How One PE-Backed SaaS Firm Lifted NRR 8%

A SaaS leadership team piped Salesforce, NetSuite, Recurly, Pendo, and Jira into one platform, lifting net revenue retention 8% and cutting board-prep time 90%.

+8%

Lifted net revenue retention

< 4 weeks

Implementation Time

$100K – $250K

Project Cost
the challenge
A PE-backed SaaS company was fighting churn and trying to get AI adopted across the business, but its operating data sat in more than ten disconnected systems of record, including Salesforce, NetSuite, Recurly, Pendo, and Jira. Nothing gave leadership one view, and preparing for the board and the monthly operating review consumed management time that should have gone to customers.
what they built
End-to-end data platform, pulling together 10+ systems of record including Salesforce, Netsuite, Recurly, Pendo, Jira, and their own product info. The system then unlocks the ability to complex analytics, multi-system automations and workflows, data science and ML models, even run agentic (human-less) task completion.
Synopsis began by building a unified 'golden domain': a single connected data layer pulling together the company's 10+ systems of record, including Salesforce, NetSuite, Recurly, Pendo, and Jira. The first working version stood up within a week. From there the focus shifted to iteration and adoption rather than further engineering. The team rolled the platform out top-down: onboarding executives first, including the operating partner and deal team, then the next layer of management, and finally every individual contributor. Each wave was used to refine the platform toward the way people actually worked. The hardest part was less technical than organizational. Driving change management and onboarding large teams in a short window is what made the tool the default way work got done rather than an optional dashboard. Within roughly a month the platform had crossed 100+ daily active users running their day from Synopsis, with cross-system analytics, automations, and agentic task completion layered on top of the unified data foundation.
best fit for
Middle-market, PE-backed portfolio companies, in both glamorous and unglamorous sectors.
Ai ROLE
impact

1 source of truth for the whole company

A company poised for a much higher exit multiple

+8% net revenue retention increase (annualized)

Better unit economics at a time where SaaS is supposed to be shrinking

90% reduction in board & monthly operating review prep

Management team spending time driving customer value vs prepping for the board
implementation complexity
Created unified 'golden domain' in 1 week. Iterated to perfection, consistently onboarding executives (including operating partner and deal team), then the next layer of management, and ALL individual contributors. Over 100+ daily active users driving their day from Synopsis.

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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industry
Technology & Software
business organization
Other
AI TYpe
Other
value type
Revenue Growth
frequently asked questions
How did a mid-size software company lift net revenue retention by 8% with a unified data platform?
The experts built a unified 'golden domain' data layer pulling together the company's 10+ systems of record, including Salesforce, NetSuite, Recurly, Pendo, and Jira, and stood up a working version within a week. The focus then shifted to top-down adoption, onboarding executives and the deal team first, then management, then every individual contributor, refining the platform around how people actually worked. Within roughly a month, 100+ daily active users were running their day from the platform, contributing to an annualized +8% net revenue retention increase.
What AI tools and approach powered the data platform?
The platform is a unified end-to-end data layer built on multiple LLMs that consolidates 10+ systems of record. On top of the unified foundation it runs cross-system analytics, multi-system automations and workflows, data science and ML models, and agentic (human-less) task completion.
What results did the software company achieve?
The company saw an annualized +8% net revenue retention increase, a 90% reduction in board and monthly operating review prep, and 100+ users accountable to using AI in their job function on a single source of truth. Better unit economics at a time when SaaS margins are under pressure.
How long did the data platform engagement take?
Under four weeks. A working first version was live within a week, and the platform crossed 100+ daily active users within roughly a month.
Who is this data platform approach best for?
Middle-market, PE-backed portfolio companies, across both glamorous and unglamorous sectors, looking to unify their data and drive AI adoption across the business.

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