It’s trivially simple to build the Enterprise Context Layer: the central intelligence that encompasses all knowledge for your company, is able to answer any questions, and self-updates.
The founders and VCs will tell you it’s the next trillion dollar company. The SaaS players will try everything to convince you that they and they alone will be the solution to solve it all. They’ll throw around words like knowledge graphs, ontologies, semantic layer, taxonomies, etc.
But what if I told you that all you need is 1000 lines of python + a github repo?
“Make an AI question bot”
Six months ago, my task was simple: build a bot that helps our GTM reps answer customer questions. Things like “will X feature be available next quarter,” “how are you different from Y competitor?”, “what’s your data retention policies?”
These sound simple. They are not.
Take “will X feature be available next quarter.” Four things have to go right for an AI to answer this correctly.
1. Product disambiguation. You need to understand what our product is and what the feature even refers to. People ask “does X feature exist” and they’re usually talking about our flagship product, inbound email security. But sometimes they’re not. We have several products that overlap, and internal names don’t always match external ones. Just getting this part right requires a well-tuned RAG system.
2. Release semantics. Does “available” mean early access or general access? Is it gated to specific customers or regions? What about EU customers, FedRAMP customers? The AI would need to know to ask a clarifying question like “where are you based?” This trips up even good retrieval systems.
3. Roadmap process. At Abnormal, we have specific processes around sharing roadmap items (e.g., signing an NDA before showing the roadmap). If there are escalations, PMs, engineers, and sometimes execs get involved. How does the AI decide whether to answer, ask for an NDA, or escalate? No system handles this today.
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