Data Fabric Solutions for Data Management
The dual challenge when it comes to enterprise data is managing the overwhelming volume it generates while finding ways to extract value from it. This unstructured information holds immense potential for improving your decision-making and fueling your innovation, but with data scattered across hundreds of disparate sources, traditional methods simply aren’t providing the accessibility or insights your organization needs.
That’s exactly why leading enterprises are turning to a knowledge retrieval system powered by AI search services. With AI, this valuable information becomes accessible, searchable, and actionable. AI-driven knowledge enables conversational experiences with retrieval-augmented generation (RAG), powers intelligent document search, summarization, discovery, and everything in between. Users can quickly find what they need when they need it, no matter the format or source.
So, how can you bring the power of AI search services to your AI-infused applications? With brand new connectors to industry-leading AI search services like Azure AI Search and Amazon Kendra from OutSystems Data Fabric, which simplifies the complexities of data integration.
In this blog post, I share how OutSystems Data Fabric helps you overcome data hurdles and drive business value.
Why AI search services matter for RAG
RAG is quickly becoming one of the most practical and scalable ways to build generative AI applications. Instead of relying solely on a large language model’s internal knowledge—which can become outdated or lack context—RAG enhances these models by pulling in relevant, up-to-date information from external sources.
This is where AI search services like Azure AI Search and Amazon Kendra shine. They sift through vast amounts of unstructured data from PDFs, documents, web pages, emails, and more to surface only the most relevant information based on a user query or prompt. The retrieved content is then passed to the LLM, giving it the context it needs to generate responses based on real, organization-specific knowledge.
The result? Higher-quality, more trustworthy outputs that are tailored to your domain. Whether you’re powering an internal chatbot, a customer-facing assistant, or a decision-support tool, AI search services ensure your generative AI experiences are backed by relevant, curated content—no model training or fine-tuning required.
Real use cases for AI search in RAG-powered applications
AI search services open the door for a variety of practical RAG-powered applications across different parts of an organization. From internal knowledge assistants to customer service automation, these tools help retrieve valuable information hidden inside documents, websites, and databases. Let’s look at some of the most common and impactful use cases.
Internal knowledge assistants
Organizations often struggle with siloed knowledge buried in PDFs, internal documentation, manuals, and policy documents. AI search can extract relevant content from these sources so an LLM can answer employee questions more accurately, which cuts down on time spent searching or escalating requests.
Customer support automation
Instead of training a model with all possible FAQs, companies can connect their support content to a search service. When a customer asks a question, RAG uses AI search to retrieve the most relevant content, enabling fast, accurate, and personalized support at scale.
Contract and document analysis
Legal and compliance teams can use AI search to pull relevant clauses or summaries from lengthy contracts or regulation documents. With RAG, the assistant can then provide concise answers, suggest actions, or summarize key details, all based on trusted source material.
E-commerce and product recommendation assistants
AI search can index product catalogs, reviews, and specifications. When paired with RAG, this can help customers make better buying decisions by surfacing accurate, rich responses drawn from up-to-date product data.
Research and report generation
Knowledge workers in sectors like healthcare, finance, or education often need quick access to complex information. RAG systems powered by AI search can retrieve facts, figures, or research content across large datasets, helping users generate detailed, high-quality reports with minimal effort.
These examples show how AI search services are a critical backbone for turning unstructured data into relevant, grounded, and actionable insights in AI-powered applications.
OutSystems Data Fabric: The foundation for delivering intelligence into every app
Building on that foundation, OutSystems now offers an even faster way to connect to AI search services. While traditionally connecting to services like Azure AI Search or Amazon Kendra required heavy custom API work, OutSystems Developer Cloud changes that with native support for these services through Data Fabric. Developers can now retrieve knowledge using simple service actions, which speeds up the creation of advanced AI use cases. IT teams benefit too, with fine-grained access control (CRUD permissions) and full usage tracking to centralize and simplify governance.
By bringing knowledge retrieval directly into the low-code development environment, OutSystems can help your team build AI-powered apps faster, with trusted, context-rich data always at hand—and all in one unified, governable platform.
Enabling new possibilities with AI search connectors
The new AI search connectors for Amazon Kendra and Azure AI Search provide pre-built integrations with these powerful AI search services, simplifying the process of adding AI search functionality to your OutSystems applications.
These connectors offer several key advantages, including:
- Unified data access: Build apps using a single virtual layer for all data, whether in relational databases, data lakes, or cloud services.
- Scalability and flexibility: Easily connect to new sources without major redesign and reduce the time spent wrangling data from different sources.
- Reliable AI outputs: Feed complete datasets into AI models to improve the quality and relevance of their outputs.
- Governance: Centralize control over AI and data access, decrease costs, and enable multi-vendor utilization.
- Time-to-value: Reduce the costs and skills required to assemble an AI team and accelerate generative AI projects and initiatives.
Your foundation for AI application development
As AI continues to evolve and shape the future of software development, the need for a strong, unified data foundation has never been more critical. From generative to multimodal to agentic AI, access to timely, relevant data is critical for making decisions, taking action, and engaging with users in meaningful ways.
With all the data types and silos, building a solid data foundation can feel overwhelming or impossible: OutSystems is here to help with a single virtual layer for all of your data, enabling you to build AI-infused applications, AI agents, multimodal AI, and whatever the next big AI thing turns out to be to interact with data consistently and securely. No custom integrations or manual data wrangling necessary.
By simplifying data access and integration across all systems, OutSystems provides the foundational layer required for scalable, production-ready enterprise applications powered by AI and AI agents, making it faster and easier to build intelligent, context-aware applications that can adapt and evolve in real time.
Learn more about OutSystems Data Fabric and the power of AI search connectors.
