Hyperautomation Services

AI for the decisions that rule-based automation can't make: the branching points where the outcome depends on content, context, or probability rather than a simple condition check.

Document classification: a fine-tuned DistilBERT or LayoutLMv3 model classifying incoming documents (invoices, purchase orders, contracts, remittance advice, query emails) by type with confidence scoring. Confidence below threshold routes to human review; above threshold, the document flows to the appropriate extraction model for its type. Classification accuracy benchmarked at 92-97% on known document categories.

NLP-based intent extraction from unstructured email: GPT-4o or Claude 3.5 Sonnet with structured output (JSON schema enforcement) extracts action items, deadlines, and entity references from vendor email bodies, "please approve the attached PO by Thursday" becomes a structured task record with action=approve, document=attached_PO, deadline=2024-01-18, routed to the appropriate approver automatically. Applied to accounts payable query handling, vendor escalation routing, and customer request triage.

Anomaly detection for transaction processing: LightGBM or Isolation Forest trained on historical transaction patterns flags deviations (invoice amount 3x the vendor's 90-day average, payment request to a new bank account not previously used, duplicate invoice submission with a different invoice number). Flagged transactions pause for manual review rather than clearing automatically, the human sees the specific anomaly, not just "flagged for review."

Approval recommendation models: trained on historical approval outcomes (approved, rejected, approved with modification) with features including invoice amount, vendor risk score, cost centre budget remaining, and contract reference. The model outputs a recommendation with confidence level and the supporting evidence, presented to the approver alongside the approval request to reduce decision time and improve consistency across approvers.

Structured data extraction from unstructured documents as a component of a larger automated process, invoices feeding AP automation, contracts feeding CLM (contract lifecycle management), compliance certificates feeding onboarding workflows, and insurance documents feeding claims processing. The OCR and document intelligence layer is not a standalone tool; it is the data intake layer for the process automation pipeline.

OCR engine selection based on document quality and structure: Azure Document Intelligence (formerly Form Recognizer) Invoice model for consistent high-quality supplier PDFs (94-99% field extraction accuracy on standard invoice formats); AWS Textract AnalyzeDocument for scanned or photographed documents with variable quality (85-95% accuracy); custom fine-tuned LayoutLMv3 for document types not covered by commercial models (industry-specific forms, legacy document formats). Image pre-processing via OpenCV: deskew for rotated scans, contrast enhancement for faded documents, perspective correction for photographed documents.

Layout-aware extraction: LayoutLMv3 understands the two-dimensional position of text on the page, not just the text sequence. A "Total" label with a value to its right is extracted as a labelled pair regardless of whether the label says "Total", "Amount Due", "Invoice Total", or "Please Pay", because the model learns from document layout patterns, not string matching.

Validation before downstream submission: extracted vendor name matched against vendor master using Levenshtein distance (similarity threshold 0.85+); PO reference cross-checked against open purchase orders in the ERP; VAT registration number validated against HMRC (UK) or IRS EIN lookup API (US); line item subtotals summed and compared to the extracted total with a 0.01 rounding tolerance. Failed validations flag the specific field in the exception queue rather than rejecting the entire document.

The workflow engine that coordinates every automated step, every human task, and every system call in a single process definition. Business rules encoded once in the orchestration layer, routing logic, approval thresholds, escalation timers, SLA deadlines, and enforced consistently across every case without relying on individual process participants to remember the rules.

Orchestration implementation: Apache Airflow for complex multi-step DAGs with dependency management and scheduled triggers; Temporal.io for long-running workflows (days to weeks) with durable execution and automatic retry on failure; custom Python or Node.js orchestration for simpler processes that don't require a full workflow platform. Workflow definitions stored as code in version-controlled repositories, workflow changes are reviewed, tested, and deployed like application code, not configured in a GUI that bypasses change management.

Routine case automation: the happy path (complete, valid data, no approval threshold triggered) executes end-to-end without human involvement. For a P2P (procure-to-pay) process, this means: invoice received via email → OCR extraction → PO matching → three-way match (invoice/PO/goods receipt) → auto-approval (under £500 threshold) → AP system posting → payment scheduled → all in under 3 minutes from email receipt.

Exception routing with full context: when a case requires human intervention, the orchestrator creates a task in the exception queue with the full case state, all documents attached, all prior automated steps logged with their output, the specific exception reason (PO mismatch, over-threshold amount, new vendor not in master), and the decision required. The human resolves one decision, not the entire case.

SLA management: each process step has a configurable SLA (e.g., manual approval step: 4 hours). The orchestrator monitors elapsed time and escalates at 50% of SLA (reminder), 100% of SLA (escalation to manager), and 150% of SLA (escalation to director). Escalation routing defined in the process configuration, not hardcoded, changing who escalation goes to during holiday periods is a configuration change, not a code change.

Hyperautomation processes typically span 4-8 systems: an ERP (SAP, NetSuite, Dynamics 365, Oracle ERP Cloud), a CRM (Salesforce, HubSpot), a document management platform (SharePoint, Google Drive, DocuWare), an email system (Microsoft 365, Google Workspace), a payment processor (Stripe, Worldpay), and often legacy databases with no modern API surface. The integration layer connects them so data flows without manual re-entry between systems.

Integration patterns by system type: SAP integration via SAP OData v4 (for S/4HANA) or SAP Business Eventing (CDS event mesh) for change-driven triggers; RFC/BAPI via JCo connector for on-premise SAP ECC; SAP Business Connector middleware for systems without direct API access. Salesforce integration via REST API v57 with SOQL queries and real-time triggers via Salesforce Platform Events (Change Data Capture). Microsoft 365 via Microsoft Graph API for email processing (read, parse, categorise), SharePoint document management, and Teams notification delivery.

Legacy system integration: database direct polling for systems without API exposure (read-only replica queries on MSSQL or Oracle using change data capture via Debezium or timestamp-delta queries); SFTP polling for file-based integrations (remittance files, EDI messages, export files from systems that can't receive API calls); screen scraping via Playwright or Puppeteer for web-based legacy systems where no API or file export is available.

Data transformation layer: JSON-to-SAP IDOC format conversion; field name mapping documented as a versioned data dictionary (source field → transformation rule → target field); currency conversion using ECB (European Central Bank) rates fetched daily; date format normalisation; code list mapping (internal cost centre codes to SAP profit centre codes). Transformation logic unit-tested so field mapping bugs are caught before they corrupt production data.

Integration reliability: HMAC-SHA256 signature verification for inbound webhooks; exponential backoff retry (30s, 2min, 10min, 1h; max 5 retries); dead-letter queue for permanently failed messages with a management UI for investigation and replay; daily reconciliation report comparing record counts between source and target systems to detect silent sync failures.

Before automating, we map how the process actually runs using event log data from your systems, not the process map on your intranet or the way your team describes it in a workshop. The two are rarely the same.

Process mining tooling: Celonis Process Intelligence or ProM (open-source) applied to ERP event logs (SAP ABAP change documents, Oracle audit tables, Dynamics 365 activity history) to reconstruct actual process execution sequences. The mining output shows: process variants (the cases that skip step 3 because an approver is on leave, the cases that loop back twice because data was missing, the cases that take a shortcut that violates the documented policy); bottlenecks (the approval step with a 4-day median duration that the process map shows as "one business day"); and rework rates (the 28% of purchase orders that require at least one correction after initial entry).

Automation potential scoring: each process step scored on four dimensions, frequency (how often does this step occur per week?), manual time (how many minutes does it take a human?), standardisation (does the same input always require the same action, or does judgment vary?), and error rate (how often does this step produce an error that requires correction?). High-frequency, high-manual-time, high-standardisation, low-judgment steps are the automation priority. Steps that require judgment (complex supplier dispute resolution, significant contract exception approval) are retained as human tasks, properly supported by the automation context rather than removed.

ROI estimation per automation candidate: time saved = frequency × manual time reduction; error cost reduction = current error rate × rework time × headcount cost; total annual value calculated per process step and ranked to produce a prioritised automation backlog. The first process we automate is the one with the highest ROI, not the one that's easiest to build.

Production monitoring tracks every process execution with the metrics that determine whether the hyperautomation is delivering its ROI targets.

Core process KPIs tracked per process: case volume (total cases initiated, cases completed, cases in progress, cases in exception); throughput time (end-to-end time from trigger to completion at p50/p95 broken down by process step); automation rate (percentage of cases that complete the full process without any human intervention, the primary ROI metric); exception rate (percentage of cases that require at least one human action) broken down by exception category; SLA compliance (percentage of cases completed within the agreed end-to-end SLA, and per-step SLA for approval and review steps).

Prometheus metrics exported from the orchestration layer, visualised in Grafana dashboards: time-series views of automation rate trends (want to see this increasing over time as exception handling improves); exception category breakdown (pie chart of exception types so the highest-frequency exception is the next automation target); integration health (last successful sync timestamp per connected system, request success rate, error rate). PagerDuty alerts for critical failures: automation rate drops below 60% in a 1-hour window (integration or model failure); an integration hasn't synced in 30 minutes beyond its expected interval; exception queue depth exceeds 100 items (backlog building faster than human review capacity).

Continuous improvement feedback loop: every human correction to an automated decision (changing a classification, overriding an extracted value, rejecting an AI recommendation) is logged with the original prediction and the human correction. Weekly batch analysis of corrections identifies systematic error patterns, if a specific exception type has been manually resolved consistently for 2+ weeks, the correction pattern is used to update the routing rules or retrain the classification model. This feedback loop is what moves the automation rate from 70% at launch toward 85-90% at 6 months.