Should we keep using Zapier or Make?

For low-volume, low-consequence workflows, yes — they are faster and cheaper to change. Anything customers notice when it breaks belongs in code with monitoring and tests. We usually migrate the second category and leave the first alone.

Can this run in our own infrastructure?

Yes. Automations deploy as services in your cloud account, using your secrets management and observability, so nothing critical depends on a third-party workflow vendor's uptime.

Do we have to replace the tools we already use?

No. Automation works best on top of the stack you have. We connect to your existing CRM, inbox, spreadsheets, and billing system through their APIs rather than asking you to migrate anything.

What happens when an automation fails?

It retries with backoff, and if it still cannot complete, the job lands in a queue and a human gets alerted with the exact input that failed. Nothing disappears silently — that is the single most important design rule.

Workflow Automation · SaaS

Workflow Automation for SaaS

Your internal ops tooling is a pile of Zaps nobody trusts.

SaaS companies accumulate operational glue: trial-to-paid handoffs, provisioning, usage alerts, churn signals, billing exceptions. It usually starts in a no-code tool and quietly becomes load-bearing without monitoring or error handling. We rebuild it as infrastructure.

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Typical stack

n8n
Make
Python
Temporal
REST & Webhooks

Use cases

What this handles for SaaS companies

Automate trial, onboarding, provisioning, and account lifecycle transitions
Route product usage signals into CRM tasks for expansion and churn risk
Handle billing exceptions, failed payments, and dunning without manual chasing
Sync product, billing, support, and CRM data so every team sees the same account

Operational glue that is monitored, tested, and version-controlled instead of quietly failing.

Context

The saas problem underneath it

SaaS teams rarely lack AI ideas. They lack the specialist capacity to build them properly alongside an existing roadmap — retrieval that is actually accurate, agents that are safe to let loose in a customer account, and inference costs that do not quietly destroy gross margin.

The AI feature on the roadmap keeps slipping behind committed work
A prototype exists but nobody is confident enough to put it in front of customers
Inference cost per account is unmodelled, so pricing is guesswork
Support volume grows with the customer base and headcount follows

What we build

Inside a workflow automation

We watch the process before we automate it. That means sitting with whoever runs it today, recording the actual clicks, and finding the branches nobody documented — the exceptions, the manual overrides, the 'except when it's this customer' rules. Then we build it, including the failure paths, because an automation that silently breaks is worse than the manual process it replaced.

Event-driven pipelines triggered by email, webhook, form, schedule, or file drop
Two-way sync between CRM, billing, support, spreadsheets, and internal databases
Conditional branching that handles the exceptions your team currently handles manually
Retry logic, dead-letter queues, and alerting so failures surface immediately
Document and report generation with data pulled live from source systems
Audit logs recording every run, input, and outcome for compliance review

Deliverables

What ships with the engagement

Process map of the current workflow with time and error cost per step
Production automation with staging environment for safe changes
Monitoring dashboard showing runs, failures, and processing volume
Alerting into Slack or email when a run needs human attention
Handover documentation so your team can adjust rules without us

FAQ