Specialized-domain agentic RAG research and question-answering system
Use an AI agent to combine large language models with external knowledge bases, retrieval, planning, and iterative research so the system can answer questions using current or domain-specific information rather than relying only on the model's training data.
online B2B Deep Tech / Infrastructure
From IBM Technology — RAG's Evolution: From Simple Retrieval to Agentic AI at 04:27
Problem: Large language models do not know today's information or an organization's specific documents, while traditional search may return irrelevant results and traditional RAG may fail when a single fixed retrieval step is insufficient.
For: Organizations or users that need answers grounded in current information, private documents, specialized-domain knowledge, or multiple data sources.
Behind this: 12 build steps · how to validate demand · 7 things the video never answers.
Other takes on AI model development
- Retrain small open-source language models for long-horizon agentic workloads and sell efficient inference and on-device deployments
- AI simulation company that models human behavior and lets organizations test decisions against simulated populations
- An open foundation-model company focused on coding and long-horizon software tasks
- Enterprise context and training-data infrastructure for AI applications
- In-database semantic analytics using large database models
- A domain-specific AI product built around private data, specialized models, and purpose-built workflows