Data Extraction Automation Services

AI reading of any document, invoices, contracts, application forms, purchase orders, shipping labels, regulatory filings, with extraction of the specific fields your downstream systems need. Azure Document Intelligence (formerly Form Recognizer) handles high-volume structured documents with its prebuilt invoice and receipt models or custom-trained models for your specific document types; Google Document AI provides strong multilingual support and table extraction; Textract handles AWS-native pipelines. For documents where context determines field meaning (a "total" could be subtotal, tax, or grand total depending on surrounding layout), LLM-based extraction using GPT-4o with structured output or LayoutLM cross-references visual layout with text content to resolve ambiguities that pure OCR cannot. We've shipped production OCR systems processing 20,000+ transactions per day in industrial environments. High-quality digital PDFs achieve 97-99% field-level accuracy with a single-pass extraction. Scanned documents are pre-processed with OpenCV (deskew, denoise, contrast normalisation) before extraction to maximise accuracy on the variable scan quality common in operational document pipelines.

Automated collection of pricing data, product catalogues, competitor intelligence, public procurement filings, market indices, and regulatory disclosures from websites, on your schedule, at any volume, without anyone manually downloading files or copy-pasting tables. Playwright and Puppeteer handle JavaScript-rendered pages that standard HTTP scraping cannot read; Scrapy and BeautifulSoup handle static HTML at scale. Anti-bot measures handled through rotating residential proxies, browser fingerprint randomisation, human-behaviour timing simulation, and CAPTCHA solving services where required, ensuring pipeline continuity even as target sites update their detection mechanisms. Change detection via structural hash comparison alerts when a page layout changes (a selector that previously pointed to the price field now returns null), so format changes are caught before they produce a backlog of empty or incorrectly structured data. Rate-aware crawling respects robots.txt directives and crawl-delay conventions, reducing the risk of IP bans on sources that impose throttling. Output delivered to your PostgreSQL, BigQuery, or Snowflake warehouse in your target schema, with deduplication logic ensuring that re-crawling a source doesn't create duplicate records for unchanged content.

Extraction triggered by email arrival, invoice PDFs from supplier inboxes, order confirmations from sales channels, shipping notifications from 3PLs, application documents from recruitment pipelines, remittance advices from customers. The pipeline monitors designated inboxes via IMAP (for standard mail servers) or Microsoft Graph API / Gmail API (for Office 365 and Google Workspace) with webhook-based push notification for sub-60-second processing latency after email receipt. Email classification by sender domain, subject line keywords, or content pattern routes each message to the correct extraction workflow, a BACS remittance from a known supplier follows a different extraction template than an unrecognised sender's PDF. Attachments are extracted, format-validated (confirming the attachment is the expected document type before processing), and processed through the appropriate extraction engine. Structured data pushed to your ERP, database, or CRM typically within 60 seconds of email arrival. Emails with no attachment, unsupported formats, or extraction failures route to an exception queue with the original email linked for manual review. No one needs to open the email, forward it, or log into a supplier portal to retrieve the document. The volume of supplier invoices, 3PL notifications, and order confirmations that your accounts payable and ops teams currently process manually scales to any volume without additional headcount.

For systems built before APIs existed, 20-year-old ERPs, government portals, partner portals that offer no data feed, we build browser automation that logs in, navigates to the required data, extracts it, and delivers it to your modern platform on a schedule. The system interacts with the UI exactly as a human would, but faster and without errors. Playwright handles the majority of legacy portal automation: Chromium headless runs a full browser context that executes JavaScript, handles session cookies, and navigates multi-step forms exactly as a human operator would. For older web stacks with poor JavaScript compatibility, Puppeteer with a pinned Chromium version provides a stable target. Authentication flows, form-based login, SSO redirects, multi-factor prompts, are handled through stored credential injection combined with session cookie reuse so the full login sequence only executes once per session rather than on every extraction run. XPath and CSS selectors locate data elements precisely; when the UI layout changes, selector failure detection alerts before the pipeline silently produces empty or incorrect output. For portals with CAPTCHA challenges on login or data export pages, we integrate CAPTCHA solving services (2captcha, Anti-CAPTCHA) that resolve image-based and reCAPTCHA v2 challenges programmatically, maintaining pipeline continuity without human intervention. Rotating residential proxy pools prevent IP-based rate limiting on portals that block datacenter IP ranges. Scrapy with its built-in request throttling and middleware pipeline handles bulk data collection from static HTML portals at high volume, downloading paginated reports, drilling through hierarchical navigation trees, and collecting structured tabular data that has no export button. Data extracted from screen scraping passes through the same validation layer as other extraction pipelines: format checks, range validation, and deduplication logic using MinHash or exact-match fingerprinting to prevent re-importing records already captured in a prior run. A bridge between systems you can't replace and processes that depend on their data.

Extraction from relational databases (PostgreSQL, MySQL, SQL Server, Oracle), REST and GraphQL APIs, third-party SaaS platforms (Salesforce SOQL, HubSpot, NetSuite SuiteQL, SAP RFC/BAPI, Dynamics OData), EDI feeds (X12 810/856/850, EDIFACT INVOIC/ORDERS), and FTP/SFTP file transfers. Incremental extraction uses change data capture (CDC) via Debezium for databases that support logical replication, updated_at timestamp queries for APIs, or checksum comparison for file sources, pulling only new or changed records rather than re-processing the full dataset on every run. API pagination handled automatically: cursor-based pagination (next page token), offset-based pagination (page number + limit), and keyset pagination (last-seen ID) each handled with the appropriate pattern for the source's pagination model. Rate limit handling applies exponential backoff and respects Retry-After headers from APIs that enforce throttling. Data is transformed, typed, and validated using schema validation (Pydantic, Zod) before landing in your target system (Snowflake, BigQuery, Redshift, or your operational database). The pipeline that keeps your analytics, reporting, or operations layer current without anyone manually running exports or monitoring FTP directories for new files.

Every extraction pipeline includes multi-layer validation: format checks, range validation, cross-field consistency rules, and business logic gates. Schema validation is implemented using Pydantic (Python pipelines) or Zod (TypeScript pipelines), extracted records that fail schema validation are rejected before they reach the target system rather than corrupting downstream data with malformed rows. Confidence scoring from OCR engines (AWS Textract returns word-level confidence scores between 0.0 and 1.0; Azure Document Intelligence returns field-level confidence per extracted entity) is evaluated against configurable thresholds: above 0.92 the record passes straight-through, between 0.75 and 0.92 it is flagged for spot-check review, below 0.75 it routes to the full exception queue. Extractions in the exception queue are presented with the original document, the extracted values highlighted at the source location, and a guided correction form, human reviewers correct only the low-confidence fields rather than re-entering the entire document. Human corrections feed back into extraction model fine-tuning: corrected examples accumulate until a retraining threshold is reached, at which point the custom extraction model is retrained on the expanded labelled dataset, improving accuracy on the specific document variants that previously caused failures. Deduplication prevents re-importing records already captured in prior runs: exact-match deduplication on unique identifiers (invoice number, PO reference, transaction ID) catches clean duplicates; near-duplicate detection using MinHash and Locality Sensitive Hashing (LSH) catches records where a minor OCR character difference would evade exact-match deduplication. Data lineage tracking records the source document, extraction timestamp, model version, confidence score, and any human correction applied to each record, providing a complete audit trail from raw source to target system for every extracted value. Pipeline health monitoring using Great Expectations or custom metric dashboards tracks extraction accuracy, throughput, and exception rates over time; a drop in accuracy below the rolling 7-day baseline triggers an alert before you have a backlog of failed extractions. Most production systems reach 85-95% straight-through processing rates within 30 days of launch.