Shipping Client Automations: Two Real Workflows That Save Time

I craft small automations and scripts that strip away busywork and reveal signal. Code is client-owned, so I'm unable to share repos. The following are anonymized explanations of what the systems do and how they're wired.

Why automations, why now

• Speed > everything. First touch in seconds wins deals.

• Focus > noise. Bots triage. Humans close.

• Compounding value. Each message dealt with and each CSV read makes the next run quicker.

Case Study #1 — AI Lead Concierge for Inbox + WhatsApp + Facebook

Problem
Owner was drowning in messages in Email, WhatsApp Business, and Facebook. A lot of time-wasters. Serious buyers buried in the mess.

Result
Responds in real time, pre-qualifies leads, initiates conversations, and prioritizes the greatest offers so that the owner can finalize them.

User flow:

1. Incoming message hits a unified webhook.

2. Responds in a few seconds with friendly opener + 3 formatted questions: budget, timeline, use-case.

3. Lead is scored. High-intent gets escalated. Low-intent gets parked.

4. Owner receives a shortlisted ranking alongside response template and context recommendations.

5. Owner jumps in for the final close.

Actually what it does

• Channels: Gmail API or provider webhook, WhatsApp Business API, Facebook Pages' messages.

• Brain: small LLM prompt set for tone, safety, and policy.

• Guardrails: fixed limits on discounts, only the facts, no guarantees, automatic transfer for special cases.

• Memory: a light CRM row for each thread: contact, last intent, budget, timeline, status.

• Ranking: score equals intent plus budget plus timeline plus responsiveness plus history.

• Digest: hourly email/WhatsApp summary with top 5 leads and next actions.

Why it succeeds

• First response under 15 seconds typical.

• Owner quits context-switching. Attention shifts to actual buyers.

Privacy & compliance

• Minimal PII stored.

• Opt-out respected.

• All prompts and logs scrubbed of sensitive data.

Case Study #2 — Daily Ops Bot: Traffic, Sales, Inventories, Alerts

Problem
Owner spent hours merging POS exports, online orders, and site analytics to understand yesterday. No time for trend-spotting or proactive fixes.

Result
A daily "ops heartbeat" that processes data, cleans it, identifies anomalies, and produces a one-screen brief every morning.

User flow:

• At 06:30, bots retrieve: POS CSV, ecommerce orders, GA traffic, ad spending.

• It is organized by channel and by SKU.

• KPIs calculated: revenue, AOV, conversion, top SKUs, OOS risk, refunds, CAC proxy.

• Standard Anomaly Detection flags spikes/dips against 7-day baseline.

• The owner receives a WhatsApp message and an email with charts and three suggested actions.

Actually what it does

• Connectors: S3, Google Drive, direct POS export, Shopify/Woo API (if used), GA/GA4.

• Transform: consistent SKUs, time-zones, tax-inclusive revenue, channel tags.

• Metrics: rolling means, pct deltas, weekend vs weekday adjustment.

• Alerts: OOS risk if stock < lead-time demand; funnel breaks if CVR decreases > Xσ.

• Output: PNG charts and text bullets; put them in a simple dashboard.

Why it works

• Replaces manual spreadsheet time.

• Surfaces "Do this now" by noon.

My building principles

• Begin small. One project. One owner. One tangible victory.

• Explainable logic. Simple thresholds or scoring before black-box magic aka Ai.

• Cache it all. APIs get rate-limited. Caching doesn't slow down UX.

• Fail safe. On error, route to human with raw context attached.

• Privacy by default. Minimal data, minimal retention, prominent opt-outs.

Engagement model

• Discovery: map inboxes, data sources, and "definition of done.

• Pilot (1–2 weeks): ship an MVP targeting a single painful workflow.

• Stabilize: logging, edge cases, dashboards, owner training.

• Iterate: add channels, stronger scoring, better visuals.