Understand the process before automating it
We measure waiting, repetition, data entry, approvals and exceptions in the current flow. Rather than accelerating a poorly designed process, we remove unnecessary steps and clarify decision points and ownership.
- Process mining and observation
- Business-rule and decision inventory
- Exceptions and human intervention
- Data source and system ownership
The right mix of RAG, agents and deterministic automation
Workflow engines or RPA suit deterministic rules, RAG suits answers grounded in enterprise knowledge, and agentic AI can support multi-step tool use. The choice depends on task ambiguity and the impact of error.
- Document and request classification
- Knowledge retrieval and summarization
- Cross-system task execution
- AIOps event enrichment and resolution workflows
Governed decisions and human approval
The data an agent can access, the tools it can call and the transaction or risk thresholds it can handle are explicitly defined. High-impact steps can require approval, dual control or recommendation-only mode.
- Role- and purpose-based permission
- Pre-action policy checks
- Human approval and separation of duties
- Action logs, rationale and rollback
Observable automation in production
An automation can appear successful while creating latency, cost, misrouting or hidden manual correction. We monitor technical behavior alongside process outcomes and user feedback.
- Task success and retry rate
- Human intervention and exception queues
- Model/tool latency and cost
- Quality sampling and version comparison