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Ask the CEO: What if customers don't have a lot of data?

Interviewer: So as anyone who plays in the data space knows, data is really important. Data quality is really important. What about customers who don't have enough or the right kind of data?

Mr. Kari: That's a really good question. I think it's very important to realize there are so many ways to actually acquire data.

You don't need to have data yourself. They are massive repositories of datasets that could fit your use case. We jump for just that initial learning before we actually start with full on automation as we start to get into the augmentation space. We could use other sources' data. There are public data sets. There are datasets that you could buy but there's also one more important thing that is so amazing with the open source movement...

20.11.2019

The Complete Guide to Bringing AI to Your Organization

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Our Most Recent Articles

The AI Lock-in: Unlocking Specialized Knowledge with CAST

"I Know Kung Fu": RAP Domain Cartridges and the Matrix Moment for LLMs

How to Train Your LLM? Memory Decoder Crushes RAG

MedGemma: A New Era for Healthcare AI

50 AI Secrets: How Every Fortune 50 Company is Using AI Right Now

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OUR LATEST ARTICLES

The AI Lock-in: Unlocking Specialized Knowledge with CAST

The biggest obstacle in enterprise AI is skill lock-in. The Cartridge Activation Space Transfer (CAST) is the breakthrough solution, instantly translating specialized Domain Cartridges (LoRA adapters) between models like Gemma, Llama and Mistral. Decouple your expertise from underlying architecture, eliminate expensive retraining, and maintain 90% performance fidelity.

"I Know Kung Fu": RAP Domain Cartridges and the Matrix Moment for LLMs

RAP moves beyond simple document retrieval (RAG) and uses lightweight, modular "Domain Cartridges" to instantly and dynamically inject specialized knowledge directly into the LLM's core parameters. Our breakthrough Fusion-Generation Engine selects the necessary expert cartridges (e.g., Law + Finance), seamlessly merges them into a single adapter, and generates an interdisciplinary response in one high-speed pass.

How to Train Your LLM? Memory Decoder Crushes RAG

For years, LLM domain adaptation has been stuck in a compromise: the immense costs and "catastrophic forgetting" of DAPT, or the frustrating latency and clunky overhead of RAG. But a new approach is here, and it feels like a generational leap. Discover the Memory Decoder, a brilliant, plug-and-play memory component that bypasses the limitations of its predecessors. By learning to imitate a retriever, this compact module supercharges your LLM, delivering both superior performance and unparalleled efficiency. Can a small, dedicated "memory chip" truly make a 0.5B model outperform a 72B-parameter behemoth? The research says yes. Read on to find out how this paradigm shift could make the old methods obsolete.

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