Emberis AI The memory layer that makes enterprise AI trustworthy (emberis.ai)

Hey everyone,

I'm Vidhi, Founder & CEO of Emberis AI. Spent the last few years deep in neuromorphic computing research, overthinking, rebuilding, and stress-testing ideas before finally landing on something that actually made sense to build. That's Emberis.

Here's the problem we couldn't stop thinking about:

• A standard RAG pipeline for a bank running fraud detection costs $60,000+ per month in LLM inference alone

• Answers come back in 7-11 seconds

• Hallucinations spike on complex multi-hop queries

• Zero audit trail; no compliance team can explain a single AI decision to a regulator

That's not an AI problem. That's an infrastructure problem. And nobody was solving it at the layer it actually needed to be solved.

So we rebuilt the memory layer from scratch, not as a search index, but as an intelligence layer that understands context, relationships, and confidence. Fast enough to answer before the LLM loads. Accurate enough to stake a compliance decision on.

Where we are today:

• Sub-10ms query latency

• 67% reduction in LLM inference costs vs standard RAG

• Fully deterministic output with audit trail on every decision

• Deep in conversations with BFSI enterprises

Still figuring out a lot. But genuinely excited about the problem and the people we're meeting along the way.

Would love to connect with anyone who's building in AI infra or the RAG space, working in fintech or regulated industries, or just curious about where enterprise AI infrastructure is actually heading.

Happy to talk shop or just hear what you're working on.

-Vidhi Waghela

Emberis AI | emberis.ai