Ruben John
Mendoza

Leads arrived through Facebook lead ads and a claim-offer form and went cold within minutes. Every reply was typed by hand, so after-hours enquiries waited. Appointments were booked manually, no-shows were never followed up, and nothing asked for a review once the job was done.

• Intake — Facebook Lead Ad and claim-offer form post a webhook payload into GoHighLevel
• Response — Twilio SMS and email, first reply inside 60 seconds
• State — The CRM contact record is the source of truth and every stage transition is a status change
• Routing — A 7-minute reply window then a sentiment split into positive, negative and neutral paths
• Booking — Calendar link on a positive reply, plus reminders ahead of the appointment
• Recovery — The no-show path re-enters the same nurture sequence rather than a separate list
• Close — Review request fires only after job completion
• Hardening — Failed webhooks retry and each step fires only on a valid event payload

• The 7-minute wait plus sentiment branch is the core state machine — a hot lead gets the booking link, silence routes to call connect and voicemail, and negative sentiment escalates to a human path.
• No-show recovery reuses the pipeline rather than a separate list.
• The review request is gated on job completion so it cannot fire against an open deal.
• Every transition is driven by an event from the previous step, with no polling and no manual stage editing.

Six workflows in production — leads answered in under 60 seconds, appointments booked automatically, missed appointments recovered, and reviews requested without anyone touching a record.

Review collection depended on whoever remembered to ask. Negative feedback went straight to the public page because nothing intercepted it first, and customers who had already left a review kept being asked again.

• Trigger — A contact tag applied on job completion in GoHighLevel
• Outreach — SMS and email, sequenced rather than sent together
• Cadence — A wait interval followed by a single follow-up
• Exit — Link-click detection stops the sequence immediately
• Intercept — Low-score responses route to a private form and raise an internal alert to the team

• Exit-on-engagement is the rule that fixed over-messaging — the moment a customer clicks the review link the sequence ends whatever step it was on.
• Negative feedback is intercepted before it reaches the public page and routed to the team as an alert, so the response happens privately and fast.
• Review requests are tied to the completion event rather than a calendar schedule, so the ask always lands after the work is done.

Four workflows collecting reviews automatically, negative feedback intercepted privately, and no customer messaged twice for the same review.

Orders were processed by hand — logged into a spreadsheet, invoiced manually in Xero, and confirmed with typed emails. Nothing checked whether the customer already existed in Xero, so duplicate contacts accumulated, and invoice references were inconsistent between orders. The error rate grew with order volume because every step depended on attention.

• Intake — A webhook receives the order payload
• Logging — The order is written to Google Sheets as the running record
• Notification — Gmail sends the customer confirmation
• Resolution — A Xero contact search runs before anything is invoiced
• Branching — A router splits on the search result, so an existing contact creates the invoice directly and a new contact is created first
• Output — The Xero invoice is generated with the correct reference
• Testing — The scenarios were built and validated in ReqBin

• The contact-resolution router is the part that matters — invoicing only runs after the contact is resolved, so a first-time customer cannot create a duplicate record and an existing customer cannot create a second one.
• Five scenarios were tested end to end — new customer, existing customer, no contact match, malformed payload and repeat order — and the passing runs required zero manual input.
• The spreadsheet acts as a log rather than a queue, so nothing waits on a human to move a row.

A webhook arrives, the order is logged, the customer is notified, the contact is resolved or created in Xero, and the invoice is produced with the right reference, across five test scenarios with no manual steps.

Commercial account data lived across an enterprise ERP, a customer account platform and operational spreadsheets that did not agree with each other. Account records were matched by hand, mismatches were found late, and the daily and weekly activity figures were collated manually. That meant transcription errors, a reporting cycle that could not scale with volume, and no reliable way to tell which source was correct.

• Sources — Enterprise ERP account exports, customer account platform records and operational spreadsheets
• Extraction — Python scripts pull and normalise each source extract into a common schema
• Matching — SQL joins resolve records on a stable account identifier, with normalised name and address matching as the fallback when identifiers differ
• Duplicates — Near-duplicate accounts are flagged rather than silently merged
• Exceptions — Unmatched and conflicting records are isolated into a review queue instead of being passed through
• Reporting — The reconciled dataset drives the daily and weekly activity reports
• Output — Excel and Google Sheets carry the review queue and the published reporting workbooks

• Reconciliation runs identifier-first with a normalised name and address fallback, so records still join when source systems format the same account differently.
• Mismatches are classified rather than skipped — missing record, conflicting value, or duplicate — so the review queue is actionable instead of a dump.
• Reporting figures are derived from the reconciled dataset, which removes manual collation and the transcription errors that came with it.
• The same pipeline runs each cycle, so figures stay comparable week to week.

Account records reconciled across three sources, mismatches surfaced for review instead of reaching reporting, and the daily and weekly activity reports generated from clean data rather than manual collation.