Overcoming the Enterprise LLM Blindspot

The past year is seeing companies racing to integrate large language models like GPT into their sales, support, strategy and other core work streams. The great promise is the ability to grant conversational access to valuable Enterprise knowledge.

But it turns out that Enterprise large language models suffer from a massive blind spot, resulting in vanilla experiences that fail to help decision makers move the needle.

Here's the problem:

What do we mean when we say ‘enterprise data’? LLMs can read content from knowledge bases, and retrieve a synthesis or summary when asked a question. They can also be provided access to internal documents or to content that lives within a particular application. But the most valuable Enterprise knowledge isn’t in knowledge bases or documents - it is in the structured data that is accumulated through actual commercial activity. It is CRM data, ERP data, IOT data - the representation of real world performance that is directly linked to company KPIs.

LLMs can’t and don’t read this data. Yet.

The most valuable Enterprise knowledge is hidden in raw data - but data is abundant and value from data is scarce. The challenge is to extract actual insights from messy and complex data ecosystems, to gain a granular understanding of your business reality and produce intelligence that informs action across the organization.

Deep and systematic insight creation used to happen in large data science projects. Making actionable insights available on demand and at scale requires a different approach and infrastructure.

LLMs don’t speak data - they speak content. The not-so-simple task here is to translate deep insights in complex data into plain language that the LLM can read. Introducing this translation layer creates a powerful mechanism that constantly surfaces new knowledge out of the Enterprise data ecosystem, allowing decision makers to access it at scale through search and conversational interfaces.

This is where SparkBeyond comes in. It extracts deep knowledge and insights out of raw enterprise data - it taps into the complex data ecosystem of a massive organization, discovers hidden patterns and reveals the drivers of optimal performance.

Then, it translates these statistical truths into plain language, allowing large language models to read and understand the company’s business reality.

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With all this in place, LLMs can now be used to power real-time experiences that move the needle on actual company KPIs. 

Here are a few examples:

In e-commerce, cart abandonment is a big problem. Shoppers show every intent of completing a purchase, but for one reason or another, never do so. 

A standard LLM interaction could look like this:

And here’s a typical response:

When the LLM has access to insights from behavioral and transaction data, the answers are mind-blowingly different:

This works with business planning as well. Say you’re tasked with planning the entry to a new market, and would like to hit the ground running - here’s how the data-aware LLM can help:

In a SaaS environment, a major effort is churn reduction. Here’s an example of deep insights translated into plain language:

And we can dive deeper:

…and deeper: