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.
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.
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.
Support Ticket to Product Feedback Loop
Problem
Customer feedback buried in support tickets never reached the product team. Same issues kept getting reported.
Automation
Support tickets tagged as feature requests or bugs are automatically logged in a product feedback tracker with frequency counts, and weekly digests go to the product channel.
+ AI Agent Layer
AI reads every incoming ticket and auto-classifies it as bug, feature request, or churn signal. Groups related requests into themes and generates a weekly product brief with prioritization suggestions based on frequency and customer tier.
Result
Product roadmap decisions backed by real customer data, not gut feel.
LinkedIn Content Pipeline
Problem
Founder wanted to post consistently on LinkedIn but spent 2+ hours per post researching, writing, and scheduling.
Automation
A content pipeline pulls from a topic bank, generates drafts with AI using the founder's voice and relevant articles, stages them for review, and queues approved posts for publishing.
+ AI Agent Layer
AI monitors industry news, competitor announcements, and trending topics to suggest timely post ideas. Analyzes past post performance to learn what resonates and adjusts tone, length, and hooks accordingly.
Result
Content stays relevant and improves over time without the founder doing research.
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.
Order Fulfillment Status Notifications
Problem
Operations team had no visibility into which orders shipped, which were delayed, and which needed attention. They checked spreadsheets manually twice a day.
Automation
Automated daily and real-time notifications for shipped items, delayed orders, and items needing secondary payment methods. Team gets a morning digest and instant alerts for exceptions.
+ AI Agent Layer
AI detects delay patterns (supplier, region, product type) and flags systemic issues before they become crises. Drafts customer communication for delayed orders that the ops team can review and send with one click.
Result
Ops catches problems upstream. Customers hear about delays before they have to ask.
Multi-Source Data Sync to Central Dashboard
Problem
Revenue, pipeline, and support data lived in 3 different tools. Getting a single view meant someone spent Friday afternoons pulling reports.
Automation
Automated sync pulls key metrics from Stripe (revenue, churn), HubSpot (pipeline, deals), and Intercom (ticket volume, response time) into a unified dashboard updated daily.
+ 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.
Blog Content from Video Transcriptions
Problem
Company recorded great video content but never repurposed it. Blog was stale, SEO suffered.
Automation
New YouTube videos are automatically transcribed, AI generates blog post drafts with SEO keywords and outlines, and drafts are staged in the CMS for review.
+ AI Agent Layer
AI analyzes the transcript to extract key quotes, generate social media snippets, suggest internal links to existing content, and create an email newsletter blurb. One video becomes 5 content assets.
Result
Content team gets a full repurposing kit for every video, not just a blog draft.
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.
Time Tracking to Invoicing Pipeline
Problem
Tracking billable hours across projects was manual. Invoices went out late, and hours were often underreported.
Automation
Time entries logged in Airtable are automatically aggregated by client and project, invoices are generated in Stripe at the end of each billing cycle, and the finance team gets a summary before they go out.
+ AI Agent Layer
AI reviews time entries for anomalies (unusually low hours for active projects, missing entries for days with meetings) and flags them before invoicing. Generates a plain-English invoice summary the client can actually read.
Result
No more "did we bill for that?" conversations. Clients get clear invoices.
Community Forum Knowledge Base Builder
Problem
Valuable Q&A from community forums was scattered and unsearchable. Support team answered the same questions repeatedly.
Automation
Forum posts are extracted, AI generates concise Q&A summaries with embeddings for semantic search, and the knowledge base grows automatically as new questions get answered.
+ AI Agent Layer
AI identifies gaps in the knowledge base by analyzing unanswered or poorly answered questions. Drafts new articles for common pain points and suggests updates to outdated entries when product features change.
Result
Knowledge base stays current and fills its own gaps.
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