8d • Plug-and-Play
Creating a Knowledge Engine in the Context of Business
To own a relationship means all things relative, assuming you could graph the relationships, are trackable.
A Knowledge Engine creates a knowledge graph based on this assumption. Cognee is an example of a SaaS that does this at a generous free scale (bridges context across multiple models without drinking through token costs). LLMs do this on autopilot, mostly out of necessity for themselves. They can extrapolate next best steps based on how well they understand the steps being related.
When it comes to lead generation (i.e. cold emails, direct outreach, social ladders), the premise of being understood when it feels like you're throwing darts is a direct derivative on how well you understand who you're trying to talk to.
This is why the ICP exercise is so powerful. You get to see the world your ideal customer profile (ICP) lives in, the pains they feel and the trials they experience.
A Knowledge Engine is what turns your OpenClaw into something beyond useful, or really any AI setup.
An example of this is a setup that replaces notetakers in meetings with real time transcription feedback. It basically joins on your behalf and functions as a participant.
If you wrap this in an Electron app, you could replace Cluely and its $20 subscription entirely, but then you incur usage cost, so it's a different billing meter.
Useful? Sure. Valuable with a knowledge engine? Excessively.
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Creating a Knowledge Engine in the Context of Business
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