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How a PE-Backed SaaS Cut Insight Time Weeks to Minutes

A PE-backed SaaS leadership team collapsed disconnected CRM, ERP, billing, and product systems into one semantic layer — answering live board questions in minutes instead of waiting until Monday.

Weeks → Minutes

Reduced time to insight

< 4 weeks

Implementation Time

Not disclosed

Project Cost

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 — a representation of the business as it actually operates. Instead of months of traditional data engineering, core systems were onboarded and integrated within days using a proprietary ETL and entity resolution engine that eliminates the need for data engineers. The platform is AI-powered end-to-end: natural language querying, anomaly detection, predictive churn and revenue models, and automated workflows that push alerts to teams via Slack without involving data specialists. Rather than delivering a fixed dashboard, Synopsis becomes the live operational intelligence layer — enabling leadership to query real 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.

best fit for

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.

Ai ROLE

The AI powers natural language querying across a unified semantic layer of all operational systems, enabling any business leader to ask questions in plain language and receive answers drawn from live, reconciled data. It also runs continuous anomaly detection, predictive churn and revenue models, and automated workflow alerts — pushing relevant signals to teams via Slack without requiring data specialist involvement.

impact

Board Meetings Run on Live Data for the First Time

During a recent board meeting, Synopsis was referenced more than the company's own name. Every department leader presented using Synopsis dashboards, and obscure questions from the board were answered in real time by querying the platform live — ending the cycle of 'we'll get you that answer Monday.'

Time to Insight Cut from Weeks to Minutes

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.

implementation complexity

Building a proprietary ETL and entity resolution engine that can onboard and integrate disparate operational systems — CRM, marketing automation, ERP, support, billing, and product — within days rather than months represents significant engineering complexity. Layering AI-powered natural language querying, anomaly detection, and predictive models on top of this semantic layer requires substantial custom development and ongoing model maintenance.

Kris Krisco

Co-Founder @ Synopsis

Synopsis

Co-Founder & CEO of Synopsis, building AI-ready data infrastructure for PE-backed mid-market companies to unify systems, enable trusted insights, and power autonomous execution.

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Financial Services

business organization

Finance & Accounting

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Executive & Strategy

AI TYpe

Data Synthesis & Reporting

Predictive Analytics & Forecasting

Conversational AI (Chatbot / Agent)

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frequently asked questions

How did a mid-size software company cut time-to-insight from weeks to minutes?

The experts unified five disconnected operational systems — CRM, marketing automation, ERP, support, billing, and product — into a single semantic layer using a proprietary ETL and entity-resolution engine, onboarding core systems within days rather than months. On top, they layered natural language querying, anomaly detection, predictive churn and revenue models, and automated Slack alerts. The result was a live operational intelligence layer that replaced days or weeks of manual data collection with instant, queryable access.

What AI tools and approach powered the operational intelligence layer?

The work combined data synthesis and reporting, predictive analytics and forecasting, and a conversational interface, built on a custom/proprietary platform (with Slack for alerting). It provides natural language querying without SQL, anomaly detection across revenue and customer behavior, and predictive churn and revenue forecasting.

What results did the software company achieve?

Time-to-insight was cut from weeks to minutes, and the company ran board meetings on live data for the first time — department leaders presented from the platform and answered unexpected board questions in real time by querying it live, ending the cycle of deferred Monday answers.

How long did the data platform engagement take?

About four to eight weeks, with core systems onboarded and integrated within days thanks to the proprietary ETL and entity-resolution engine.

Who is this operational intelligence approach best for?

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