Movate’s Propel Labs is riding the W(AI)VE with its latest innovation: a powerful content curation engine designed to make enterprise data AI-ready for GenAI-powered RAG solutions.

At the heart of any successful GenAI implementation lies high-quality, context-rich data and that’s where intelligent curation becomes crucial. Without proper curation, even the most advanced AI models risk hallucinations, irrelevant answers, and diminished ‘trust’.

Movate’s ready-to-deploy solution streamlines the process of extraction, enrichment, and transformation of large-scale, diverse datasets, ensuring that the right content is retrieved in the right context; by semantically organizing enterprise knowledge, it empowers AI systems to better understand user intent and deliver highly accurate, source-grounded responses.

Enterprise function leaders are overwhelmed by vast amounts of untapped data scattered across content management systems. They rely on a variety of tools to create and modify content, thereby leaving behind a trail of fragmented and siloed information. As unstructured data heaps unchecked, accessing timely, relevant insights become increasingly difficult, especially when it’s needed most.

The top 3 features of the solution (elaborated below) include: multi-CMS connectors, graph RAG-based entity extraction, and RAG-ready curation and APIs.

The pain point

Organizations often store massive volumes of information across disparate Content Management Systems (CMS) like SharePoint, Confluence, and various other platforms in the market. Much of the content is unstructured, inconsistently tagged, and not readily usable by GenAI solutions such as RAG architectures.

The well-known fact in today’s AI economy is that data readiness is key to successful AI implementations. In the context of content curation, the lack of data readiness creates significant barriers to leveraging enterprise knowledge effectively and adds complexity to developing AI-driven applications like chatbots and intelligent content search tools.

Why a shift in focus needed

Industries are rapidly investing in building GenAI solutions to stay competitive and enhance automation. However, there’s a growing gap between model capabilities and the quality of data fed into them. Without focusing on structured, curated data, even the most advanced GenAI tools risk underperformance and mistrust.

• GenAI performance depends on high-quality, well-structured, and contextual data.
• Most enterprise content is unstructured and unsuitable for direct AI consumption.
• Lack of data curation leads to hallucinations, poor relevance, and low trust in GenAI.
• Intelligent curation enables metadata tagging, entity extraction, and traceable retrieval.
• Structured, curated content ensures scalable, compliant, and high-impact GenAI outcomes.

A novel solution

Movate’s Software-as-a-Service (SaaS) solution streamlines the entire automation pipeline right from ingesting and cleansing to semantically structuring enterprise content across multiple CMS platforms. The solution ensures data is AI-ready, enabling users to access relevant, contextual information instantly.

Powered by a unique architecture and an advanced tech stack, the solution from Movate’s Propel Labs integrates seamlessly with GenAI and RAG workflows. By leveraging embeddings and vector databases, the content curation engine empowers organizations to build intelligent, context-aware AI assistants/agents and deliver consistent, enterprise-grade search experiences.

Distinguishing traits of the solution

Drive seamless AI integration & intelligent content retrieval. Here are the 3 key features:

Multi-CMS connectors help to seamlessly ingest content from SharePoint, Confluence, Google Drive, and other CMS platforms using secure, scalable connectors as they supports full sync, incremental updates, and content permission mapping.Graph RAG-based entity extraction: Automatically extract and map key entities from ingested documents—such as date, Line of Business (LOB), document type, owner, region, product, and more by using an LLM-powered semantic parser and knowledge graph builder.

Enriched metadata powers include faceted retrieval in RAG workflows, contextual conversations with better grounding, knowledge graph visualization and relationships between content.

RAG-ready curation & API: Applies advanced chunking, semantic summarization, and vectorization techniques to transform raw enterprise content into optimized, GenAI-ready formats for RAG workflows. Movate’s curation engine intelligently prioritizes high-relevance content by using metadata, entity tags, and usage patterns, thereby ensuring that the most ‘contextually’ valuable information is retrieved at the very first search or prompt.

“Movate’s solution offers a plug-and-play API that integrates seamlessly with LLM-based chatbots without needing extensive custom configuration or third-party tools; the solution supports traceability through document-level citations and embeds metadata in a consistently unified format.” – Krishnan Gopalrao, VP, Movate AI Propel Labs.

The differentiating factors

The distinguishing traits of the RAG-native solution are its explainability, open plug-and-play, and enterprise readiness.

• The solution is purpose-built for GenAI RAG workflows and processes from the get-go.
• Explainability – Every chunk is source-linked and metadata tagged.
• Open, plug and play – Deployable with any vector databases and LLMs in the market.
• Enterprise readiness – Offers access controls, audit logging and Active Directory (AD) integration with Out-Of-the-Box (OOB) functionality.

Imagine sifting through decades’ worth of documentation stored in content management systems, fragmented across functions or business units with multiple documents, file formats and versions. No more of this thanks to the Movate’s content curation engine’s capabilities.

The bottom line

The team at Movate’s Propel Labs successfully piloted the solution, delivering measurable outcomes across presales and bid management functions. They validated the performance of the RAG-based chatbot and search tool using content repositories from the CMS. Continuous feedback loops were incorporated to optimize chunking and enhance the quality of retrieved results.

The outcomes speak for themselves:

• 80% reduction in manual effort to prepare documentation for AI ingestion.
• 5X faster deployment of internal GenAI apps using curated content.
• Boost credibility and user trust via citations and references for search results.
• Greater scope of content discoverability with high relevance and user’s search context, thereby reducing repetitive search queries by the users or SMEs.

Plans to scale up include helping clients with heavy documentation loads, working with solution integrators and GenAI chatbot solution providers, offering freemium and paid usage plans, and leveraging the solution as an AI-enablement layer to support the digital workplace.

Amidst tight deadlines and high-pressure moments, finding that critical presentation buried across decades of folders is no longer a hassle.

Final thoughts

Imagine a scenario where an account manager or presales team no longer needs to manually rummage through tons of documents in the CMS to search for a proposal or specific document for a client, especially during time-critical scenarios.

From raw to ready, at the click of a button, the NLP and ML-powered Movate’s content curation engine delivers precision ‘semantic search results’ summarized, indexed along with all the related documentation and citations.

Ride the next W(AI)VE of AI transformation with Movate, your go-to partner for scalable, responsible, and impactful AI-led solutions.

For demos and a closer look at the solution’s architecture and technologies, write to Krishnan.Gopalrao@movate.com

What is W(AI)VE of transformation? The W(AI)VE of transformation is Movate’s vision for the AI-driven future – a strategic approach that empowers global enterprises to accelerate growth, disrupt their industries, and challenge the boundaries with cutting-edge AI innovation. Fueled by our deep expertise working with top innovators, proprietary and partnered AI solutions, and an unwavering focus on measurable outcomes, W(AI)VE puts strategy in action to scale AI transformation across industries and business functions. Read more.

About the author

Swaminathan is a Senior Data Architect specialized in designing scalable data architectures and intelligent pipelines that power GenAI and Agentic AI solutions. With deep expertise in building AI-ready data foundations, he enables enterprises to seamlessly integrate content from diverse sources and transform it into structured, contextual assets for RAG and multi-agent orchestration.

He brings hands-on experience with tools like Azure Databricks, LangChain, OpenAI, and Claude, and excels at developing ingestion, transformation, and embedding workflows that serve as the backbone of enterprise AI applications. Swaminathan also has a strong command of Python, SQL, Power BI, Tableau, Informatica, and data governance frameworks, ensuring secure, compliant, and high-performance solutions across industries like finance, telecom, and healthcare. A collaborative leader and mentor, he aligns data and AI strategies to deliver real business impact.

Certified as an AWS Cloud Practitioner, SnowPro Core, and Tableau Data Scientist, Swaminathan is passionate about driving AI-led transformation and unlocking the full potential of enterprise data. LinkedIn