The world of generative AI, large language models (LLMs), co-pilots, chatbots and the like are a crowded noisy space to say the least. Noisy and hyped up. Trying to make sense of that noise in an enterprise context, where the promise seems obvious and yet the practical application remains elusive, has been impossible for most organizations.
With that in mind, we want to help organizations make sense of what is needed in order to bring real, tangible, domain-specific generative AI to specific use cases in the enterprise, and in particular what underlying capabilities are critical to running or building GenAI applications that help business users and teams do their work in meaningful new ways.
This is where hila Enterprise comes in. hila Enterprise is a platform for safely and reliably building and running domain-specific generative AI applications on structured and unstructured data in an enterprise. With hila Enterprise, anyone can use one of our pre-built GenAI applications, e.g. Conversational Finance, for a fast start to AI, or use our platform's application development components to build their own genAI enterprise applications for custom use cases. We work with strategic partners, such as KPMG and Cognizant to bring it all to life.
We know we're in a crowded space, and lots of platforms and tools sound alike. So, let's take a closer look.
What are the underlying capabilities of hila Enterprise that make it so unique? Here are the five+1 things to know about hila Enterprise.
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LLMs, by their very nature, hallucinate. Yet, to make productive use of these systems, hallucinations must be mitigated, as enterprises need precision. In the enterprise context, hallucinations are proving to be an insurmountable barrier for any serious productive use, keeping AI to IT experimentation or pockets of personal use in organizations.
Why? We've spent years becoming data driven. Building mountains (or lakes) of data, so that we can be exact in our insight gathering. When we apply this to the use of AI tools in critical functions, not pockets, then we can clearly see hallucinations as a major barrier to widespread use in mission-critical scenarios.
Let's take the example of finance teams considering use of natural language AI tools. Any business user relying on generative AI to answer questions about financials, operations, headcount, contracts and other mission-critical matters, needs generative AI applications (whether built on large foundation or public models, custom LLMs, or open-source) to be exact. These applications' outputs would be forming the basis of presentations to the board, of earnings calls and SEC filings, or the basis for hiring and firing decisions to name a few.
It is easy to quickly see in this finance example that hallucinations in mission-critical functions within an enterprise are unacceptable.
This is why we've built hila Enterprise to eliminate hallucinations at a level unlike any other tools out there. Specifically, our benchmarks show a 100% reduction in hallucinations.
How? We far surpass conventional methods, such as RAG techniques. We use a consortium of sophisticated models that work collaboratively to scrutinize and enhance the accuracy of generated answers. This consortium of models acts as a robust quality control mechanism, identifying inaccuracies and refining responses.
In addition, our text-to-SQL pipeline takes a similarly unique approach to counter hallucinations specifically within structured data. We use multiple LLMs, each with its unique strengths and capabilities to verify outputs and identify hallucinations.
Both techniques maximize the benefit of our model-agnostic approach. We tap into the best models for the purpose, provide the quality control on the outputs, and improve correctness. We provide the mechanisms to prevent hallucinatory content from ever reaching the user, a critical aspect of building trust (and adoption) by business users working in mission-critical functions.
We harness the power of fine-tuned and locally deployed LLMs to address specific challenges such as anti-hallucination, text to SQL conversion, language translation, and vectorized embedding. By tailoring our models to excel in these specialized and domain-specific tasks, we can improve the accuracy of generated outputs, and significantly improve speed and efficiency. Specifically, we can achieve better than a 96% accuracy improvement (additional services took that number up to 100%). In addition, our approach to leveraging fine-tuned and local LLMs means we can keep your data private and within your landscape (keep reading for more on security).
With hila Enterprise, we empower users with advanced information extraction capabilities, encompassing both content within documents and comprehensive details about the documents themselves. That means hila Enterprise can extract business data as well as metadata from thousands of documents, with superior accuracy at a much lower cost (4x lower cost). Superior accuracy is driven by the underlying extraction techniques in hila Enterprise which uses multiple LLMs with agents that communicate with each other. Lower cost is driven by using our advanced extraction techniques in hila Enterprise as compared with using a large public model.
We provide a system that can flexibly work across structured and unstructured data, enabling users to ask any question in natural language of their diverse enterprise data via a single, unified user interface.
For the data-driven enterprise mentioned earlier, the ability to seamlessly navigate through diverse data types to get accurate responses is foundational to widespread adoption. Whether users are asking questions of data that resides in systems of record, spreadsheets, PDFs, charts, or simple text, hila Enterprise covers it. If our decades of work with enterprises have taught us one thing, it's that companies operate on diverse data sources and systems, and those have to be captured for insights in order to make them valuable. Otherwise, it's a partial view, or, in the world of generative AI, it's only a partial response.
We don't think there should be tech stack or cloud vendor constraints on using the power of generative AI. So we built hila Enterprise to work across any cloud infrastructure or technology vendor, any public LLM such as ChatGPT, Claude, etc., any custom LLM as well as our own LLMs, as the unifying AI layer. Whether a company operates in a multi-cloud environment or utilizes various technology vendors (and most do), our platform provides a cohesive and integrated solution. This flexibility provides a much richer opportunity when building and running genAI applications for specific use cases.
Here is the +1. We talked about it earlier, but it's worth highlighting on its own. Security is paramount in an enterprise. Your data has to be private. It's what reflects your strategy, your differentiation, your values, and all the things that make you unique to your customer base and stakeholders. These days, the prompts are just as proprietary as the responses, no? We don't take this lightly. Your questions, answers and data remain private, and remain behind your firewall.
To sum it up – what's so special about hila Enterprise? Well, several things, including eliminating hallucinations, the lynchpin for widespread adoption by business users, enabling users to query both structured and unstructured data for richer and more nuanced responses across diverse data, and massively improving flexibility, speed, accuracy and cost with our everything-agnostic approach.
Looking for a hint of what genAI in your enterprise might do? Go to www.vian.ai and sign up to experience hila firsthand.
Want to see the full capabilities of hila Enterprise and discuss your specific use case? Get in touch here. We'd love to show you more.