Granite Retrieval Agent provides developers with a flexible platform to build retrieval-augmented generative AI agents that combine semantic search and large language models. Users can ingest documents from diverse sources, create vector embeddings, and configure Azure Cognitive Search indexes or alternative vector stores. When a query arrives, the agent retrieves the most relevant passages, constructs context windows, and calls LLM APIs for precise answers or summaries. It supports memory management, chain-of-thought orchestration, and custom plugins for pre- and post-processing. Deployable with Docker or directly via Python, Granite Retrieval Agent accelerates the creation of knowledge-driven chatbots, enterprise assistants, and Q&A systems with reduced hallucinations and enhanced factual accuracy.

Cognita offers a modular architecture for building RAG applications: ingest and index documents, select from OpenAI, TrueFoundry or third-party embeddings, and configure retrieval pipelines via YAML or Python DSL. Its integrated frontend UI lets you test queries, tune retrieval parameters, and visualize vector similarity. Once validated, Cognita provides deployment templates for Kubernetes and serverless environments, enabling you to scale knowledge-driven AI assistants in production with observability and security.

Cortexon transforms enterprise data into intelligent, context-aware AI agents. The platform ingests documents from multiple sources—such as PDFs, Word files, and databases—using advanced embedding and semantic indexing techniques. It constructs a knowledge graph that powers a natural language interface, enabling seamless question answering and decision support. Users can customize conversation flows, define response templates, and integrate the agent into websites, chat applications, or internal tools via REST APIs and SDKs. Cortexon also offers real-time analytics to monitor user interactions and optimize performance. Its secure, scalable infrastructure ensures data privacy and compliance, making it suitable for customer support automation, internal knowledge management, sales enablement, and research acceleration across various industries.

DocChat-Docling is an AI document chatbot framework that transforms static documents into an interactive knowledge base. By ingesting PDFs, text files, and other formats, it indexes content with vector embeddings and enables natural language Q&A. Users can ask follow-up questions, and the agent retains context for accurate dialogue. Built on Python and leading LLM APIs, it offers scalable document processing, customizable pipelines, and easy integration, empowering teams to self-serve information without manual searches or complex queries.