The challenge
A US-based cloud SaaS provider serving the service operations industry faced two significant data challenges: one customer-facing and one internal.
The business wanted to introduce analytics and AI-powered insights directly into its software platform, giving customers access to reporting and intelligence as a native product feature. However, the platform operated on a single-tenant architecture, with approximately 1,000 separate customer databases, each containing variations in structure and schema. This made it impossible to create a unified analytics layer across the customer base.
Internally, data was equally fragmented. Multiple SaaS applications supported different business functions, but there was no master data management strategy or single source of truth. Teams lacked a unified view of customers, operations and performance, making even basic reporting difficult and time-consuming.
The solution
InsyteGroup built two unified data platforms to address both challenges in parallel.
The first platform consolidated approximately 1,000 separate customer databases into a single data lake designed for embedded product analytics. The core technical challenge was the schema variation across databases, because each customer’s data was structured differently. We solved this using AI-assisted mapping that handled schema differences at scale, normalising the data into a consistent model that could power analytics across the entire customer base.
The second platform integrated the business’s internal systems to create a true customer 360, a single, complete view of every customer relationship, combining product usage data with commercial, support and operational information from across the business.
On top of these foundations, we delivered embedded BI directly inside the SaaS platform, giving end customers access to analytics and insight as a native part of the platform. For internal teams, we deployed AI agent capabilities that enabled natural language querying across all data, advanced analysis, and automated AI-generated narratives.
The impact
For customers, the transformation created an entirely new feature set that didn’t previously exist. Customers gained access to analytics and AI-generated insights directly inside the product they were already using, with no separate tools, no data exports and no waiting for reports.
For the internal teams, the customer 360 platform delivered a complete, trusted view of every customer relationship for the first time. Natural language querying meant teams could ask questions of their data in plain English and get answers immediately. Advanced AI analysis and automated narratives were delivered at scale across the platform, surfacing insights that would have been impossible to find manually across 1,000 separate databases.
The project demonstrated that even the most complex, fragmented data environments can be unified and made intelligent, when the architecture is designed right and AI is grounded in properly modelled, semantically consistent data.
