AI-Powered Process Automation
Intelligent Workflows Beyond RPA
Process mining, AI-enhanced RPA, intelligent document workflows, and continuous optimization - automation that learns and adapts.
Why This Matters
Traditional RPA Breaks. AI-Powered Automation Adapts.
Robotic Process Automation (RPA) revolutionized enterprise operations - but traditional RPA is brittle. When a screen changes, a form layout shifts, or an edge case appears, rule-based bots fail. Enterprises spend 30-40% of their RPA budget on bot maintenance. The promise of 'set it and forget it' automation never materialized.
AI-powered process automation changes the game. Instead of rigid rules, bots use ML classifiers to handle exceptions, Document AI to understand any format, and process mining to discover optimization opportunities automatically. When a bot encounters an edge case, an ML model routes it to the correct resolution path instead of failing.
We build intelligent automation systems that combine traditional RPA (UiPath, Automation Anywhere) with AI capabilities - computer vision for screen understanding, NLP for document processing, and ML for decision-making. The result: automations that handle 95%+ of cases autonomously, including the edge cases that break traditional bots.
Our Tech Stack
Production-Grade Tools We Deploy
RPA + AI
Process Mining
Document AI
Workflow Orchestration
OCR & Extraction
ML Classification
Integration & Messaging
Monitoring
Architecture Deep-Dive
How We Build It
Intelligent Process Discovery
Process mining with Celonis to identify automation candidates. Data-driven ROI prioritization. Task mining for desktop workflows that reveals hidden automation opportunities.
- Celonis process mining on ERP event logs (SAP, Oracle) to visualize actual processes
- Variant analysis: discover how the same process runs differently across teams
- ROI calculator: prioritize automation candidates by volume x complexity x cost
- Task mining: record desktop interactions to discover repetitive manual workflows
- Conformance checking: identify deviations from standard operating procedures
- Bottleneck detection: ML-driven analysis of process delays and root causes
AI-Enhanced RPA
Traditional RPA bots augmented with ML-based exception handling. When a bot encounters an edge case, an ML classifier routes it to the correct resolution path instead of failing.
- ML classifiers for exception routing: 95%+ accuracy on edge case handling
- Computer vision for UI element detection: bots adapt to screen changes
- Self-healing automations: bots detect and recover from failures automatically
- NLP for email triage: classify, extract, and route incoming messages
- Hybrid human-bot workflows: seamless handoff when AI confidence is low
- Bot performance monitoring with automated alerting and health checks
End-to-End Document Workflows
Intake (email/upload) -> extraction (Document AI) -> validation (ML classifiers) -> system entry (RPA/API). Processing invoices, purchase orders, insurance claims at 99%+ accuracy.
- Multi-channel intake: email, upload portal, scanner, mobile capture
- Document classification: automatically identify document type (invoice, PO, claim)
- Google Document AI / AWS Textract for structured data extraction
- LayoutLMv3 for layout-aware extraction of complex forms and tables
- ML validation: cross-reference extracted data against master data
- Straight-through processing rate of 85-95% with human review for exceptions
Continuous Process Optimization
Real-time process KPIs with Grafana dashboards. ML-driven bottleneck detection. Automated A/B testing of process variants to continuously improve efficiency.
- Real-time Grafana dashboards: throughput, error rates, processing time, SLA adherence
- ML-driven bottleneck detection: identify and alert on process slowdowns
- A/B testing of process variants: compare automation strategies with data
- Predictive analytics: forecast processing volumes for capacity planning
- Cost-per-transaction tracking: measure ROI of each automation
- Continuous improvement loop: mine -> automate -> measure -> optimize
Data Security, Governance & Safety
Enterprise AI demands enterprise-grade security. Every solution we deploy follows strict data sovereignty, safety, and compliance standards.
Data Sovereignty
- Your data stays in your infrastructure - always
- Deploy on your cloud (AWS, Azure, GCP) or on-premise
- No data leaves your environment
- Full compliance with regional data residency requirements
Model Safety & Guardrails
- NVIDIA NeMo Guardrails for content safety
- PII detection and redaction with Presidio
- Prompt injection defense and input sanitization
- Hallucination detection and factual grounding
Access Control & Audit
- Role-based access control for all AI systems
- Immutable audit logs for every interaction
- SOC 2 Type II, ISO 27001 compliance frameworks
- GDPR, HIPAA, and industry-specific regulations
Responsible AI
- Bias testing with Fairlearn and AI Fairness 360
- Model explainability via SHAP and LIME
- Transparency reports for stakeholders
- Continuous fairness monitoring in production
FAQ
Frequently Asked Questions
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