Use Cases
What We've Actually Built
Real automations running in production for real companies. Each one started with a conversation about what was broken.
New Lead to CRM to Slack in Real Time
Problem
Leads from ad campaigns sat in platform inboxes for hours before anyone noticed. Sales was always a step behind.
Automation
New leads from TikTok (and other ad sources) are automatically created as contacts in HubSpot with full attribution, and the sales team gets an instant Slack notification with lead details.
+ AI Agent Layer
AI enriches each lead with company data, LinkedIn profile, tech stack, and estimated ARR. Scores the lead and writes a one-paragraph brief so the rep knows exactly who they're calling.
Result
Reps reach out faster with context they used to spend 15 minutes researching.
Calendly Booking to CRM + Meeting Prep
Problem
When prospects booked calls, the sales rep had to manually look up their info and prep. No-shows weren't tracked.
Automation
New Calendly bookings auto-create or update HubSpot contacts, notify the assigned rep in Slack with context, and log no-shows for follow-up sequences.
+ AI Agent Layer
AI researches the prospect's company (website, LinkedIn, recent news, funding rounds) and generates a one-page meeting prep brief: who they are, what they likely need, potential objections, and suggested talking points. Delivered to the rep 30 minutes before the call.
Result
Every call feels prepared. Reps close more because they show up informed.
Lead Follow-Up Sequence with Auto-Stop on Booking
Problem
Founders sent follow-up emails manually, or worse, sent a "just checking in" email to someone who already booked a call.
Automation
New leads from forms or ads enter an automated email follow-up sequence. The moment a lead books a Calendly call, the sequence stops. No manual tracking, no embarrassing double-sends.
+ AI Agent Layer
AI personalizes each follow-up based on the lead's company size, industry, and form responses. Adjusts send timing based on open/click patterns and rewrites subject lines that underperform.
Result
Every lead gets followed up. Nobody gets an awkward email after they already booked.
Stalled Pipeline Auto-Nudge
Problem
Deals and projects sat untouched in the CRM for weeks. Nobody noticed until a prospect went cold or a client churned.
Automation
Automated monitoring flags deals and projects that have been in the same stage too long. The assigned rep gets a Slack nudge with context, and the record gets tagged so leadership has visibility.
+ AI Agent Layer
AI analyzes why deals stall (missing next step, no recent activity, contact went dark) and drafts a re-engagement message the rep can send with one click. Escalates to leadership if multiple deals stall in the same stage.
Result
Nothing goes stale silently. Your CRM pings you before you lose the deal.
Stripe Invoice Paid to Project Kickoff
Problem
When a client paid an invoice, someone had to manually check Stripe, update the project tracker, and notify the team to start work.
Automation
When a Stripe invoice is marked paid, the project status updates automatically in Airtable and the delivery team gets a Slack message with client details and scope.
+ AI Agent Layer
AI reviews the project scope and generates a draft kickoff checklist, identifies potential blockers based on similar past projects, and suggests a timeline the PM can approve or adjust.
Result
Projects start with a plan instead of a blank page.
Deal Won to Accounting Sync
Problem
When a deal closed, someone had to manually create the client in Xero or QuickBooks, set up the invoice, and match it to the CRM record. It took 20 minutes and got missed on busy days.
Automation
When a deal is marked Closed Won, the contact and company are automatically created (or matched) in Xero with the correct billing details, and the finance team gets a Slack confirmation.
+ AI Agent Layer
AI checks for duplicate contacts across CRM and accounting, reconciles mismatched names or emails, and flags deals where the billing entity differs from the CRM contact (common with enterprise clients).
Result
Finance never asks "did this deal close?" again. Invoicing starts the same day.
Stripe Invoice Reminders with Escalation
Problem
Chasing unpaid invoices was the founder's least favorite job. Reminders went out late (or never), and overdue invoices piled up.
Automation
Automated reminders go out at 10 days before due, on the due date, and at 7, 14, and 17+ days past due. Each escalation gets firmer. Status is tracked in Airtable so finance knows where every invoice stands.
+ AI Agent Layer
AI adjusts tone per escalation stage (friendly reminder to firm follow-up) and personalizes each email with the client's name, invoice details, and payment link. Flags chronically late payers for the founder to review.
Result
Invoices chase themselves. You never write the "just following up on payment" email again.
Client Onboarding Checklist Automation
Problem
New client onboarding involved 12 manual steps across 4 tools. Steps got missed, clients waited, and the team scrambled.
Automation
When a deal closes in HubSpot, an onboarding record is created in Airtable, a welcome email goes out, Slack channels are created, and a checklist tracks every step to completion.
+ AI Agent Layer
AI personalizes the welcome email based on the client's industry, deal notes, and goals. If onboarding stalls (a step is open for 48+ hours), it drafts a nudge message for the account manager to send.
Result
Every client gets a personalized start. Nothing stalls silently.
New Task to Full Project Setup
Problem
Every new project meant manually creating a Drive folder, a doc, a Notion entry, a spreadsheet row, and subtasks. It took 30 minutes and someone always forgot a step.
Automation
One task created in Asana triggers the full setup: Google Drive folder and doc, Notion calendar entry, Google Sheets row, and all subtasks with due dates. Everything linked back to the original task.
+ AI Agent Layer
AI generates the project brief from the task description and past similar projects, pre-fills the subtask checklist with role assignments based on team availability, and sets realistic due dates based on historical completion times.
Result
One click, five systems updated. Your PM tool becomes the single source of truth.
Live KPI Dashboard from All Your Tools
Problem
MRR, pipeline, churn, and support volume lived in 3 different tools. Getting a single view meant someone spent Friday afternoons pulling reports into a spreadsheet.
Automation
Automated sync pulls key metrics from Stripe (MRR, churn), HubSpot (pipeline, deals won), and Intercom (ticket volume, response time) into one dashboard updated daily. No manual exports.
+ AI Agent Layer
AI generates a weekly narrative summary: what changed, why it matters, and what to watch. Flags anomalies (sudden churn spike, deal velocity drop, support volume surge) with suggested next steps.
Result
Leadership reads a summary instead of staring at charts. Anomalies get caught early.
Process Knowledge Base via Slack
Problem
Only one or two people understood how the automations worked. When they were out, nobody knew what connected to what or why a workflow was set up a certain way.
Automation
As each automation is built, the logic and context are captured in a searchable knowledge base. Your team asks questions in Slack ("how does our onboarding flow work?" or "what happens when a payment fails?") and gets answers grounded in your actual workflows.
+ AI Agent Layer
AI maps how your scenarios connect to each other, combines the technical analysis with human context (why it was built this way, what the edge cases are), and generates answers that cite specific workflows with the reasoning behind them.
Result
Anyone on the team can understand your ops without asking the one person who set it up. The knowledge base grows automatically as new automations are added.
Automation Health Monitoring + Auto-Recovery
Problem
Automations failed silently. The team only discovered broken workflows when a customer complained or data went missing.
Automation
24/7 monitoring watches all active automations, auto-restarts transient failures, and escalates persistent issues to Slack and PagerDuty with error context.
+ AI Agent Layer
AI analyzes error patterns to distinguish between transient blips and structural problems. For recurring failures, it identifies the root cause (API rate limit, schema change, auth expiration) and recommends a specific fix.
Result
Team fixes the right problem the first time instead of restarting and hoping.
Email Triage and Routing
Problem
The founder's inbox was a second job. Support requests, vendor invoices, partnership pitches, and customer replies all landed in the same place. Important emails got buried.
Automation
Incoming emails are automatically categorized (support, billing, sales, internal) and routed to the right person or channel. Urgent items get flagged in Slack immediately. Low-priority emails get batched into a daily digest.
+ AI Agent Layer
AI reads each email and determines intent, urgency, and who should handle it. Drafts replies for common requests (pricing questions, meeting scheduling, support acknowledgments) that the team can send with one click or let auto-send after a review window.
Result
The founder checks email once a day instead of 30 times. Nothing urgent gets missed, nothing routine wastes their time.
Nice to Haves for Your Team
Once your core ops are running smoothly, these are the automations teams ask for next.
Support Ticket to Product Feedback Loop
Auto-classify support tickets as bugs, feature requests, or churn signals. Group into themes and deliver a weekly product brief with prioritization suggestions.
Time Tracking to Invoicing Pipeline
Aggregate time entries by client and project, generate invoices in Stripe, and flag anomalies before they go out. Clients get invoices they can actually read.
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