Document Automation Services

AI extraction of structured data from any document format, contracts, invoices, purchase orders, forms, compliance filings, and reports, using the extraction method matched to the document's format and consistency characteristics. Digital PDF extraction using PyMuPDF or pdfminer.six for text-layer PDFs with clean formatting: header and line item extraction without OCR overhead at 99%+ field-level accuracy on consistent document templates. Scanned document OCR using Tesseract with preprocessing (deskew, contrast normalisation, noise reduction) for older physical documents converted to image PDFs, typical extraction accuracy 92-96% for high-quality scans, lower for degraded originals with the degradation level assessed before committing to an accuracy target. LLM-based extraction using GPT-4o or Claude for semi-structured and variable documents where rule-based extraction fails: a contract with non-standard clause ordering and custom defined terms, a supplier proposal with an idiosyncratic format, or a letter with embedded data that is not in a table. Azure Document Intelligence (formerly Form Recognizer) and AWS Textract as managed services for high-volume production extraction where per-document cost matters at scale, these services amortise GPU infrastructure costs across their customer base. Confidence scoring per extracted field: each extracted value returned with a confidence score, with values below the configured threshold (typically 0.85) routed to a human review queue rather than written to downstream systems, preventing low-confidence extractions from silently corrupting your database. Extraction accuracy tracked in a dashboard per document type and per field, with degradation alerts when accuracy falls below baseline, so you know when a new document variant is causing extraction failures before it propagates downstream.

Template-based document assembly that generates contracts, proposals, SOWs, compliance filings, and custom reports from your data automatically, producing documents in the exact Word, PDF, or HTML format your recipients expect without anyone opening a document editor. Template engine selection based on output format requirements: Docxtemplater for Word document generation from DOCX templates with variable substitution, conditional sections, and repeating table rows (the most common approach for contract and proposal generation that preserves your existing Word template formatting); WeasyPrint or Puppeteer for high-fidelity PDF generation from HTML/CSS templates where precise layout control matters; Jinja2 for text and markup document generation in Python pipelines. Conditional clause logic: template sections marked with business rules that determine inclusion or exclusion based on input parameters, a services agreement template that automatically includes GDPR data processing terms when the counterparty is in the EU, selects the applicable jurisdiction from a mapping table based on the counterparty's country, and includes specific liability caps based on the contract value. Variable substitution from your CRM, ERP, or form input: party names, addresses, pricing, and deal-specific terms pulled directly from your source systems rather than typed by a human, eliminating the transposition errors that affect manually assembled documents. Multi-template library management with version control: template updates deployed without breaking in-flight document workflows, with old template versions preserved for reproducing historical documents. Document generation time target under 30 seconds from trigger to completed document, including data retrieval and template rendering, replacing the 2-hour manual process that assembles the same document from the same data sources every time.

AI that reads incoming documents, emails, uploads, portal submissions, and scanned mail, and classifies them by document type, urgency, and destination routing before any human touches them, replacing the inbox triage that takes 30-60 minutes of human time per hundred documents. Classification model architecture: fine-tuned text classification using the document's extracted text content combined with metadata features (sender domain, file format, file name pattern, submission portal), a multi-label classifier trained on your historical document corpus produces higher accuracy on your specific document mix than a generic model applied off the shelf. Document type classification at multiple levels: broad category (invoice, contract, correspondence, regulatory filing, purchase order) and sub-category (supplier invoice vs. customer invoice, NDA vs. services agreement vs. employment contract), the level of granularity matched to your routing requirements. Urgency scoring based on extracted signals: due dates and deadline language extracted from the document combined with submission timing (a document submitted 3 days before its deadline classified at higher urgency than the same document type with 30 days remaining). Confidence threshold routing: high-confidence classifications processed automatically; medium-confidence classifications sent to a quick-review queue where a human confirms or corrects the classification with a single click; low-confidence documents sent to full human review, the three-tier approach that keeps automation rates high while preventing misrouting. Classification accuracy tracked per document type in a dashboard: overall accuracy above 95% is achievable for consistent document types, with lower accuracy expected for novel or ambiguous documents that fall between categories. Misclassification corrections captured and fed back into periodic model retraining so accuracy improves over time as the model learns from edge cases.

Digital approval chains with conditional routing based on document type, extracted value, jurisdiction, risk level, or any other field the document extraction layer provides, replacing the informal email chain that loses track of who approved what and when. Workflow engine built on configurable state machines: each document type has a named workflow definition that specifies states (Submitted, Under Review, Approved, Rejected, Signed, Archived), transitions between states, the actor or role responsible for each transition, and the conditions that determine routing branches. Routing rules example: a purchase order with extracted value above $50,000 routes to department head then CFO for dual sign-off; a purchase order below $10,000 routes to department head only; a purchase order with a new supplier routes through vendor onboarding before the standard approval chain regardless of value. Approval actions via web interface, email deep link, or Slack message, approvers click Approve or Reject from wherever they are without logging into a separate system. DocuSign, Adobe Acrobat Sign, and HelloSign integration for legally binding digital signature collection: envelope creation via API, signatory order enforcement (counterparty cannot sign before internal approval completes), signature completion webhook triggering the next workflow state, and signed document stored with the full audit trail. Reminder escalation: configurable reminder emails sent 48 hours before an approval deadline, 24 hours before, and on the deadline day; unresponded approvals escalated to the approver's manager after the configurable deadline passes. Audit log capturing every action with actor identity, timestamp, IP address, and the document state before and after the action, a complete chain of custody record for compliance and dispute purposes.

Automated report generation that pulls data from your source systems, applies your calculation and formatting logic, and produces the finished report in the exact format your recipients expect, replacing the manual assembly process that takes hours because data lives in multiple systems that don't talk to each other. Data pipeline architecture using Python with SQLAlchemy for database connections (PostgreSQL, MySQL, SQL Server), the Salesforce/HubSpot/NetSuite API clients for CRM and ERP data, and pandas for data transformation and calculation logic, the same calculation rules your analyst applies manually, encoded as code that runs the same way every time. Report format generation: openpyxl for Excel reports with multi-sheet workbooks, conditional formatting, and embedded charts that match your existing Excel template format exactly; WeasyPrint or ReportLab for PDF reports with your logo, typography, and layout; Puppeteer for HTML-to-PDF generation when the report layout is complex or requires chart rendering. Scheduling via cron job or an orchestration platform (Apache Airflow, Prefect, or AWS EventBridge): weekly management packs generated on Sunday at midnight for Monday morning delivery, monthly regulatory filings generated on the first of the month, daily operational dashboards generated overnight for the start-of-day review. Delivery methods: email via SendGrid or AWS SES with the report attached or linked, SharePoint upload for teams that manage reports in a document library, Slack message with the report summary and a PDF link, or a direct ERP upload for regulatory filings that go straight into a compliance system. Data freshness validation before report generation: each data source queried for its last-update timestamp and the report held if a source hasn't updated within the expected window, preventing reports that look complete but are missing a day of data from a source that had an overnight processing failure.

End-to-end contract management system from first draft through execution, storage, and renewal alert, built for legal, procurement, and operations teams who manage high contract volumes and cannot afford to miss renewal dates, lose executed copies, or track obligations manually in spreadsheets. Contract generation from a template library: Word DOCX templates with conditional clause logic, jurisdiction-specific variants, and standard and non-standard clause sets maintained by the legal team in a version-controlled template library, generating a first draft takes minutes rather than the hour it takes to copy-paste from a previous contract and manually update every reference. AI-assisted contract review using LLM extraction to flag non-standard clauses: liability caps above your policy threshold, unusual indemnification language, non-standard IP assignment terms, and missing standard protective clauses like limitation of liability, each flagged with the relevant clause text and the standard language it deviates from. Obligation extraction: AI scans executed contracts for time-bound obligations (payment due dates, delivery milestones, renewal notice windows, insurance certificate submission deadlines) and populates a shared obligation calendar with each item linked to the source contract clause. Contract repository with PostgreSQL full-text search: every executed contract stored with extracted metadata (counterparty, effective date, expiration date, contract value, document type, governing law) searchable in under a second, finding the 2021 agreement is a name search, not an email archive crawl. Renewal pipeline: configurable alert windows (90 days, 60 days, 30 days before expiration) triggering Slack or email notifications to the contract owner, with one-click renewal initiation launching the generation and signature workflow from the most recent contract as the base template.