Case Study
Roko Labs built embedded operational analytics for a financial content platform serving Wells Fargo, Merrill Lynch, BlackRock, and Edward Jones. Customer interviews surfaced two metrics that mattered: submission volume and time-to-approval. Three months of discovery-first build delivered 12 new reports and 75% less time spent on external reporting.
Client
Content distribution for Wells Fargo, Merrill Lynch, BlackRock, and Edward Jones.
Industry
Project Duration
The Challenge
The platform manages content submission and approval workflows for asset managers serving Wells Fargo, Merrill Lynch, BlackRock, and Edward Jones. It runs at scale, but clients had limited visibility into how those workflows performed.
Client-internal teams needed submission volume, approval timelines, and bottleneck data. Most were exporting raw data and rebuilding reports to calculate time-to-approval and throughput. That created duplicated effort, inconsistent definitions, and fragmented insight across organizations.
The fix had to live inside the product, not outside it.
Our Vision
Roko Labs partnered with the client to move workflow data inside the product as embedded operational intelligence.
The objective was not just new reports. It was surfacing the metrics that mattered most, including submission volume and time-to-approval, directly inside the workflow. Once embedded, clients would see performance immediately, without exporting anything.
The work needed more than interface updates. It needed a new data foundation that could feed analytics in real time.
Approach
Apache Superset was already in the platform from an earlier data warehouse modernization. Rather than introduce a new reporting tool, Roko Labs expanded the Superset deployment into the embedded analytics layer.
The integration deepened against the core data models, standardizing metric definitions and tightening alignment between workflow data and visualization logic. The open-source foundation kept architectural control with the team while accelerating delivery.
Future metrics ship faster on this stack, with cleaner governance over reporting logic and a scalable path for analytics growth.
Process & Outcome
The engagement ran on a discovery-first principle. Customer interviews, rapid prototyping, and technical validation ran in parallel, ensuring every metric was both useful to users and scalable inside the platform.
The Results
The engagement ran on a simple principle: validate before building. Customer discovery, rapid prototyping, and technical validation came before any engineering commitment. By the time we built, we had already aligned on the metrics, the visualizations, and the architecture that would ship.
