DigiEdze AI · AI Automation
Automate the Work Between Systems
The expensive work is rarely inside one application — it is the copying, checking, chasing and reconciling between them. We automate those seams with agents that handle the exceptions traditional automation could not.
- workflow automation
- process orchestration
- agentic automation
- approval workflows
- exception handling
Reference architecture
- 07Process intake — event · schedule · request
- 06Durable workflow engine
- 05Deterministic steps
- 04Agentic exception resolution
- 03Approval gate
- 02System actions via tool registry
- 01Outcome verification
The Problem
Cost of Making It Wrong
Rule-based automation covers the happy path and hands every exception back to a person.
The work between systems is invisible to process mining because it happens in inboxes and spreadsheets.
Automation that cannot explain its decision cannot be approved in a controlled process.
What We Build
AI Automation Capability Grid
Process discovery
Mapping how the work actually flows, including the undocumented steps that consume the most time.
Workflow orchestration
Durable, resumable workflows with retries and compensation, so a long process survives failure.
Agentic exception handling
Agents that read the case, gather context and resolve the exceptions deterministic rules cannot express.
Approval workflows
Human checkpoints placed where the risk is, with the full reasoning trail presented for the decision.
System actions
Governed writes into ERP, CRM and ITSM through the tool registry, never through screen scraping alone.
Continuous measurement
Automation coverage, exception rate and cost per case tracked as the process changes.
How it works
Tune your workflows for complete automation based on the need across your systems, through the technology we build for you.
- Durable workflow engines
- Agent orchestration
- MCP tool servers
- Event streaming
- Approval interfaces
Use Cases
AI Automation Use Cases
Finance
Invoice exceptions investigated, matched and resolved with an audit trail per case.
Insurance
First-notification-of-loss triage routed and enriched before a handler opens it.
Business Operations
Joiner-mover-leaver processes executed across systems with policy checks at each step.
Outcomes
What We Measure
Track how much operational work has shifted from manual work to AI-driven execution, and the value shift it is delivering.
- Automation coverage
- Exception resolution rate
- Cycle time per case
- Cost per completed case
Questions
AI Automation, Answered Directly
How is this different from RPA?
RPA reliably repeats a defined sequence. Agentic automation handles the cases where the sequence does not apply — missing data, ambiguous matches, unusual conditions — by reasoning over context and escalating when it should. The two combine well: rules for the path, agents for the exceptions.
Related services
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Identify an Automation Opportunity
A 30-minute conversation with our architect can tailor this for you. Bring your most complex scenario where automation seems complex, and we will build one that transforms your enterprise.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT