Delivering Powerful AI Solutions With Our Gen AI framework
A flexible framework to support Gen AI applications
We develop Generative AI solutions tailored to suit individual customer needs and leverage their exclusive proprietary data to the fullest extent. Whether it's creating an expert chatbot, a digital assistant, a recommendation engine, or a sentiment analyzer, we adopt a modular and layered approach, coupled with our deep proficiency in graphs. This ensures unparalleled flexibility in developing Generative AI applications, allowing us to meet diverse requirements with precision.
Some Use-Cases for our Generative AI Platform
Expert assistant
for customer service
Provides fact-checked responses from compliance-vetted knowledge bases, with business rules acting as guardrails
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Self-serve
analytics and dashboards
Users can use natural language to interrogate enterprise data and generate visualizations
Personalization engines & content suggestions
Users are presented with personalized messages and content based on profile information, interaction history, etc. without the need to create business rules and content catalogs
Python code generator with Copilot-type functionality
Developers get automatic code suggestions and can let the system complete low-level tasks
Sentiment Analysis
Leverage Natural Language Processing (NLP) to gain insights about brand perception, customer satisfaction, product adoption & advocacy, etc.
The Gen AI applications layer offers ready-to-use solutions for the most common use cases that can be addressed by LLMs. Unlock the power of interactive dashboarding with real-time analysis, leverage chatbots for exceptional customer service and sales, or streamline your campaign and content management with automated processes.
Chatbot Builder & AI Search
Automated
Campaign Builder
ACL & Control Plane
Interactive Analytics & Dashboard
Content Manager
Customer Service / Campaign Monitoring
The application enablers layer lets you gain full control over LLMs and other foundational models. Our aim is to minimize the impact of hallucinations and incorrect answers, ensuring the highest level of accuracy. This layer seamlessly supports both the application layer and any custom applications through a Python API.
Chatbot & Information Retrieval Functions
Knowledge Graph & Data Base Query Engine
Generative AI-Supported Classification
Content Monitoring, Logging and Alerts
Generative AI-Supported Feature Engineering
General Gen AI-supported Task Solver (promptable)
The LLM Handlers layer includes comprehensive handlers for major LLM API providers such as OpenAI, Azure Open AI Studio, NVIDIA's NeMo, Vertex AI (PaLM), Hugging Face, and more. This allows you to select the best Generative AI solutions tailored to your specific needs.
Open AI API Handler
(Native & Azure)
Open-Source For Commercial Use LLMs
Vertex AI API Handler
Natural Language Processing Toolkit
NVIDIA NeMo Connector
Data & Modeling layer offers a range of benefits. Firstly, it enables you to create a knowledge graph or knowledge base by leveraging your existing structured and unstructured data sources. Furthermore, it collects logs generated by the application, providing valuable insights for fine-tuning every aspect of the platform. Additionally, you have the option to store your existing models within this layer, which can be utilized by applications supporting inbound sales or campaigns.
Templates, Bot Profiles & Parameters
Usage Logs
Campaign Management Models
Retention, etc. Models
Knowledge Base & Graph For Bot
Data Mart For Applications
Vector DB connectors (pinecone, FAISS, etc)
The Lynx Difference
The extensive utilization of Graph AI and Knowledge Graphs in our platform enables the rigorous codification of specific knowledge, thus improving the performance of any LLM for knowledge-intensive and domain-specific tasks. This approach offers several benefits:
Create Generative AI applications that can behave according to industry norms or a specific regulatory framework
Achieve greater accuracy of results and the ability to test these results with a formal test framework developed by Lynx Analytics
Support greater transparency and explainability of outputs, enhancing the trustworthiness and accountability of the system
Log conversations and visualize them to understand how the structure of a knowledge graph led to specific interactions between a user and an application
Avoid hallucinations and catastrophic forgetting by ensuring LLM-based applications rely mostly on curated data with a known structure
Our Technology Partners
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