Webinar | July 23 at 10 AM PDT
Build reliable domain-specific models with Memory Tuning
General purpose LLMs hallucinate, making them making them unreliable for high-value enterprise use cases where accuracy is non-negotiable.
We're empowering enterprises to systematically eliminate hallucinations from LLMs so they can confidently deploy high-accuracy, domain-specific agents at scale—without a fleet of AI PhDs.
Memory Tuning makes it possible to efficiently add new knowledge to your model and retrieve the exact right facts. We use two technologies—LoRAs and Mixture of Experts (MoE)—to enable you to turn any open LLM into a Mixture of Memory Experts.
In this webinar, you’ll learn:
Why RAG alone can't eliminate hallucinations—and where it breaks down
How Memory Tuning systematically adds knowledge to a model’s “brain” without full fine-tuning
Real-world results: How Fortune 500 companies achieved 95%+ accuracy on high-value use cases
Step-by-step: How to Memory Tune your LLM for your domain-specific facts, schemas, and critical tasks
