Automate the workflows RPA cannot touch.
AI-augmented automation for processes that require judgment, interpretation, and adaptation — not just rule-based recording and replay.
AI-Augmented Workflow Automation
End-to-end workflow automation that combines AI reasoning with process orchestration — handling variations, exceptions, and edge cases that break rule-based bots.
Cognitive Automation
Automation for tasks that require interpretation and judgment — classifying requests, extracting meaning from unstructured inputs, and making context-aware decisions within your process.
Process Mining & Optimization
AI-driven discovery of automation candidates across your operations — analyzing process patterns, identifying bottlenecks, and prioritizing workflows by automation potential and business impact.
Intelligent Document Routing
AI-powered document classification and routing that reads, understands, and directs incoming documents to the right workflow — invoices, claims, applications, correspondence.
AI-Powered Decision Support
Decision engines that evaluate complex criteria, score confidence, and recommend actions within your workflows — augmenting human judgment, not replacing it.
Human-in-the-Loop Design
Confidence-based routing that escalates to human reviewers when AI certainty falls below your threshold — exceptions are handled, not dropped.
The Difference
RPA records clicks. We build understanding.
Side by side — how traditional RPA handles your workflow versus how AI-augmented automation handles it. Same process, fundamentally different capability.
Click any stage on the right to explore its components
Fixed Triggers
Scheduled runs or manual starts. Breaks when input format changes.
Screen Scraping
Reads fixed positions on a screen. Breaks when UI changes.
Rule-Based Only
If/then logic. Cannot handle ambiguity or exceptions.
Brittle Execution
Replays recorded clicks. Fails silently when systems update.
Fail and Queue
Exceptions go to an error queue. Human starts from scratch.
Record → Replay → Break → Fix
vsTrigger → Understand → Reason → Act → Review
Handles What RPA Cannot
Processes that require interpretation, judgment, and context-aware decisions — not just screen scraping and click recording. AI reasoning handles the variations that break rule-based bots.
Works With Your Existing Systems
API-native integration with your ERP, CRM, document management, and workflow systems. No rip-and-replace — automation connects to what you already have via APIs and webhooks.
Scale Without Proportional Headcount
Handle growing volume without proportionally growing your operations team. Automation absorbs the routine and judgment-assisted work; your team focuses on exceptions and strategy.
Measurable Process Intelligence
Every automated workflow produces metrics — throughput, processing time, error rates, confidence scores, and escalation patterns. You see what is working and where to optimize next.
Adapts When Processes Change
AI-powered automation adapts to process variations instead of breaking. When forms change, inputs vary, or rules evolve, the system adjusts — no bot repair cycle.
Key Capabilities
- AI-augmented workflow automation (LLM reasoning + process orchestration)
- Cognitive automation for judgment-dependent tasks
- Process mining and automation candidate identification
- Intelligent document processing, classification, and routing
- AI-powered decision support and confidence scoring
- Human-in-the-loop review workflows with escalation routing
- Business process automation with LLM integration
- System integration via APIs, webhooks, and event-driven connectors
- Process monitoring, metrics, and continuous optimization
- Exception handling and graceful degradation design
Technologies
Engagement Models
Automation Discovery Sprint
- Process analysis for 3–5 target workflows
- Automation feasibility and complexity assessment
- Integration mapping with existing systems
- Prioritized automation roadmap by ROI and complexity
- Go/no-go recommendation with production estimate
Production Automation Build
- Full automation for 2–3 target workflows
- AI decision engine with confidence scoring
- Human-in-the-loop review workflows
- System integration (ERP, CRM, document systems)
- Monitoring dashboard and process metrics
- Handover documentation and runbook
Enterprise Automation Platform
- Everything in Production Build
- Process mining across departments
- Multi-workflow orchestration platform
- Advanced decision support and routing
- Custom model training for domain-specific tasks
- Team training and knowledge transfer
Frequently Asked Questions
How is this different from RPA?
Traditional RPA records screen interactions and replays them — it works for predictable, rule-based tasks but breaks when forms change, inputs vary, or the process requires judgment. Intelligent automation adds an AI reasoning layer: LLMs that understand document content, classify requests, evaluate complex criteria, and make confidence-scored decisions. The automation adapts to variation instead of failing on it. RPA and intelligent automation can coexist — RPA handles the mechanical steps while AI handles the judgment-dependent ones.
Can this handle processes that require human judgment?
That is the core differentiator. The AI decision engine evaluates complex criteria and scores confidence on every decision. When confidence is high, the automation proceeds. When confidence falls below your threshold, the workflow routes to a human reviewer with context and a recommended action pre-filled. Your team reviews and decides rather than starting from scratch. Over time, the system learns from human decisions to handle similar cases with higher confidence.
What systems do you integrate with?
The automation layer connects via APIs, webhooks, and event-driven integrations — not screen scraping. We build connectors for ERP systems (SAP, NetSuite, Dynamics), CRM platforms (Salesforce, HubSpot), document management systems (SharePoint, Confluence), email and communication tools, and custom internal applications. The integration layer handles data mapping, validation, and error handling so the automation stays resilient when downstream systems change.
How do you identify which processes to automate?
The Discovery Sprint analyzes your target workflows across five dimensions: volume (how often the process runs), complexity (how many decision points and variations), error rate (where mistakes are most costly), integration points (what systems are involved), and judgment dependency (what percentage requires human interpretation). The output is a prioritized roadmap — not every process benefits from AI automation, and the Sprint identifies which ones do.
What happens when a process changes?
AI-powered automation handles process variation by design. The classification and decision layers understand content semantically — they are not tied to specific form fields, button positions, or screen layouts. When processes evolve, the AI adapts to new input formats and routing patterns. Major process redesigns may require retraining or reconfiguration, but the automation does not break silently — monitoring detects drift in confidence scores and processing patterns.
How long does it take to see results from automation?
The Discovery Sprint produces a prioritized automation roadmap in 2 weeks. A production automation build for 2–3 workflows typically takes 8–12 weeks including integration, testing, and human-in-the-loop setup. You see measurable results — throughput improvements, error rate changes, processing time reductions — from the first automated workflow. The platform approach (16+ weeks) rolls out automation across departments incrementally.
Ready to automate the workflows RPA cannot handle?
Book a 30-minute call. We will discuss your target workflows, integration landscape, and where AI-augmented automation can deliver the most impact.