Situation

The client is an AI-powered analytics platform serving the financial services industry. Its tools are built to help lenders, underwriters, and risk managers evaluate loan decisions for consistency and identify potential bias, using public datasets like HMDA alongside the organization's own proprietary data management process.

For a product built on data, getting the data infrastructure right is not optional. But like many early-stage companies, the team was operating with reporting and data processes that had developed organically rather than by design. Legacy reports existed. Data was being used. But there was no formal architecture connecting the pieces, and no structured documentation of what the system should look like as the platform scaled.

Before the team could confidently move forward with development, they needed to understand what they had, what they needed, and how to get from one to the other.

Solution

ProsperSpark engaged as a consulting partner to help the client think through and document their data infrastructure needs from the ground up.

The work focused on two primary deliverables:

Data warehouse architecture diagram

ProsperSpark mapped the client's data ecosystem end to end, designing a diagram that captured sources, flows, storage layers, and how the pieces connected. The diagram gave the team a shared visual reference for where data came from, where it needed to go, and how it should be organized inside a proper warehouse structure. Rather than continuing to reverse-engineer existing reports, the client now had a forward-looking design they could build toward.

Business Requirements Document (BRD)

ProsperSpark also produced a BRD that translated the architectural design into a structured program plan. The document covered what the data infrastructure needed to do, what the key requirements were, and how the current reporting landscape mapped into the new design. It gave the development team the context and requirements they needed to move forward without reinventing decisions that had already been made.

Both deliverables came out of close collaboration with the client, including calls to align on requirements and validate direction before finalizing the outputs.

Results

The client came into this engagement with an operational data challenge and a development roadmap that needed grounding. They left with both a technical plan and the documentation to support it.

The architecture diagram gave the team a clear picture of what the data warehouse should look like at a structural level. Instead of piecing together requirements from scattered reports and undocumented assumptions, the team could reference a designed system. That shift from reactive to planned infrastructure matters especially in a regulated space like fair lending analytics, where consistency and auditability are part of the product value proposition.

The BRD gave the development team something to work from. Requirements were documented, the scope was defined, and the path from current-state reporting to proper infrastructure was laid out. That kind of upfront clarity tends to reduce expensive misalignment later in development.

For a startup moving quickly in a space that intersects AI, financial regulation, and large public datasets, having a structured technical foundation to build from is a meaningful operational advantage.