How App Orchid Uses Generative AI and Large Language Models in the Platform

Enterprises are awash in information, yet often struggle to extract meaningful insights. There’s too much data and it’s really hard to explain what it means to a Gen AI model. App Orchid is at the forefront of tackling this challenge, utilizing the power of Generative AI (Gen AI) and Large Language Models (LLMs) to transform how business data can be made useful to both AI and humans.

Let's delve into how Gen AI and LLMs are woven into the fabric of App Orchid's offerings:

The Core: Semantic Intelligence and Knowledge Graphs

A semantic layer acts as a middleware between Gen AI and your enterprise data. Without a semantic layer, Gen AI cannot easily understand raw enterprise data, leading to hallucinations or getting answers wrong.

At the heart of App Orchid's platform is the semantic intelligence layer, powered by patented knowledge graph technology. These aren't just data repositories; they are intelligent structures that meticulously capture the structure, semantics, and business context of enterprise data.  

This foundational layer is crucial because it provides a rich, contextual understanding that LLMs need to operate effectively within a unique business environment. It's like giving the LLM a map of your entire organization's data landscape and the tools to navigate it successfully.

Automatically Enriching the Semantic Intelligence Layer with LLMs:

LLMs also play a critical role in continually enriching App Orchid's knowledge graphs, making them more comprehensive and intelligent:

• MSO, Table, & Property Context Generation: LLMs can analyze various data sources (like master data objects, tables, and properties) and automatically generate relevant context, further populating and refining the semantic layer.

• Linguistic Enrichment: This involves using LLMs to enhance the linguistic understanding of the data, adding synonyms, related terms, and other linguistic nuances that improve the accuracy and flexibility of natural language queries.

• Entity Extraction: LLMs are highly effective at identifying and extracting key entities from unstructured or semi-structured data, which can then be used to further enrich the knowledge graph and provide more granular insights.

App Orchid has automated the semantic enrichment process using LLMs.  

Conversational Analytics: Easy Answers

Imagine asking a question about your business data in plain English and getting an immediate, insightful answer – not just a raw number, but analytics and even machine learning-generated insights. This is the promise of App Orchid's Easy Answers product. Here's how Gen AI and LLMs make this a reality:

• Question to Ontology Query: When a user asks a question in natural language, LLMs are used for intent determination, disambiguation and map tokens that our platform doesn’t map to interpret the query and translate it into a precise "ontology query." This leverages the underlying knowledge graph to understand the user's intent and map it to the specific data entities and relationships within the enterprise's data. This method offers what we believe is the most accurate Text2SQL approach available in the market.  

• Resolve Ambiguity: The questions users ask can often be ambiguous. LLMs play a vital role in identifying and resolving these ambiguities, ensuring that the system understands exactly what the user is asking, potentially by prompting for clarification if needed.

• Reasoning: LLMs enable the system to perform complex reasoning over the question asked and the resulting response, answering “how did the platform answer the question”. This means the platform can connect disparate pieces of information within the knowledge graph to derive answers that require logical inference.

• Chat: A conversational interface powered by LLMs, allowing for a fluid and intuitive interaction where users can refine their questions, explore follow-up inquiries, and engage with the data as if they were talking to a human analyst.

• Summarize Results: Once the analysis is complete, LLMs are used to distill complex data and analytical outputs into concise, easy-to-understand summaries. This eliminates the need for users to wade through spreadsheets or dashboards to find the key takeaways.

• Insight Explanation: One of the most powerful applications is the LLM's ability to provide insightful explanations for the data. This goes beyond simply presenting numbers; it offers context, highlights significant trends, and even suggests potential implications, helping users truly understand "why" something is happening.

Data Security: A Top Priority

App Orchid understands that security is paramount when dealing with sensitive enterprise data. A key aspect of their LLM integration is a commitment to data security. The platform is designed so that LLMs only receive metadata or obfuscated data for processing. This crucial safeguard ensures that raw, sensitive enterprise information is not exposed to the LLMs, maintaining data privacy and compliance.

Gen AI Integration: The Best AI model for the Job

App Orchid does not lock you into a single model. Our customers can bring their own models or choose a model from leading commercial vendors (e.g. Open AI, Google, Anthropic) or open-source offerings (Mixtral, Llama, etc.). Customers can also use different models for different use cases – for example, a large context window of Gemini can handle the querying workloads, while a lower cost ChatGPT model can summarize answers.

The Future of Enterprise Data

By strategically leveraging Gen AI and LLMs, App Orchid is transforming how enterprises interact with their data. From intuitive natural language querying to powerful insight generation and continuous knowledge enrichment, App Orchid is empowering businesses to unlock the true potential of their information, making data analysis more accessible, intelligent, and ultimately, more impactful.  

Reach out to discover what AO can do for your business.  

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