AI Workflows & Integrations

What's included

Scoping

• Workflow selection based on volume, cost, and how much a wrong output would hurt, since plenty of tasks shouldn't be automated at all
• Your current process mapped step by step before anything gets built
• Decision on where a human stays in the loop and what they're approving
• Build or buy call between n8n, Make, Zapier, and a custom service, with the reasoning written down either way

Build

• Pipeline built with retries, error handling, and dead letter queues, because API calls fail and silent failures are the expensive kind
• Prompt design and iteration against a real sample of your inputs
• Structured output with schema validation, so downstream steps get predictable data
• Model routing that sends easy steps to a cheap model and hard steps to a capable one
• Context assembled from your live data sources rather than pasted snippets
• Batching and async handling for volume work

Integrations

• CRM, ad platform, analytics, warehouse, Slack, and Google Workspace connections
• API and webhook integration for anything without a native connector
• Authentication and credential handling that doesn't involve a shared password in a doc
• Rate limit and quota management across every service in the chain

Quality and safety

• Eval set built from your own examples, so you can tell whether a prompt change made things better or worse
• Review step with approve, edit, or reject before output goes anywhere client facing
• Logging of every input, output, and cost per run
• Guardrails on what actions the workflow can take, especially anything that writes to a live system
• Rollback path when something goes wrong

Operations

• Monitoring and alerting on failures, latency, and cost anomalies
• Spend cap per workflow
• Documentation and a handover session so your team owns it
• Change process for prompt updates that doesn't require a developer

Why it matters

Most AI automation projects die in the same place. The demo works, the pilot works, and then it meets real inputs and produces something wrong that nobody catches until a client sees it. What separates a demo from a system is error handling, an eval set, and a human review step at the point where mistakes get expensive.

Scoping is the other honest part. Some tasks are cheaper to leave alone. Automation earns its keep on work that's high volume, well defined, and tolerant of a review step, like reporting summaries, lead enrichment, content briefs, ticket triage, and QA passes over campaign builds. Deciding what to leave manual is half the value of the engagement.