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Creating Contextual Human-Centric AI to Drive Business Outcomes

Grounded answers with citations. Rules-aware retrieval. Policy-as-code governance. Deployed on-prem or in the cloud, measured by task-level KPIs.

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Context + Semantics = Business Outcomes

Your AI is only as good as its context. Progress Data Platform fuses knowledge graphs, vectors and multi-model search to ground every answer in your enterprise truth, reducing hallucinations, improving precision, and cutting retrieval cost. Every response links back to sources so teams can trust, verify, and act.

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Semantic Enterprise Discovery

Content Generation

Business Benefits of Progress Data Platform

Accuracy

Delivers precise answers by understanding the context and nuances of queries.

Expertise

Integrates specialized knowledge for industry-specific insights.

Adaptability

Learns and improves responses over time for better future outcomes.

Compliance

Facilitates information adherence to current regulations and standards.

Timeliness

Provides up-to-date responses by handling dynamic data sources.

Customization

Adapts to specific enterprise needs and operational contexts.

Efficiency

Streamlines decision-making processes, saving time and resources.

Competitive Edge

Offers faster, informed responses for a market advantage.

Customer Experience

Enhances interactions with accurate and relevant support.

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How Contextual, Rules-Aware Progress RAG Works on the Progress Data Platform

Prompt → Retrieval (graph + vector) → Rules check → Answer + citations

Multi-Model Database Store and Query Documents​ 1 1 Ingest content as-is into a multi-model database, curate and harmonize it into the relevant model required for both the LLM and other downstream applications, making it extensible for future applications and services. 2 Semantically tag your private content using classification based on ontologies, taxonomies, entity and fact extraction— all within a semantic platform that helps you turn your content into a knowledge graph of your data. 3 Using a semantic platform, tag your queries against this knowledge graph and identify relevant content within your data, relating to the subject and context of the queries. 4 Create a prompt using our Augmented Prompt Generation approach, including a hybrid search against the semantic knowledge graph, increasing relevancy and accuracy for the user.​ 5 Pass this prompt to the generative AI and get an answer that is then re- validated against the knowledge graph and the reference documents to promote accuracy. Semantic Platform Knowledge Capture Generative AI 4 5 2 3 User Queries Ingest content as-is into a multi-model database, curate and harmonize it into the relevant model required for both the LLM and other downstream applications, making it extensible for future applications and services. 1 Ingest Content Semantically tag your private content using classification based on ontologies, taxonomies, entity and fact extraction—all within a semantic platform that helps you turn your content into a knowledge graph of your data. 2 Multi-Model Database Enterprise Private Documents Using a semantic platform, tag your queries against this knowledge graph and identify relevant content within your data, relating to the subject and context of the queries. 3 Semantic Platform Knowledge Capture Generate a prompt from relevant content, the semantic knowledge graph and the user’s query. 4 User Queries Pass this prompt to the generative AI and get an answer that is then re-validated against the knowledge graph and the reference documents to promote accuracy. 5 Generative AI

Every answer is re-validated against your knowledge graph and reference docs, then shipped with citations for improved trust.

Progress Data Platform delivers the most advanced suite of tools, designed to power your AI with your enterprise data. This includes:

  • Automated Semantics for Progressive AI

    This combines two agile and scalable approaches to graphs and vectors in our data platform.

  • Progressive Graphs

    Users can proactively create graphs, and knowledge models of your data and business, helping to drive real-time semantic search and identify gaps in your enterprise graph.

  • Progressive Vectors

    Progressively vectorize your content based on your use case/query, without having to vectorize all your content. This reduces costs by four times, ten times, one hundred times, depending on your usage patterns. All this provides you with a platform that can easily switch embedding models without the costs common in other platforms.

Building your generative AI solution on Progress Data Platform means building an AI data platform for future innovation, supporting multiple use cases and helping to maintain the safety and security of your enterprise data. Progress Data Platform applies human intelligence to your data, at a machine scale and provides one progressive platform, one agile architecture, one source for all your AI data-centric innovation.

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Product Spotlight

To learn more about the key products that make up the Progress Data Platform and how their features leverage your enterprise data, visit our AI solution pages for MarkLogic and Semaphore.

MarkLogic AI

Securely leverage all your enterprise data to build scalable and trustworthy generative AI (GenAI) applications with a flexible architectural pattern.

Semaphore AI

Semaphore provides data in context and domain-specific knowledge for explainable, relevant and trustworthy generative AI results.

Creating Contextual Human-Centric AI to Drive Business Outcomes

AI's value isn't in volume, it's in outcomes. At Progress, we make AI contextual and human-centric: every answer is grounded in your enterprise data and shipped with citations so teams can trust, verify and act.

The Progress Data Platform powers trustworthy applications and insight engines with rules-aware retrieval and task-level evaluations to raise accuracy, plus policy-as-code governance, lineage and audit trails to meet compliance. Deploy on-prem or in the cloud and measure impact with clear KPIs, delivering trust, scale and value responsibly and ethically.

Embracing the Future of Data with Artificial Intelligence

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