Accurate Gen AI Applications That Suit Your Requirements
At Lynx Analytics, we provide organizations with advanced Generative AI and Large Language Models (LLMs) solutions through LynxScribe, our development environment and toolkit. LynxScribe components enable us to accelerate the development of customized solutions tailored to our customers’ needs, while reducing overall implementation costs. From multilingual chatbots to co-pilots and conversation simulation engines, our solutions drive meaningful results. LynxScribe will seamlessly integrate with the upcoming LynxKite 2000:MM, to be revealed at NVIDIA GTC 2025.
LynxScribe
Delivering Benefits to Our Customers
Accelerated Project Timeline: Build and deploy custom generative AI applications in hours, not weeks.
Cost Efficiency: Reduce development costs with pre-built, reusable components, and lower usage costs by combining different service APIs and hosted LLMs.
Better Accuracy: Leverage Graph AI and LLMs together for superior data retrieval and precision.
Examples of Applications Enabled By LynxScribe

Content Generator
for Marketing Campaigns
Create content quickly for different media channels such as email, social media, and print ads.

Image Generator
for Marketing Campaigns
Blend images and text to create high-quality content that automatically complies with regulations and company guidelines.


Interactive
Analytics
Interrogate your data in a dialog format using natural language; generate visualizations and get automatic summaries and recommendations for further analysis.

Specialized S
Chatbots & Co-Pilots
Obtain fact-checked responses from compliance-vetted knowledge bases, with business rules acting as guardrails.

Modular Components: The Building Blocks of Innovation
LynxScribe building blocks are model-agnostic, compatible with any foundational model or vector database provider, ensuring they adapt to evolving needs. Designed to be assembled and configured, they enable rapid customization to meet specific customer use cases and technology environments.

LynxScribe Modular Components
LynxScribe’s modular components make solution delivery flexible and efficient. Each component is designed to perform a specific function, such as:
RAG Graphs: Harness Retrieval-Augmented Generation with integrated graph technology for advanced information retrieval.
Cluster Algorithms: Analyze and visualize chat logs, Facebook group activity, and more using embedding-independent clustering algorithms (built into pre-made applications).
Embedders and Re-Rankers: Choose from a variety of embedders and re-rankers available under LynxScribe, which can be freely customized to work with RAG Graphs.
Audio Transcribers and Processors: Convert voice to text with high accuracy, leveraging intermediate phonetic translations for different languages such as Mandarin and Cantonese.
Simple Task Execution: Modify text data using prompts to perform tasks like text generation, language translation, sentiment analysis, or searching for relevant information.
LynxScribe offers a flexible and efficient environment, enabling Lynx Analytics AI engineers to prototype and experiment with concepts directly in Jupyter notebooks or their own code. Once an application is ready and enhanced as per the customer needs, it can be easily deployed on major cloud platforms like Azure, AWS, or GCP. LynxScribe’s cross-platform compatibility allows Lynx Analytics AI engineers to migrate applications quickly, whether they are shifting between cloud platforms or switching from OpenAI models to open-source LLMs (e.g., Llama 3) on NVIDIA GPU clusters—all with minimal configuration changes. It also ensures seamless integration with other applications.
Lynx Analytics AI engineers can leverage LynxScribe as an API service or directly copy and reuse components in their own codebases for rapid customization. For even greater ease, LynxKite 2000:MM will provide a user-friendly interface to visually manage workflows, simplifying the development process further. Its design is highly compatible with most LLMs and vector stores, allowing users to adapt to evolving needs, whether it is deploying applications across different infrastructures or changing the underlying AI models and vector databases.
Our advanced Graph RAG process transforms documents, codes, and other structured or unstructured inputs into a powerful knowledge base. By recognizing key terms and named entities, we construct an ontology graph, which forms the foundation of the RAG graph. To further enhance retrieval, we generate synthetic questions for information nodes and train edge weights using real chat history.
Unlike traditional RAG methods that rely solely on embedding similarity, our graph-enhanced retrieval pulls not only the most relevant text chunks but also their contextual connections through trained edges. This approach significantly improves accuracy, ensuring deeper insights and more precise responses.
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