Workflow discovery
Map triggers, inputs, decisions, actors, systems, delays, and exception paths.
Automations & Workflows
Turn repetitive, fragmented work into observable automations and workflows across software, AI agents, integrations, and human judgment.
01 / THE PROBLEM
Automate the stable path. Design the exceptions.
Automation often copies an unclear process into brittle integrations. When inputs vary or a system fails, work becomes harder to inspect and recover.
A strong fit when
- Teams moving information manually between systems
- Operations with repeated triage, preparation, reporting, or routing work
- Growth and service teams coordinating repeatable work across tools
- Businesses that need a reliable human-in-the-loop workflow
02 / THE SYSTEM
Capabilities connected around the outcome.
01 02 Automation design
Choose deterministic rules, AI-supported steps, and human review according to the work.
03 Systems integration
Connect approved services with clear data mapping, idempotency, retries, and audit context.
04 Agentic workflow design
Add bounded AI tasks where context and judgment are useful, with explicit tools, approvals, and stop conditions.
05 Operational visibility
Expose state, ownership, failure, cost, and recovery so the process remains manageable.
03 / APPROACH
Make the risky decisions testable early.
We map the current operation, separate rules from judgment, define source systems and ownership, then automate in reviewable stages with explicit exception handling.
Typical outputs
- Current-state workflow map
- Automation opportunity and risk model
- Target workflow architecture
- Integrated automation
- Monitoring and recovery playbook
Working principles
- Fix the process before encoding it
- Rules and judgment stay distinct
- Exceptions have owners
- Every workflow can recover
04 / Practical answers
Before we start.
The answer depends on the data, providers, hosting model, user permissions, retention requirements, and consequence of the use case. An AI engagement should document approved sources, data flows, model and tool access, retention choices, permission boundaries, human review, logging, and deletion responsibilities. No sensitive data should be sent to a model or service merely because it is technically available.
Related thinking
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