Enterprise context and training-data infrastructure for AI applications
Build a system that captures an organization's operational cases, expert decisions, and historical outcomes, converts them into structured context and training data, and supplies that context to interchangeable AI models.
online B2B Deep Tech / Infrastructure
From 20VC with Harry Stebbings — Leo Aschenbrenner's Situational Awareness Blows Up | Moonshot AI Raises $3.5B at $35B at 50:23
Problem: General-purpose models lack the organization's product knowledge, customer history, configurations, prior cases, and domain-specific reasoning. Raw model intelligence may achieve about 70% accuracy while the valuable edge cases remain unresolved.
For: Enterprises and software companies that want AI to solve domain-specific support, operational, analytical, or security problems more accurately.
Products from this video
Claude Fable orchestrator Fireworks Google Drive Model Context Protocol (MCP)
Examples
- Palo Alto Networks: captures and transcribes customer cases so its systems can learn what constitutes a good or bad answer.
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
- In-database semantic analytics using large database models
- A domain-specific AI product built around private data, specialized models, and purpose-built workflows
- Continually learning AI model provider for enterprises