Methodology, audit-trail design, the work. Never more than monthly.
Service · Project-based engagement
From a folder of half-broken Zaps to workflows your ops team trusts.
Half the businesses we meet have a folder of Zaps and Power Automate flows that worked once and now fail silently. Leads dropped, invoices unsent, the ops manager checking by hand on Friday because nobody trusts the automation. We do the boring middle layer: figure out which workflows are actually worth automating (and which should stay manual), pick the right tool for each (n8n, Make, Power Automate, Zapier, or Python), and ship them with retries, dead-letter handling, and observability so a transient API failure does not quietly corrupt your data. The same orchestration discipline is also what makes AI Integration land: AI nodes need clean, observable workflows to plug into.
What you get
A documented audit of every candidate workflow, ranked by impact, frequency, and complexity. Most engagements automate three to five; the rest stay manual with reasons in writing.
Working workflows shipped into your existing stack — n8n, Make, Power Automate, Zapier, or Python — picked on fit, not on what we already know. No "automation platform you also have to maintain."
Production-grade error handling: retries with backoff, dead-letter queues, idempotency, and alerts that fire when a workflow fails, not just when it runs.
Observability: every run logged, a dashboard for the ops team, and a weekly digest of what ran, what failed, and what is drifting before it becomes an incident.
Full handover: runbooks, credentials transfer, source-controlled exports of every workflow (so they survive a vendor change or a platform outage), and one-on-one training for whoever owns it next.
Optional retainer for ongoing care — when an API changes, a step needs updating, or a new workflow needs adding. Billed by the hour with a monthly cap you set.
How we build it
Complex jobs are composed, not monolithic. A thin orchestrator fans out to small, reusable sub-workflows under a typed data contract — each independently testable and isolated, so one failing branch doesn't take down the run. Business values live in one central config, referenced everywhere, so a change is a one-line edit, not a redeploy. Notifications and write-backs are idempotent: a key is claimed before the action and confirmed only on success, so retries and overlapping runs can't double-post or double-pay. And every workflow shares one error backbone — classify the fault, record it for replay, escalate only what needs a person.
Questions before you ask
More questions? Bring them to the discovery call — that's literally what it's for.
Next step
How it works
30-minute discovery call → workflow audit (what to automate, what to leave alone) → fixed-price build sprint → handover with runbooks → optional retainer for ongoing care.
Best for
SMEs and NGOs with a stack of disconnected tools that someone copies data between by hand every week; ops leads tired of stitching together half-broken Zaps; teams about to commission AI work who need clean, observable workflows for the AI nodes to plug into.
Tech we work in
Principal credentials
Goals, timeline, current state — whatever helps us understand the work. Free 30-minute call follows; no commitment.