The Challenge
A PE-backed, high-growth SaaS company serving contractors had accumulated disconnected systems — CRM, marketing automation, ERP, support, billing, and product — with no unified view of the business. Leadership couldn't reliably answer which customers were at risk of churn or which behaviors drove upsell and margin. Every monthly operating review and board meeting consumed enormous human capital collecting and reconciling data — and even then, unexpected board questions went unanswered in the room.
What They Built
Synopsis unified all operational systems into a single Golden Domain semantic layer using a proprietary ETL and entity resolution engine, then layered AI-powered natural language querying, anomaly detection, predictive churn and revenue models, and automated Slack alerts — enabling leadership to query live business data in real time, including during board meetings.
Synopsis began by tackling the foundational problem: five disconnected operational systems — CRM, marketing automation, ERP, support, billing, and product — each used different data schemas with no shared entity definitions across customer records, revenue events, and product usage data. Traditional data engineering would have taken months to resolve these conflicts. Synopsis's proprietary ETL and entity resolution engine onboarded and integrated core systems within days, creating a unified Golden Domain semantic layer that represents the business as it actually operates. On top of that foundation, Synopsis layered four AI capabilities: natural language querying that allows any user to interrogate live business data without SQL; anomaly detection that surfaces unusual patterns across revenue, delivery, or customer behavior; predictive models for churn risk and revenue forecasting; and automated workflows that push alerts directly to relevant Slack channels without data specialist involvement. The result was not a fixed dashboard but a live operational intelligence layer. During a recent board meeting, every department leader presented using Synopsis, and unexpected board questions were answered in real time by querying the platform live — ending the cycle of deferred Monday answers.
AI Role
What previously required days or weeks of manual data collection and reconciliation — often locking leadership into a fixed narrative before board meetings — was replaced with instant, queryable access to unified operational data across all systems.
AI Model
Custom / proprietary
Infrastructure
• Synopsis platform (proprietary ETL and semantic layer) • CRM system • Marketing automation platform • ERP system • Support platform • Billing system • Product analytics • Slack (alert delivery)
Integration Points
• CRM + ERP + Billing + Support + Product → Synopsis ETL and entity resolution engine • ETL engine → Golden Domain semantic layer • Semantic layer → NL query, anomaly detection, predictive models • Anomaly detection → Slack automated alerts
PE-backed, high-growth middle-market companies ($30M–$500M revenue) in SaaS, field services, or similar operationally complex verticals that lack a dedicated data leader and need enterprise-grade data infrastructure deployed in days, not months.
