Chris Buys Houses - Full Operations Backend
Chris ran his lead pipeline through two separate platforms - DataSift for property data and GoHighLevel for sales outreach. Nothing talked to each other. His team was updating both by hand every time a lead moved stages, and every morning he'd sit down and manually process updates before he could start real work.
- Two-way sync between DataSift and GoHighLevel - dispositions, attempt tracking, pipeline stages, and sold status all flow in both directions automatically
- Bulk operations DataSift didn't support on its own - tier tagging across two priority levels, auto-list cleanup, bulk sold status updates, and overlap dedup between tiers
- Centralized API layer - every DataSift action runs through one helper workflow with built-in retry logic, so if a call fails it retries with a fresh token before giving up
- Token manager with shared state via Google Sheets - all 12 workflows pull from the same session token. If it expires mid-run, the token manager re-authenticates, updates the sheet, and hands the fresh token back so the workflow picks up without failing
- Slack notifier handling both error alerts and a daily activity log - record counts, what ran, what didn't, errors flagged
Cross-System Sync
When a lead's disposition changes in GoHighLevel, this workflow picks it up via webhook, validates the payload, grabs a fresh token, searches DataSift for the matching owner by phone, updates their phone status, and logs everything to Sheets with a Slack notification at the end. If the owner isn't found or the API errors out, those get logged separately.
Tracks call and SMS attempts from GHL back into DataSift. Finds the matching property, updates attempt counts for phone and SMS separately. Results get aggregated and logged. If the property doesn't match, it gets marked and reported instead of dropped.
When an opportunity moves to the sold stage in GHL, this catches it and bulk-marks the matching record as sold in DataSift. Hardest workflow in the whole build - both platforms had their own definition of "sold" and getting those to agree took more time than most of the other workflows combined. Has its own token refresh logic in case the session expires mid-call.
The reverse direction. When a contact gets marked sold in REISift, this looks them up in GHL, finds the matching opportunity, tags the contact, and moves the opp to the sold stage. Handles cases where the contact exists but the opportunity doesn't, or where the contact isn't in GHL at all.
Daily Batch Operations
Runs on a schedule. Pulls leads that qualify for Tier 1 priority (HOT FTM and 4+ criteria) from DataSift and tags them in bulk through the centralized API helper. This was a manual process Chris did every morning - now it runs on autopilot.
Same idea for Tier 2 leads. Pulls from two different list criteria (3+ and Lists) and tags them through the API helper. Runs right after the Tier 1 job.
Dedup logic. If a lead qualifies for both Tier 1 and Tier 2, it should only show as Tier 1. Removes the Tier 2 tag from overlap leads so Chris's team isn't seeing duplicates across priority lists.
The most complex daily workflow. Searches DataSift for "soon to be sold" records, normalizes the data, pushes sold status updates to GHL, then bulk-marks them in DataSift too. Has its own token refresh with retry logic - if the search call gets a 401, it force-refreshes and retries before alerting Slack. Logs both success and failure counts.
Cleans up DataSift's auto-generated lists on a schedule. Removes records that shouldn't be there anymore. Runs through the same centralized API helper as everything else.
Infrastructure
The backbone of the whole system. Every workflow that talks to DataSift calls this first. It reads the current token from a shared Google Sheet, checks if it's still valid, and either returns it or logs into DataSift to grab a fresh one. The sheet acts as shared state - if two workflows run at the same time, they both pull from the same token instead of fighting over sessions. Has a daily safety check and a Slack alert if login ever fails.
Centralized API layer. Every DataSift action in the entire system routes through this one workflow. It grabs the token, makes the API call, and if it gets a 401 it force-refreshes the token and retries once before flagging the error. Logs successes and failures to Slack with separate paths for zero-match results, real errors, and retry failures.
Dual-purpose notification hub. Real-time path: any workflow can call this to send an error or event alert to the Slack errors channel immediately. Daily path: runs at 8 PM CT, builds an end-of-day digest with everything that happened - how many records were updated, what ran, what errored - and posts it to the logs channel. If the digest itself fails, that error gets sent to the errors channel too.
Client Onboarding Automation
A new client fills out an intake form. From there, everything happens automatically:
- Creates a project folder structure in Google Drive
- Spins up a Trello card with a pre-built checklist
- Sends a branded HTML welcome email to the client
- Logs the onboarding event to Google Sheets
- Posts a summary to the team's Slack channel
- Error handling on every external node so nothing fails silently
Shopify Custom Order Intake
Built for a custom product shop (demo brand: "Timber & Thread Co."). When a Shopify order comes in with customization details buried in the order notes field:
- DeepSeek AI extracts the specs (size, material, color, engraving text, whatever the customer typed)
- Creates a structured production task in Airtable with all specs broken out
- Sends a branded HTML confirmation email to the buyer with their customization details listed back to them
Revision Request Tracker
Monitors a Gmail inbox for emails that look like revision requests. When one comes in:
- AI parses the email to extract what the client wants changed, which project it's for, and how urgent it is
- Logs it to Google Sheets with all fields structured
- If the AI flags it as urgent, fires a Telegram ping to the project owner immediately
- Sends an acknowledgment email back to the client so they know it was received
Inbox to CRM Pipeline
Not every email deserves CRM attention. This workflow:
- Watches Gmail for new messages
- Filters out marketing, newsletters, and noise automatically
- Runs the remaining emails through DeepSeek to extract sender, intent, urgency, and relevant details
- Creates a structured entry in Notion with all that context
- Posts a clean summary to Slack so the owner can scan it without opening email
Lead Triage System
When a lead comes in through any web form:
- Normalizes the form data regardless of which form platform sent it
- DeepSeek scores the lead on quality (budget signals, urgency, fit)
- Logs to Google Sheets with the score and reasoning
- Fires a Slack alert with the score so the sales person knows whether to prioritize it or let it sit
Cold Outreach Engine
A Google Maps scraper feeds prospect data into a two-account Gmail outreach system with automated follow-ups. The whole pipeline runs on its own.
- Google Maps scraper pulls business data by niche and location - a free alternative to paid lead databases
- AI writes each cold email from scratch based on the prospect's business - no templates
- Two Gmail accounts with staggered send windows (cold: 8-10am, FU1: 10am-12pm, FU2: 12-2pm)
- Cross-account dedup so the same prospect never gets emailed twice
- Row-level tracking in Google Sheets (even rows = Account A, odd = Account B)
- Bad email format detection and auto-skip
- Five rotating templates per follow-up stage
Facebook Lead Scout
Passively monitors 20 Facebook groups for potential clients:
- RSS scanner watches all 20 groups on a loop
- AI classifier reads every post and decides GO, MAYBE, or SKIP based on buying signals
- Dedup sheet so no lead gets surfaced twice
- Telegram alerts on every GO with context and the direct link to the post
