Enterprise Knowledge Search
Happiest Minds wanted users of its Digital Content Monetization platform to find and act on information buried in large unstructured repositories more quickly, because keyword-based search only returned matching paragraphs and limited decision speed and customer value.
Reported impact
Happiest Minds increased productivity, reduced the time needed to create business charts, and gave customers a faster, more accurate way to retrieve content insights through natural-language interaction.
Happiest Minds worked with IBM on a proof of concept that loaded unstructured repository data into IBM watsonx.ai on IBM Cloud so the company could replace exact-match lookup with AI-assisted retrieval and summarization. In the DCM platform, watsonx.ai is used to search across large collections of content, identify the most relevant passages through retrieval-augmented generation, and then summarize and rephrase the results for end users interacting through chat. That approach lets the platform turn a broad document search problem into a pointed-answer experience, which is important for a product with more than 50 catalog features and multiple downstream dashboards and business charts. The same RAG pattern also speeds internal content curation, because relevant material is surfaced faster and can be repackaged into insight views without the manual hunting and rewriting that previously slowed teams down. The solution is still being tuned through internal and beta testing, but it already gives Happiest Minds a repeatable architecture for conversational access to its content estate.
Unstructured content from the DCM platform is loaded into watsonx.ai on IBM Cloud, relevant documents are retrieved through RAG, the selected passages are summarized and rephrased for chat interactions, and the curated outputs feed dashboards, charts, and decision support inside the platform.
“IBM ran a proof of concept (POC) with Happiest Minds for two months and loaded its unstructured data into a Prompt Lab in the IBM watsonx.ai AI studio.”
“Key components of retrieval-augmented generation (RAG) were used to identify relevant documents”
“The solution’s enhanced search capabilities have delivered a massive productivity increase of 30%.”
“With watsonx.ai on IBM Cloud, Happiest Minds was able to get pointed data responses from the content repositories.”
“reduced the time required to create business charts by 15 hours per feature per month”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
IBM Sample Dec 10, 2025 Tech Mahindra Tech Mahindra needed a way to extract value from long, complex, unstructured enterprise documents while also proving that generative-AI outputs could be governed, explained, and trusted in regulated customer… 30% productivity improvement IBM Deep Search · IBM Granite 13b · IBM watsonx.ai IBM Sample Dec 9, 2025 LDS Łazewski Depo & Partners LDS needed to reduce the time, cost and risk involved in manual patent searches and drafting work so its attorneys could protect clients' intellectual property more efficiently without sacrificing accuracy or data… over 40 IP professionals IBM watsonx.ai IBM Sample Sep 8, 2025 C-Metric C-Metric needed to help a healthcare-services client search fragmented document repositories, reduce data errors and make compliance-heavy workflows faster and more reliable. 40% reduction in operational costs Aivio · IBM Granite · IBM Watson Discovery 