oimrqs ops
English proof-of-process / synthetic data / no client claim

n8n / Make / Zapier / AI workflow reliability

A complete AI workflow path, shown from intake to audited handoff.

This proof shows how I structure automation work for roles that need practical production thinking: clean incoming data, run one controlled LLM/API step, route review, write to business systems, and leave retry/log evidence instead of a black-box automation.

Concept demo Synthetic run data No contact path

Workflow map

One run path needs observable states, not just connected nodes.

The useful proof for automation work is a small trace that a buyer, manager or engineering lead can inspect without sharing secrets or production data.

01

Intake event

Webhook, form, inbox, CSV row or scheduled pull enters with a run ID and source timestamp.

02

Data cleanup

Required fields, formats, dedupe key, source system and invalid rows are made explicit.

03

API / LLM step

Prompt variables, model call, API payload, cost markers and confidence rules stay visible.

04

Validation queue

Low-confidence outputs pause for review instead of silently reaching customers or the CRM.

05

Business handoff

CRM, Sheet, Airtable, HubSpot, GHL or Slack receives only the approved, mapped fields.

06

Retry + runbook

Failures, replay rules, owner action and residual risk are written down for the next operator.

Normalized input

safe sample
Source Inbound role-fit form
Intent AI workflow build request
Systems n8n, LLM API, HubSpot, Slack
Dedupe email + company + request hash

AI step

guarded output
0.82 confidence

The model classifies the request, drafts a summary, and flags missing context. Anything below the approved threshold goes into review before it reaches a customer-facing or CRM workflow.

Handoff payload

mapped fields
HubSpot: company, contact, automation type Sheet: run ID, state, missing fields, cost marker Slack: human-readable summary + action needed Runbook: replay rule, owner, known limits

Synthetic run log

The operator should see why a run passed, paused or failed.

Run State Decision Evidence
aiwf-2046 Clean Synced CRM writeback + Slack note
aiwf-2047 Duplicate Skipped Dedupe key matched previous request
aiwf-2048 Review Paused Missing budget + confidence below rule
aiwf-2049 API error Retried 429 backoff, replay succeeded

Weak automation

It works once, but nobody can trust it.

  • LLM output goes directly into business tools without a confidence gate.
  • Retries create duplicates or hide partial failures.
  • CRM and Slack receive different versions of the same event.
  • There is no runbook for the next operator.

Reliable first build

One path is small, inspectable and ready to extend.

  • Every run has an ID, source state, mapped output and final decision.
  • AI output is reviewed when confidence or required context is weak.
  • CRM, Sheet and Slack handoffs use the same normalized payload.
  • Error handling and replay steps are documented.

Acceptance gates

What this proof is meant to signal in an external role email.

Fit

Use for English roles asking for n8n, Make, Zapier, AI workflow, LLM API, CRM, Sheets, Airtable or Slack automation examples.

First slice

One end-to-end path from source event to reviewed business handoff, not a vague promise to automate everything.

Validation

Run IDs, normalized payloads, confidence rules, skipped duplicates, retry evidence and a short runbook.

Limits

No fake client case, no private data, no credentials, no contact form, no off-platform CTA and no claim of shipped production work.