It's like giving every technician a genius sales coach who already knows what the customer needs before they knock on the door.

Robby's agentic upsell engine ingests structured data from ServiceTitan (job history, equipment age, prior quotes, membership status) and enriches it with third-party property and demographic signals to build a real-time customer intent profile. A predictive lead-scoring model ranks each upcoming visit by upsell potential, while an LLM-powered agent generates personalized, context-aware talking points tailored to the specific technician's communication style and the customer's history. The system operates autonomously before each dispatch, pushing prep sheets to technicians via mobile without requiring any manual input from office staff. Post-visit, the agent captures outcomes and feeds them back into the scoring model, creating a continuous learning loop that improves recommendation accuracy over time. Early results suggest customers are uncovering six figures in weekly revenue opportunities that were previously invisible.

It's like Waze for revenue — instead of rerouting you around traffic, it reroutes your technicians toward the money they're already driving past.

It's like giving the owner of a plumbing company the same real-time revenue dashboard that a Fortune 500 CEO gets, but built for trucks instead of trading floors.

Robby's strategy layer sits atop its operational and go-to-market AI agents, aggregating every technician interaction, upsell attempt, conversion outcome, and documentation event into a unified revenue intelligence graph. Time-series forecasting models project weekly and monthly revenue trajectories based on current pipeline and historical seasonality patterns, while anomaly detection flags underperforming technicians, declining close rates, or emerging demand spikes in specific service categories. Clustering algorithms segment customers by lifetime value and churn risk, enabling leadership to allocate marketing spend and technician capacity with precision. The dashboard surfaces actionable recommendations — not just charts — such as "Technician A's HVAC upsell rate dropped 22% this week; recommend ride-along coaching" or "Demand for water heater replacements is trending 40% above forecast in ZIP 30309; consider adding a crew." This transforms home service business owners from reactive operators into proactive strategists, closing the analytics gap between blue-collar field services and white-collar SaaS businesses.

It's like Moneyball for plumbers — instead of batting averages and on-base percentages, you're optimizing close rates and revenue per truck roll.

It's like having an invisible assistant riding along on every service call who writes up perfect notes and files them before the technician even gets back in the truck.

Robby deploys a multimodal NLP pipeline that processes technician inputs — voice memos, photos, short-form text — and transforms them into structured visit documentation conforming to ServiceTitan's data schema. Named entity recognition extracts equipment models, part numbers, fault codes, and customer requests, while a classification model tags each finding by urgency, revenue potential, and required follow-up action. The agentic orchestration layer then autonomously syncs these structured records into ServiceTitan, triggers follow-up workflows (e.g., quote generation, membership offer), and flags discrepancies for office staff review. This eliminates the documentation bottleneck that causes most home service companies to lose critical revenue signals between the field and the back office. The system also builds a longitudinal customer record that feeds back into the predictive upsell engine, compounding data quality over time.

It's like autocomplete for paperwork — except instead of finishing your sentence, it finishes your entire job report and files it while you're still wiping your hands.