List of Microsoft Azure AI Search Customers
Redmond, 98052-6399, WA,
United States
Since 2010, our global team of researchers has been studying Microsoft Azure AI Search customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Microsoft Azure AI Search for Application, Web and Enterprise Search from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Microsoft Azure AI Search for Application, Web and Enterprise Search include: PPL Electric Utilities, a United States based Utilities organisation with 6527 employees and revenues of $7.90 billion, AssetIntel, a United States based Professional Services organisation with 25 employees and revenues of $3.0 million, Sharedien, a Switzerland based Professional Services organisation with 21 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using Microsoft Azure AI Search, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Microsoft Azure AI Search customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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AssetIntel | Professional Services | 25 | $3M | United States | Microsoft | Microsoft Azure AI Search | Application, Web and Enterprise Search | 2024 | n/a |
In 2024 AssetIntel implemented Microsoft Azure AI Search to power an AI powered bridge inspection prototype in the United States transportation infrastructure sector. The project was developed in partnership with the Microsoft AI Co Innovation Lab, and it uses Microsoft Azure AI Search within the Application, Web and Enterprise Search category to enable semantic and visual search workflows for inspection imagery.
The implementation combined Microsoft Azure AI Search with Azure OpenAI and Azure Computer Vision to create an indexing and image analysis pipeline. The solution indexed inspection images, extracted visual features, and surfaced relevant records through semantic search and relevance tuning, supporting inspector query workflows and automated defect flagging.
Integrations are explicitly with Azure OpenAI and Azure Computer Vision, and the deployment scope targeted transportation infrastructure inspection operations and asset management processes in the United States. Governance work focused on preparing the prototype for FedRAMP aligned production deployment, including operational controls for model usage and image data handling that reshape inspection workflows and handoff processes between field teams and asset managers.
The prototype reduced processing time by over 40 percent and improved defect identification accuracy while being developed toward FedRAMP aligned production deployment, outcomes provided by AssetIntel and Microsoft AI Co Innovation Lab in the source case study.
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PPL Electric Utilities | Utilities | 6527 | $7.9B | United States | Microsoft | Microsoft Azure AI Search | Application, Web and Enterprise Search | 2021 | Neudesic |
In 2021 PPL Electric Utilities worked with Neudesic to build a web application that leverages Microsoft Azure AI Search to help field workers find the most relevant information while onsite. The engagement targeted field operations in the United States and focused on improving operational knowledge access for crews performing maintenance and service tasks.
The Microsoft Azure AI Search implementation was delivered as an Application, Web and Enterprise Search solution, incorporating semantic search capabilities, document indexing, relevance tuning and query pipeline configuration to surface operational knowledge and documentation. The project architecture centered on a cloud hosted search service plus a browser and mobile accessible web UI, with automated indexing routines to ingest and refresh content used by field crews.
Neudesic implemented connectors and integration patterns to bring content from existing content repositories and document stores into Microsoft Azure AI Search, enabling unified search across disparate operational content. The deployment was structured to support onsite access by field teams, with search relevance and result ranking tuned to common field queries and task oriented information needs.
Governance work included content indexing policies, relevance tuning cycles and workflow definitions for content owners to maintain searchable documentation. The implementation improved information access for field crews and streamlined operational knowledge retrieval as reported by the project, with the Microsoft Azure AI Search application explicitly serving the utilities field operations and maintenance business functions.
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Sharedien | Professional Services | 21 | $2M | Switzerland | Microsoft | Microsoft Azure AI Search | Application, Web and Enterprise Search | 2024 | n/a |
In 2024 Sharedien implemented Microsoft Azure AI Search to add AI-driven automated metadata tagging and semantic enrichment to its digital asset management platform, fitting within the Application, Web and Enterprise Search category. The work was executed in collaboration with the Microsoft AI Co-Innovation Lab and targeted Sharediens Switzerland-based digital asset management operations and large asset repositories.
The implementation instrumented Microsoft Azure AI Search as the central search and indexing layer, integrating Azure OpenAI for semantic embeddings and natural language enrichment, and Azure Document Intelligence for document understanding and extraction. Functional capabilities implemented include automated metadata tagging, semantic enrichment of asset records, indexed full text and extracted entities, and orchestration of tagging pipelines to feed the DAM search index, with Microsoft Azure AI Search providing query-time semantic ranking and retrieval.
Operational coverage focused on the digital asset management function, impacting metadata curation, asset ingestion workflows, and operations teams responsible for asset lifecycle management. The rollout was coordinated through the co-innovation engagement with Microsoft, including phased onboarding of large repositories and configuration of metadata schemas and quality gates to support automated tagging and human review workflows.
Outcomes reported with the implementation include a projected reduction in manual tagging effort and operations costs estimated at 10–25 percent, and an expected acceleration of time-to-value for large asset repositories. Governance changes introduced standard metadata schemas and tagging validation checkpoints to sustain semantic tagging accuracy and operational consistency.
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