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Workflow audit + baseline
Map the happy path, exceptions, owners, systems of record, touch time, elapsed time, and the KPI the workflow is supposed to move.
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We work close to the operators, redesign one business workflow, and own the engineering path from current-state mapping through evals and production rollout.
Make one workflow materially better and prove it in production. Productization comes later, after repeated paid proof.
Map the happy path, exceptions, owners, systems of record, touch time, elapsed time, and the KPI the workflow is supposed to move.
Remove needless coordination before adding AI. Classify each step as deterministic software, agentic judgment, or a human checkpoint.
Implement the smallest useful system, create golden examples, measure failure categories, cost, latency, and explicit fallback paths.
Integrate with the systems already running the business, start with the smallest unit of autonomy, and keep actions auditable.
Monitor exceptions, drift, model cost, permissions, reliability, and business KPIs. Expansion is earned by production evidence.
Stable inputs + stable rules. Fast, cheap, testable, and easy to audit.
Variable inputs or bounded decisions with examples, evals, and controlled tools.
High-risk or weakly supported decisions with the evidence assembled for a fast review.
We start where our engineering background is strongest and where the workflow can be measured: US B2B SaaS and tech-enabled service teams coordinating work across CRM, email, spreadsheets, analytics, and internal systems.
This is a market hypothesis, not a fabricated case study. Paid proof decides whether it stays, narrows, or changes.
If the sprint shows meaningful expected value, the next step is a scoped build. If it does not, stopping is a valid result.
Discuss a workflow ↗