and domain-specific terminology.
5. Validate and score
Check extracted values against expected formats, business rules, and cross-field consistency. Flag extractions where confidence is low. Surface a human-review queue that contains only documents that actually need attention.
What makes this hard
Document layouts are not standardized.
Every supplier has a different invoice template. Every hospital system has a different discharge summary format. A system trained on your top-ten suppliers fails when supplier eleven sends their first invoice. Production extraction systems handle layout variance by design, not by exception.
Scan quality is variable and unpredictable.
A faxed invoice photographed on a phone and emailed as a JPEG is a different problem from a machine-generated PDF. Real document populations contain all of these. Extraction accuracy on clean PDFs doesn't predict accuracy on degraded scans.
Handwriting breaks most pipelines.
Handwritten annotations on typed forms. Handwritten dates on signed contracts. Standard OCR doesn't handle these well. Production extraction systems need specific handling for handwriting, with accuracy benchmarked separately.
Tables are structurally ambiguous.
A table with merged cells, nested headers, subtotals, and footnotes is not a grid. It's a visual convention that naive extraction systems flatten incorrectly.
The schema you need isn't always explicit.
You need the effective date. The document has a signature date, an execution date, a date in the recitals, and a date of last amendment. Knowing which one you need requires domain knowledge — not just pattern matching.
Documents change.
Suppliers update templates. Regulatory forms get revised. Extraction systems trained on a static snapshot degrade silently as the document population shifts.
What deployment actually looks like
Discovery against your real documents. We test against a sample of your actual document population, not demo PDFs. We determine extraction accuracy, identify edge cases, and scope the integration surface.
Prototype deployment. A reference system running on your documents within 2–4 weeks. You see actual field-level accuracy before any significant investment.
Integration with your existing systems. Plugging extraction output into your ERP, EHR, contract management platform, or data warehouse.
Production and ongoing operations. Models monitored continuously, retrained as your document population evolves, drift detected before it affects downstream data quality.
For the engineering detail behind each stage, continue to [Architecture →]. For the operational reality, see [Operations →].
Human review queue rate
< 15% of documents
Confidence scoring drives selective escalation
Bandwidth to cloud
0 KB/s (optional event egress)
Air-gapped or VPC-isolated
The five layers
1. Document ingestion and normalization
Default stack: Apache Tika for format detection and conversion; Ghostscript for PDF rendering at configurable DPI; OpenCV for image pre-processing (deskew, despeckling, contrast normalization, orientation correction).
When we substitute: PDFium for high-fidelity PDF rendering where layout precision matters; custom pre-processing pipelines for specific scan degradation patterns (thermal fax artifacts, photocopied handwriting, watermarked templates).
Tradeoffs: DPI vs throughput (200 DPI is faster; 300 DPI recovers more handwriting and small fonts). Whether to normalize all inputs to a single raster format or preserve vector PDFs for layout-aware downstream processing.
2. OCR and text extraction
Default stack: Tesseract 5 (LSTM engine) for general-purpose OCR; PaddleOCR for multilingual and rotated-text scenarios; native PDF text extraction (pdfminer / pdfplumber) for machine-generated PDFs where available — always prefer native text over OCR when the PDF has an embedded text layer.
When we substitute: EasyOCR for handwriting-heavy pipelines; TrOCR (transformer-based OCR) for degraded historical documents; Google Document AI or AWS Textract in hybrid deployments where data governance permits cloud inference for specific document types.
Cost surface: OCR is the most compute-intensive step for scanned documents. For machine-generated PDFs, native text extraction is an order of magnitude cheaper and more accurate.
3. Layout analysis and document understanding
Default stack: LayoutLMv3 for document classification and layout-aware field extraction; DiT (Document Image Transformer) for layout segmentation; rule-based table reconstruction for structured forms with consistent grid geometry.
When we substitute: Donut (end-to-end document understanding without explicit OCR) for controlled document types; LLM-based extraction (GPT-4V, Claude) for long-tail document types where training a dedicated model is not cost-effective.
The failure mode: layout models trained on public benchmarks (DocBank, PubLayNet, FUNSD) perform well on academic papers and standardized forms. Real enterprise documents have layout patterns not present in any public benchmark. The gap between benchmark accuracy and field accuracy is the gap between a demo that wins a pilot and a deployment that gets cancelled.
4. Field extraction and normalization
Default stack: Named entity recognition (fine-tuned BERT/RoBERTa) for entity-level extraction; regex-anchored extraction for high-confidence structured fields (dates, currencies, account numbers); LLM-based extraction for semantically complex fields (obligation text, clause summaries, contextual date resolution).
When we substitute: Pure LLM extraction with structured output via function calling for document types where the field schema is ambiguous or highly variable. Template-based extraction for document populations where layout variance is low and training data is scarce.
Normalization layer: extracted values pass through date standardization (ISO 8601), currency normalization, entity resolution, and address normalization. This layer is where extraction output becomes data-warehouse-ready.
5. Validation, confidence scoring, and human review routing
Default stack: Per-field confidence scores; cross-field validation rules (invoice total = sum of line items; date ranges are coherent; required fields are present); business-rule validation (invoice amount within expected range for supplier).
Human review routing: Documents below configurable confidence thresholds, documents that fail validation rules, and documents flagged as novel layout types route to a human review queue. Reviewer corrections feed back into the training loop. Every document that exits the pipeline has either a confidence record or a human sign-off record.
The hard problems we plan for
Layout variance at scale
A production AP automation pipeline might process invoices from 3,000 distinct suppliers over a year. We address this through layout-agnostic architectures (LayoutLMv3, Donut) rather than template-matching approaches that require per-supplier configuration.
Handwriting and degraded scans
OCR accuracy on printed text and handwriting are different numbers. We benchmark them separately and report them separately.
Low-data document types
For a new document type with 50 training examples, a few-shot LLM outperforms a fine-tuned model. For a mature type with 10,000 labeled examples, the fine-tuned model outperforms the LLM at lower cost and higher throughput. We choose based on data availability.
Schema evolution
We architect extraction schemas as versioned contracts, not hardcoded field lists — schema changes don't require pipeline rebuilds.
Concept drift
Drift detection — comparing confidence score distributions and validation failure rates against a baseline — fires before your downstream data quality metrics catch the problem.
One architecture, five operational profiles
The underlying extraction pipeline is one system, not five. What changes across verticals is the document taxonomy, the validation rules, and the downstream integration surface.
Vertical
Daily Volume
Latency Budget
Accuracy Priority
Typical Downstream
Finance & AP
High
< 10 s
Field accuracy, PO matching
ERP / EAP systems
Legal & Contracts
Low–Med
Minutes–hours
Clause precision, entity resolution
CLM, data warehouse
Healthcare
Med
< 30 s
Coding accuracy, PII compliance
EHR / claims systems
Insurance
High
< 60 s
Straight-through rate, audit trail
Core insurance platforms
Logistics
High
< 15 s
Field accuracy, multi-language
TMS / WMS / customs
Deployment topology
On-premise GPU server (T4 / A10 / L4, or AMD MI-series). Standard for enterprise document pipelines where document sensitivity precludes cloud processing. 200–400 pages/min per GPU worker.
CPU-only worker cluster. Viable for pipelines dominated by machine-generated PDFs. 40–80 pages/min per 4-core worker. Cost-effective at moderate volume.
Hybrid. CPU cluster handles ingestion and native text extraction; GPU workers handle OCR and layout models for scanned documents only.
Air-gapped. Full pipeline with no outbound network. Right for regulated environments — legal, government, defense.
Deployment via Docker / Kubernetes with GPU operator, or systemd units for bare-metal environments. We don't lock you into our orchestration layer.
Observability
Per-document-type dashboards exposing throughput, per-field accuracy, confidence distributions, validation failure rates, human review queue depth, and retraining queue size. Logs structured for Prometheus, Datadog, OpenTelemetry, or your existing stack.
Security and data architecture
Data residency: all processing on customer infrastructure. Optional event egress under your control.
Encryption: at-rest (AES-256, customer-managed keys via KMS / Vault / HSM) and in-transit (mTLS service-to-service, TLS 1.3 for client APIs).
RBAC: per-document-type and per-field access control. PII fields accessible only to authorized roles.
Audit logging: every document processed, every field extracted, every human review action, every model update logged to an immutable store.
PII handling: configurable on-device redaction before any data leaves the processing environment.
Compliance frameworks: GDPR, HIPAA, PIPEDA, PHIPA, SOC 2. Specific control mappings available during architecture review.
Air-gapped deployment: full pipeline with no outbound network. Model updates via signed offline packages.
Build vs buy
What a 6-month internal build looks like
A capable ML team can stand up Tesseract + a regex extraction layer for your five most common document types in four weeks. Multi-layout generalization, handwriting handling, confidence scoring, human review workflow, drift monitoring, and schema versioning are the other twenty weeks.
Where building makes sense
When document extraction is a core differentiator of your product and you intend to invest in a permanent document AI team. The build amortizes.
Where buying makes sense
When extraction is an enabling capability for a broader product. The build does not amortize, and the ongoing operational cost of an in-house document AI team exceeds the engagement cost of a specialized partner.
What we won't do
Process documents containing biometric or sensitive personal data in jurisdictions without explicit legal basis.
Ship models we can't explain to your compliance team — every extraction decision has a confidence score, a model version, and an audit trail.
Lock customers into proprietary annotation formats or model artifacts — you own your training data and your models.
Promise accuracy numbers we haven't measured on your documents.
Build extraction pipelines that aggregate personal data in ways that violate applicable privacy law or our ethics policy.
Engagement model
Engagements start with a paid technical discovery: 2–4 weeks against your real documents, your real hardware, your real integration surface. By the end you have a reference architecture, a benchmarked field-level accuracy baseline by document type, and a go/no-go decision based on actual numbers.
Production engagements run on milestone-based contracts with defined acceptance criteria (per-field F1 targets, throughput SLAs, human review queue rate). Ongoing operations (monitoring, retraining, on-call) run as a separate retainer scoped to your document volume and SLA requirements.
[Request Architecture Review →] [Book a Discovery Call →]
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