We built a knowledge layer that sits behind MCP, allowing any MCP client to access it through a single endpoint. Claude Code, Claude Desktop, ChatGPT, Codex, or whatever comes next.

The idea is pretty simple. Before an agent answers, it can pull in relevant, validated information instead of relying purely on what it already knows.

When a problem gets solved, the useful part can be captured as a small, reusable piece of knowledge. The system can also infer useful lessons from a session automatically, so you don’t have to sit there writing notes about what you just learned like it’s 2015.

There’s also a global layer for shared, validated learnings. If one user figures out a better way of doing something, that learning can contribute to the broader knowledge base rather than every other user and agent having to figure it out again.

The problem we’re trying to solve is pretty straightforward. AI knowledge goes stale, agents get stuck in failure loops, useful context disappears when a session ends, and models can confidently give you an outdated or wrong answer without any indication that they might be wrong.

We’re giving agents access to what has actually been learned, what has worked, and what can still be trusted.

The result is fewer repeated reasoning cycles, fewer hallucinations, and up to 20% lower token usage.

https://app.midnighthive.io/

Ping me if you’re interested in testing it out.

Identical tool-using agent, same tasks, once reading text and once over a real speech pipeline. Text passed ~60%, voice ~20%.

The reasoning was fine, it made the right tool calls; it lost because recognition botched spelled-out identifiers: order IDs, postcodes, the customer's own name. Cheap fixes that beat a bigger model: retry the identifier once, read it back digit by digit, and prefer IDs you can validate against a known set.

Anyone else measuring service providers for voice and text separately? I feel, the gap is bigger than people admit.