Workflow Automation
Repetitive operational work automated with engineering discipline, reliability math included.
Automation fails in the exceptions, so we design for them first. We map the real process (including the undocumented branches), decide rules-versus-model per step, and build orchestration that survives retries, partial failures, and the API that goes down at 2 a.m. Then we measure cycle time before and after, because automation that cannot prove its ROI is a hobby.
Who this is for
Operations teams spending skilled hours on copy-paste, triage, reconciliation, or report assembly.
How the work is done
Map the actual process
Event-log analysis and shadowing surface the real flow and its variants, the mapped process is usually 60% of the truth, and the exceptions are where the cost lives.
Design the decision logic
Deterministic rules where judgment is codifiable; models where it is not, with confidence thresholds routing low-certainty cases to humans. Every automated decision is logged with its inputs for auditability.
Engineer the orchestration
State-machine design with idempotent steps, exponential-backoff retries, dead-letter queues, and reconciliation jobs, the unglamorous reliability engineering that separates automation from automation theater.
Prove the value
Before/after measurement of cycle time, error rates, and unit cost with enough samples to be statistically meaningful; a dashboard keeps the system accountable after the launch excitement fades.
How the Workflow Automation engagement runs
We begin with the decision, use the evidence that can genuinely change it, and make the reasoning reviewable from first input to final handover.
What we need to begin
- Event logs or system exports that reveal the process as run, including its undocumented branches.
- The exception cases, how they are handled today, and their volumes.
- Access to the systems at both ends, including API credentials, scopes, and rate limits.
- Current cycle time, error rate, and cost per unit, captured as the before measurement.
If an input is unavailable, we state the gap, its effect on confidence, and the agreed workaround. It is never quietly ignored.
Your four-phase engagement map
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Phase 1
Map the actual process
Event-log analysis and shadowing surface the real flow and its variants, including the exceptions where the cost lives.
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Phase 2
Design the decision logic
Deterministic rules where judgment is codifiable, models where it is not, with low-certainty cases routed to humans and every decision logged.
-
Phase 3
Engineer the orchestration
State-machine design with idempotent steps, exponential-backoff retries, dead-letter queues, and reconciliation jobs.
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Phase 4
Prove the value
Before-and-after measurement of cycle time, error rates, and unit cost with enough samples to be meaningful, plus a dashboard.
Methods and models we draw on
- Process mining & event-log analysis
- Rules vs. model decision design
- State-machine orchestration
- Idempotency & retry semantics
- Exception-queue design
- Before/after statistical measurement
Methods are chosen for the problem, not the brochure, expect a subset of these, applied properly, plus whatever the evidence demands.
The decision this enables
Hours returned to judgment work, with error rates and cycle times you can quote.