{"id":59824,"date":"2026-03-04T11:48:26","date_gmt":"2026-03-04T11:48:26","guid":{"rendered":"https:\/\/dmi5324.dotlogicstest.com\/?post_type=case-study&p=59824"},"modified":"2026-06-23T15:35:06","modified_gmt":"2026-06-23T15:35:06","slug":"case-studyai-evaluation-enterprise-search","status":"publish","type":"case-study","link":"https:\/\/dmi5324.dotlogicstest.com\/pl\/case-study\/case-studyai-evaluation-enterprise-search\/","title":{"rendered":"Evaluating AI Responses for Enterprise Search Platform"},"content":{"rendered":"\t\t
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The Client<\/span><\/b>\u00a0<\/span><\/h2>

A major technology company approached DATAmundi with a critical task: review the AI-generated responses behind their enterprise search platform. These responses covered highly specialized domains including finance, healthcare, and technology, so accuracy mattered. The client needed to confirm that outputs were grounded in real source material and did not introduce unsupported information that could damage user trust. With such high-quality expectations, they were looking for a partner able to deliver expert data annotation on a global scale.<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The Brief<\/span><\/b>\u00a0<\/span><\/h2>

Execute a comprehensive evaluation framework to assess AI-generated summaries against source passages and human-generated responses across diverse specialized domains. Each evaluation needed to verify grounding accuracy,\u00a0identify\u00a0hallucinations, and meet the client’s exacting quality standards.<\/span>\u00a0<\/span><\/p>

The project also\u00a0required\u00a0measurable reviewer consistency using inter annotator agreement (IAA) metrics, including Percent Agreement (how often reviewers selected the same label) and Fleiss\u2019 Kappa (agreement beyond random chance). The client defined a target Fleiss\u2019 Kappa score, a benchmark used to confirm that expert reviewers reached consistent conclusions rather than agreeing by coincidence.<\/span>\u00a0<\/span><\/p>

The Challenge<\/span><\/b>\u00a0<\/span><\/h2>

The difficulty was not defining the evaluation\u00a0criteria but\u00a0applying them reliably at scale.<\/span>\u00a0<\/span><\/p>

The material covered multiple highly technical subject areas, which meant judgement calls could not rely on generic annotation guidelines alone. Evaluators needed enough subject familiarity to recognize when an answer was technically correct, partially supported, or subtly misleading.\u00a0Small differences\u00a0in interpretation between reviewers could quickly introduce inconsistency into the dataset.<\/span>\u00a0<\/span><\/p>

Consistency, therefore, became a central risk. The project required tight calibration across reviewers, clear decision frameworks, and continuous quality monitoring to ensure that identical cases received identical judgements regardless of who reviewed them.<\/span>\u00a0<\/span><\/p>

Early measurement confirmed this challenge:\u00a0when reviewing the\u00a0initial\u00a0Fleiss\u2019 Kappa scores,\u00a0inter-rater reliability analysis showed low statistical agreement, despite reviewers appearing to agree in\u00a0roughly 80\u201390% of cases. This discrepancy suggested that alignment was often superficial, with evaluation criteria being interpreted differently,\u00a0particularly when\u00a0determining\u00a0whether an AI response introduced unsupported information<\/span>\u00a0<\/span><\/p>

At the same time, the client\u2019s internal review process was extremely thorough. Any inconsistencies or weak justification in the annotations would be flagged during expert validation. The delivery approach, therefore, had to prioritize reviewer alignment, documentation clarity, and defensible evaluation decisions from the outset.<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Our Approach<\/span><\/b>\u00a0<\/span><\/h2>