$120K/year saved. 36,000 tickets/month handled autonomously.

This case is retained on the site not as a lead gen case study — but as proof that our AI engineering goes deep.

Every lead gen system we build for B2B clients uses the same underlying technical stack we proved at enterprise scale on this DTC brand: custom AI models, production-grade n8n workflows, Shopify / CRM / calendar integrations, 24/7 autonomous operation, zero-hallucination guardrails.

When a B2B client asks *"can your AI really run 24/7 without breaking?"* — the answer is: we've been running it for a Shark Tank brand at 36,000 tickets/month for over a year. The lead gen systems we build use the same rigor.

• 🔧 Custom AI classification model — Trained on 18 months of historical tickets to classify incoming messages into 18 categories with 95%+ accuracy
• 🔧 AI response generation with guardrails — Only answers from the brand's knowledge base. Unsure = escalates to human instantly.
• 🔧 Full Shopify integration — Real-time order lookup, status tracking, customer history pulled into every AI response
• 🔧 Freshdesk API orchestration — Ticket creation, status routing, tagging, agent assignment, unlimited sending
• 🔧 Multi-store support — Handles 2 Shopify storefronts with different policies, different products, different routing logic
• 🔧 n8n production workflow — 3-min polling, 100% uptime across 12+ months
• 🔧 Automated daily operations reporting — Founder gets a daily summary of ticket volume, categories, escalations, resolutions

Building AI systems that work is harder than running prompts against GPT-4 and hoping. It requires real systems engineering — guardrails, integrations, error handling, monitoring, graceful degradation, zero-hallucination design.

When we build your B2B lead gen system, we bring that same engineering rigor. Same production-grade workflows. Same error handling. Same 24/7 reliability. The difference is we're pointing the AI at sourcing buyers instead of answering tickets.