# AI/LLM OFFERINGS

## CUSTOMIZED RAG CHATBOT

### Problem Statement
TBD

### Sales Pitch
TBD

### Use Cases

* **Customer Support**: RAG chatbots reduce response times and enhance the resolution of customer queries, leading to improved customer satisfaction scores and retention rates.

* **Sales and Marketing**: By providing personalized recommendations and assistance, RAG chatbots drive lead generation, conversion rates, and overall sales performance.

* **Human Resources**: RAG chatbots streamline employee onboarding, training, and HR inquiries, freeing up valuable time for HR professionals to focus on strategic initiatives.

* **Knowledge Management**: RAG chatbots facilitate knowledge sharing and retrieval within organizations, ensuring that employees have access to accurate information whenever needed, thereby fostering a culture of continuous learning and innovation.

### Arch Diagram

### Training

### Action

### RAG Architecture

## KNOWLEDGE GRAPH

* **Probelm**

The industry is grappling with the exponential growth of unstructured data, leading to challenges in data management, knowledge extraction, and the efficient leveraging of this information for decision-making. Businesses are finding it increasingly difficult to navigate vast repositories of data, understand complex patterns, and derive actionable insights. This challenge is compounded by the need for rapid adaptation to technological advancements, competitive pressures, and the demand for personalized customer experiences. Additionally, there is a gap in effectively bridging human expertise with AI capabilities to enhance productivity, innovation, and problem-solving.

* **Solution**

Integration of Large Language Models and Knowledge Graphs offers a transformative approach to tackling these challenges:

Enhanced Data Understanding and Management: Knowledge Graphs organize data in a structured format, making it easier to manage, query, and understand relationships between data points. When combined with the advanced natural language processing capabilities of LLMs, it enables the system to interpret and generate human-like responses from complex data structures. This integration facilitates the extraction of meaningful insights from unstructured data, improving data usability across the organization.

Improved Decision Making: By leveraging LLMs for their predictive analytics and natural language understanding, and Knowledge Graphs for their ability to model complex relationships and dependencies, businesses can achieve a more nuanced and comprehensive analysis of data. This synergy enhances decision-making processes by providing deeper insights, forecasting trends, and identifying patterns that may not be immediately obvious.

Customized and Dynamic Solutions: Knowledge Graphs can be dynamically updated with new information, ensuring that the data remains current. When combined with the adaptive learning capabilities of LLMs, which can adjust to new data and patterns, businesses can develop more personalized and timely solutions. This is particularly beneficial in rapidly evolving industries where staying ahead of trends and customer preferences is critical.

Bridging Human Expertise with AI Capabilities: The combination of LLMs and Knowledge Graphs can create intuitive interfaces for human interaction, making it easier for experts to query, analyze, and interact with complex datasets. This collaboration enhances human decision-making, fosters innovative problem-solving, and accelerates the development of new ideas and solutions.

Automated Knowledge Discovery and Sharing: This integrated approach can automate the discovery of new knowledge by identifying connections and insights within and across datasets that were previously unrecognized. It facilitates the sharing of these insights across the organization, breaking down silos and fostering a culture of knowledge-driven decision-making.

* **Create KG using LLM**

* **Architecture**


## CONTENT GENRATION TOOL

Create AI-powered content generation tools specialized for each domain, capable of producing high-quality articles, blog posts, product descriptions, or marketing materials. These tools can help clients streamline content creation processes and maintain a consistent online presence.

## PREDICTIVE ANALYTICS SOLUTIONS

Build predictive analytics models using LLMs to analyze data trends, forecast market changes, predict customer behavior, or optimize business processes. Clients can use these solutions to make data-driven decisions and stay ahead of the competition.

## FINANCIAL RISK ASSESMENT TOOLS

Design financial risk assessment tools leveraging LLMs to evaluate investment opportunities, assess credit risks, or detect fraudulent activities. These tools can assist financial institutions in making informed decisions and managing risks effectively.

## CUSTOMER SUPPORT AUTOMATION

Implement AI-powered customer support automation systems that can handle routine inquiries, troubleshoot technical issues, and escalate complex queries to human agents when necessary. These systems can improve customer satisfaction and reduce support costs for clients.

## Other AI use cases

* **Code generator**
Gen AI based solution that automates data extraction from ER diagram and generate code based on that

* **Coding Wizard**
Solution increase developer productivity and support coding languages like Go, Google SQL, Python, Java, JavaScript.

* **Vendor Contract Co-pilot**
Understand the lengthy contracts and extract vital information from it and also automate manual task of contract building and generation of all probably asked questions.

* **Structured Data Enhancement Co-pilot**
Gen AI based Data Enrichment and Product classification

* **Media Monitoring**
AI/ML solution that creates summary of different media reports using Generative AI and NLP

* **Order Information Extraction from Emails**
Support decision intelligence cycles by extracting information from the wealth of email interactions (including attachments) such as POs, Shipment notices & more

* **Medical Record Summarization** Medical Document Categorization, Extraction and Summarization

* Cora Knowledge Assist
A Generative AI Knowledge Management product offering that can assist in knowledge-intensive structured & unstructured tasks by providing immediate, relevant and complete information on-the-fly.

* **Auto Categorization and Sentiment Analysis**
NLP & Text mining enabled auto-categorization and sentiment analysis of key themes and trends from social media chatter

* **Document Translator**
Real-time document translator that revolutionises the claim reimbursement auditing process by eliminating transcription delays and errors, thereby accelerating revenue capture


## LLM + Web



* Threat Summarization

## Knowledge graph for sales and marketing

TBD

## LLMOPs


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* **Tech Stack**
1. **Data indexing**:Spark, LangChain, Airflow
2. **Vector database**:ChromaDB, Qdrant, Milvus, Pinecone, or FAISS
3. **Application stack**:Python, LangChain, LlamaIndex, FastAPI
4. **DevOps Stack**: Docker, Kubernetes, git, CI/CD, terraform
5. **Cahcing**: Redis
6. **Semantic Caching**: VectorDB + GPT Cache
7. **LLM Experimentation**: Mlflow

* **Caching**

Text generation with large language models (LLMs) can be a relatively slow process, frequently posing significant challenges for user experience.

Responses from large language models (LLMs) can be cached to efficiently manage repeated queries or contexts. The simplest method involves using standard caching systems like Redis, which match new queries to previously cached ones exactly. However, due to the vast diversity in queries, this method may not be optimal for LLM applications. A more advanced approach is semantic caching, which utilizes embedding-based search to identify similar cached queries to the new query. This type of cache typically depends on a vector database internally.

* **Feature store**
Applications powered by large language models (LLMs) often require access to contextual data, such as customer profiles, to personalize interactions. Integrating these applications with a feature store, a concept from traditional MLOps, is particularly beneficial. Feature stores offer fast, low-latency access to a wide range of data features and ensure compatibility with existing ML models. They also support dynamic updates to data features through connections with other infrastructure components.

* **Prompt management**

Prompts are crucial for Generative AI (GenAI) applications as they encode much of the application behavior and business logic, often using numerous customized or dynamically generated prompts. While simple applications might hard code prompts into configuration files, complex enterprise systems require robust prompt management infrastructure. This infrastructure supports dynamic modifications, facilitates A/B testing, allows pre-production testing and fixes, and automates testing across different models and inputs.

* **Guardrails: Safety, compliance, and user experience**
Ensuring quality and safety in LLM-based applications is challenging due to several factors including the difficulty in evaluating generated text, the versatility of conversational systems, integration with vector search, continuous updates by LLM providers, and the complexity of testing LLM chains. These challenges necessitate robust measures at various levels of the LLM stack, such as training datasets, fine-tuning, and interceptors. Intercept techniques, known as guardrails, are critical in managing application behavior and enhancing user experience by performing checks and corrections. These include toxicity assessments, topic bans, relevance validation, contradiction checks, and hallucination detection. While basic safety and compliance measures can utilize standard libraries, more complex application behaviors may require custom-designed guardrails for greater flexibility.

* **Observability**

**Prompt Analytics**: The prompts received from users or external systems should be logged and analyzed. In particular, the prompts can be clustered and visualized to better understand the application usage.

**Hallucinating Prompts**: Prompts that deviate from the typical clusters can be flagged as outliers.

**User Feedback**: The quality analysis and detection of problematic prompts can be facilitated by capturing implicit or explicit user feedback (e.g. thumbs up/down).

**Scheduled Checks**: Continuous LLM updates can be countered with automatic quality, compliance, and safety checks.

**Monitoring**: LLM-backed applications require tracking metrics such as cache hit ratio and throughputs/latencies at different stages of the LLM chains.

**Logging**: Logging requests, responses, prompts, and automatic checks enable auditability, and support optimization and troubleshooting.