CrushBank transforms enterprise data discovery and the path to AI readiness with IBM Bob
For most organizations, the AI challenge is not choosing a model—it’s reaching the data. Valuable business knowledge sits buried inside decades-old applications, disconnected databases, and systems built long before AI became a boardroom priority. CrushBank, an AI company focused on unlocking and activating enterprise knowledge, saw this challenge play out repeatedly. While businesses were eager to pursue AI initiatives, the information needed to power them often remained trapped in legacy applications, siloed databases, and disconnected systems.
As demand for AI adoption accelerated, customers increasingly turned to CrushBank for help bridging the gap between legacy data and modern AI platforms. The problem was not a lack of AI models. Instead, it was the effort required to understand existing systems, uncover relationships within data, and build the infrastructure needed to make that information usable. Traditional approaches depended on specialized teams and lengthy discovery efforts before organizations could realize any value. For CrushBank, the challenge became finding a faster, more scalable way to transform fragmented enterprise data into AI-ready assets while reducing the cost and complexity of adoption for customers.
~1 week
to deliver solution prototypes and demonstration environments
One‑person
product teams, working alongside IBM Bob, replaced the traditional four‑six person consulting teams
Days, not weeks
Data discovery and onboarding time reduced to a matter of days from 6 – 8 weeks
CrushBank partnered with IBM® Bob™, an AI-powered development partner, to rethink one of the biggest barriers to AI adoption: unlocking and preparing enterprise data for use. Rather than relying on traditional discovery engagements to untangle legacy applications, analyze databases, and map relationships across disconnected systems, the company incorporated Bob into every stage of the process. Working alongside CrushBank’s engineers, Bob helped analyze system architectures, uncover dependencies, generate schemas, and build the ingestion pipelines needed to transform fragmented information into AI-ready data.
The result was a more repeatable and scalable approach to data enablement that reduced much of the manual effort historically associated with these projects. CrushBank’s data environment ran on IBM Cloud® and included IBM watsonx.data®, an Iceberg-based architecture, and the Milvus vector database—enabling governed data handling and semantic search. With governance in place and the approach mature, CrushBank reimagined its delivery model. Instead of assembling large consulting teams for every engagement, the company paired individual engineers with Bob, creating AI-augmented “one-person product teams.” These teams combined human expertise with AI-assisted analysis and development, enabling engineers to take on work that traditionally required multiple specialists across data engineering, integration, architecture, and application development. Throughout the process, CrushBank maintained a human-in-the-loop review model to validate outputs, guide implementation decisions, and ensure quality and governance standards were met.
The company then transformed data residing in enterprise applications, databases, and document repositories into structured, governed, and AI-ready assets. Once prepared, the data was connected through secure MCP servers, allowing customers to access and use enterprise knowledge through the AI platforms and applications that best fit their needs. For customers requiring managed orchestration, CrushBank can also leverage IBM watsonx® Orchestrate® where orchestration fits the deployment. By focusing on data accessibility rather than a single AI destination, CrushBank created a flexible framework that enabled organizations to unlock existing information, support emerging AI use cases, and integrate with a rapidly evolving AI ecosystem.
One of the biggest changes CrushBank saw was the speed at which its customers could move from exploring AI possibilities to seeing tangible results. Instead of spending weeks evaluating systems, mapping data, and planning implementations before an AI initiative could begin, organizations were able to assess opportunities, validate use cases, and begin building solutions far sooner. The result was a more practical and repeatable path to AI adoption, enabling customers to focus on outcomes rather than the weeks of upfront discovery work that had previously been required.
The operational impact was significant. Discovery and data onboarding efforts that traditionally required six to eight weeks of specialized consulting work could now be completed in a matter of days. Solution prototypes and demonstration environments could be delivered within approximately a week of an initial engagement, allowing customers to evaluate value earlier and move from concept to execution more quickly.
The transformation also reshaped CrushBank’s own business. By pairing engineers with IBM Bob in AI-augmented “one-person product teams,” the company reduced its reliance on traditional four- to six-person consulting teams while increasing its delivery capacity. What began as a company focused primarily on AI-powered IT support evolved into a broader data enablement practice, expanding the range of industries and business challenges CrushBank could address and creating a more scalable foundation for growth. That shift—from manual effort to an AI‑augmented approach has fundamentally changed how CrushBank delivers value, while expanding what customers can do with their data.
CrushBank is an AI-driven platform that centralizes and normalizes enterprise data into a secure, client-specific data lakehouse. Built on IBM watsonx technology, it enables organizations and managed service providers to quickly find, understand, and act on information—reducing search time, improving resolution, and unlocking institutional knowledge across the business.
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Examples presented as illustrative only. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.