Workflows that survive the day everyone is out.
Most processes work fine until the person who knows the exception is on leave. Automation is how a process stops depending on institutional memory that lives in one head.
This is the less glamorous cousin of AI automation and often the higher-value one. Plenty of operational pain is not a reasoning problem — it is a routing problem, an approvals problem, or three systems that do not talk. Fixing that deterministically gives you something predictable, cheap to run, and auditable.
We map the process as it is actually performed, including the exceptions people handle informally, then rebuild it with explicit ownership at each step, timeouts on anything that can stall, and a record of who approved what.
What we build
Approval chains
Multi-step approvals with delegation, timeouts, and escalation, so a request cannot sit unnoticed in someone's inbox for a week.
Cross-system sync
Keeping records consistent between CRM, finance, and operational tools, with conflict rules decided up front rather than discovered during an incident.
Onboarding and offboarding
The checklist that spans six systems and currently lives in a document, turned into a tracked process with accountability per step.
Scheduled operations
Recurring jobs — reconciliation, exports, reminders — that alert when they fail rather than failing quietly and being noticed a month later.
Exception handling
Explicit paths for the cases that currently get handled by someone who "just knows", so the process does not degrade when they are unavailable.
How the engagement runs
- 01
Document reality
The process as performed, including the informal exceptions.
- 02
Decide ownership
Every step gets an owner, a timeout, and an escalation path.
- 03
Automate the mechanical parts
Deterministic where possible; AI only where judgment is genuinely required.
- 04
Instrument
Failures alert. Throughput is visible. Nothing stalls unseen.
This is a good fit if
- A process crossing three or more systems or teams
- Approvals that stall and are chased manually
- Compliance or audit requirements that need a defensible record
- Growth that has made an informal process stop scaling
We’d turn this down
- Processes about to be replaced by a new system — automate after the migration, not before
- A single-step task one person does twice a week
- Situations where the real problem is that two teams disagree on who owns a decision. Software will not settle that
Questions we get about this
- Is this different from AI automation?
- Yes, and the distinction matters. Workflow automation is deterministic — rules, routing, integrations — so it is predictable, cheap to run, and easy to audit. We reach for AI only on the steps that genuinely need judgment. Most engagements end up as a mix, weighted more to rules than people expect.
- Can you use our existing automation platform?
- Usually. If you already run Zapier, Make, n8n or similar, we will build there when it fits and tell you when it does not. Low-code platforms hit real ceilings around error handling, versioning and volume, and it is better to know where that ceiling is before you reach it.
- What happens when a workflow breaks?
- It alerts, retries where retrying is safe, and escalates to a named owner when it is not. Every run is logged. The failure mode we design hardest against is the one where a workflow stops and nobody notices for weeks.
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