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I configured a HubSpot CRM pipeline for an electrical service business to create a clear and repeatable process from the first customer inquiry to job completion.
The pipeline covers new lead intake, customer contact, estimate tracking, follow-up, job scheduling, completed work, and closed outcomes.
The goal was to replace scattered lead management with a structured workflow that gives the business clear visibility into every opportunity and reduces the risk of forgotten estimates or missed follow-ups.
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The wide counters are what let it survive the home screen — in that grid of other app icons it still reads as one shape instead of collapsing into noise, which is the real test for a CRM mark. Is the gradient locked to the logo, or does it shift across the product surfaces?
Fieldwork now works from request to approved quote.
Try the demo: https://launchinseven.com/samples/fieldwork
Enter a fictional service request, see the itemized estimate, then open Operations to edit the quote, review it and export a printable quote or CSV. Records survive a reload in your browser. The demo uses fictional data and local storage; a shared live system would need its own agreed backend and access controls.
The point is to make a business workflow concrete enough to test: what the customer enters, what the team reviews, what changes status and what leaves the system.
I direct scope and review; GPT-6 Astra handles implementation and many checks. This is an original working sample, not a paid client case.
If an existing web workflow is causing friction, message me on Contra with the URL or redacted screenshots and what should happen. We can agree a small paid first milestone—such as a documented workflow review or a defined interface fix—with its deliverable, fee, inputs and timing confirmed before work.
I'd keep a short change history beside the itemized estimate, so the team can see what changed before approval and which version was exported.
Built an AI-powered customer support automation workflow that can classify incoming emails, identify support requests, retrieve relevant information from a knowledge base, and assist with generating the right response.
The workflow combines AI agents, RAG, embeddings, Pinecone, Gmail, and automated notifications to reduce repetitive support work while keeping human review in the loop.
The flow includes:
📩 Incoming email detection
🤖 AI-based support request classification
📚 Knowledge base retrieval with Pinecone
🧠 Context-aware customer support agent
✍️ Automated response draft generation
🔔 Reviewer notification through Telegram
⚡ Separate handling for non-support emails
I’ve been exploring more ways to combine AI + automation + real-world business workflows, rather than using AI only as a chatbot.
More automation experiments coming soon. 🚀
#AIAutomation #ArtificialIntelligence #Automation #AIAgents #RAG #n8n #Pinecone #OpenAI #WorkflowAutomation #CustomerSupport #GenerativeAI #MachineLearning #BusinessAutomation #Developer #FullStackDeveloper
