Quadric Builds Custom MCP Server to Stop AI Agents From Hallucinating About Specialized Hardware
Key Takeaways
- ▸AI agents hallucinate on domain-specific knowledge outside their training data, creating a critical gap for specialized hardware companies
- ▸Custom MCP servers can bridge this gap by connecting AI agents to live, internal knowledge bases with proper access controls
- ▸Externalizing domain expertise through AI agents frees expert engineers from repetitive questions and dramatically improves team onboarding for novel hardware
Summary
Quadric, a hardware company building specialized GPNPUs, has developed a custom Model Context Protocol (MCP) server to address a fundamental problem: AI agents confidently generate false information when asked about domain-specific hardware architecture, compiler outputs, and proprietary terminology that doesn't exist in public training data. The company discovered that while language models perform well on general questions, they hallucinate when queried about Quadric-specific concepts like their Chimera GPNPU architecture, compiler logs, and internal tools. Rather than relying on off-the-shelf MCP solutions designed for public APIs and SaaS platforms, Quadric built its own server to provide live access to internal knowledge databases with proper access controls and context awareness.
The impact has been immediate and substantial. By externalizing domain expertise through the MCP server, Quadric's Field Application Engineering (FAE) and factory engineering teams have reclaimed significant time previously spent answering repetitive questions about hardware specifications and terminology. The sales team now uses the tool to deepen product understanding without relying on expert availability, while new employees benefit from an infinitely patient AI assistant that understands the company's novel hardware stack. Hallucinations regarding Quadric's stack have been reduced to nearly zero, and the system accelerates onboarding for engineers joining a team building genuinely novel hardware.
- Off-the-shelf MCP solutions are insufficient for proprietary hardware stacks; custom implementations are necessary to handle complex access patterns and organizational constraints
Editorial Opinion
Quadric's MCP server represents an elegant solution to a real constraint in enterprise AI adoption: the mismatch between what language models know (public information) and what specialized companies need (proprietary knowledge). This approach could become a template for hardware companies, biotech firms, and other organizations building novel, non-commoditized products where domain expertise is concentrated and difficult to scale. It's a reminder that the future of AI in enterprise isn't just about better models—it's about better architecture for connecting AI agents to the specific knowledge they need.
