AI/ML
Secure AI-powered ChatBots
Build secure and tightly governed AI-powered applications without fear of exposing your data.
Generative AI is unlocking new ways to drive innovation, improve productivity, and derive more value from data. However, using third-party AI models and solutions introduces new security and governance challenges. For example, AI models can inadvertently expose data to users not authorized to see them.
Snowflake Cortex is a fully managed service that brings powerful AI and semantic search capabilities to the Snowflake platform. Most importantly, applications built on Snowflake Cortex can rely on the security and governance features of Snowflake. They can work fully within Snowflake with complete control over who can access what data. They do not require sharing any information with third-party services.
Infostrux “Secure AI-powered ChatBots” solution accelerates the build and deployment of secure Snowflake Corex-based apps. It can address use cases in customer support and feedback analysis, product recommendations, marketing and many other areas in a short amount of time
Business Use Cases
Customer Service Chatbots:- Implement personalized, context-aware chatbots in customer service that access relevant knowledge base articles, FAQs, and customer data based on specific user queries.
- RAG can retrieve the best matching information while ensuring only authorized agents can access sensitive customer data.
Personalized Product Recommendations:
- Create dynamic product recommendations for e-commerce websites and apps by analyzing user browsing history, purchase data, and product descriptions.
- RAG can retrieve similar products with relevant features while respecting user privacy by limiting access to sensitive purchase details.
Market Research and Analysis:
- Analyze large volumes of market research reports, news articles, and social media data to gather insights on industry trends, customer sentiment, and competitor activity.
- Retrieve relevant documents and identify key themes based on specific research questions while ensuring secure access to confidential data sources.
Legal Document Search and Review:
- Develop intelligent search tools for legal teams to navigate through contracts, court rulings, and other legal documents based on specific legal issues or clauses.
- Retrieve relevant documents and highlight key passages while enforcing access control based on user roles and document sensitivity.
Healthcare Information Retrieval:
- Create a secure system for healthcare professionals to access patient medical records, research articles, and clinical guidelines based on specific patient symptoms or diagnoses.
- Retrieve relevant information while ensuring HIPAA compliance and restricting access to sensitive medical data based on authorized roles and permissions.
Personalized Learning Systems:
- Develop adaptive learning platforms that recommend educational content, answer student questions, and provide personalized feedback based on individual learning needs and progress.
- Retrieve relevant learning materials and generate tailored explanations while upholding student data privacy and respecting access control requirements.
Fraud Detection and Prevention:
- Analyze financial transactions and user activity to identify suspicious patterns and prevent fraudulent activities.
- Retrieve relevant data points based on predefined fraud detection rules while ensuring secure access to sensitive financial information.
Key Benefits of the Solution
Enhanced Security and Governance:
- Precise Control: Assign granular permissions to users and groups based on roles, resources, and specific actions they can perform (e.g., read, write, delete). This minimizes data exposure and prevents unauthorized access.
- Data Security Compliance: Comply with industry regulations like GDPR and HIPAA by controlling access to sensitive data based on pre-defined policies.
- Reduced Risk of Errors and Fraud: Limit users' ability to modify or delete data based on their roles and permissions, mitigating accidental or malicious data manipulation.
- Simplified Management: Centralize and manage all access controls in one place, streamlining administration and making it easier to audit user activity.
Increased Business Agility:
- Faster Time to Insights: Streamline data access for authorized users, enabling them to analyze and leverage data more efficiently for quicker decision-making.
- Improved Collaboration: Enable secure data sharing and collaboration between teams and departments while maintaining data privacy.
What is Retrieval Augmented Generation(RAG)?
Retrieval Augmentation Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like GPT-4 (the AI model supporting ChatGPT) by adding an information retrieval system that provides the models with relevant contextual data. Through this information retrieval system, we could provide the LLM with additional information about a specific industry, a company's proprietary data, etc.
RAG and Snowflake Cortex LLMs work fully within Snowflake. Because of this, the solution can define and enforce fine-grained access controls for your data using already powerful Snowflake security and governance features.
Activities
Document Preprocessing
- Text Cleaning: Apply techniques like tokenization, stop word removal, punctuation removal, and normalization to ensure consistent representation of textual data.
- Entity Recognition: Extract named entities, like people, organizations, locations, dates, etc., from documents to create additional features that capture semantic relationships.
- Topic Modeling: Identify latent topics within documents to help the retrieval model cluster similar content and identify relevant information for specific queries.
Feature Construction
- Document Embeddings: Use techniques like Word2Vec or Doc2Vec to create compact representations of documents and queries that capture semantic similarities and relationships on text data
- Contextual Features: Extract features like document length, publication date, author information, or document category to provide additional context for the retrieval model.
- Interaction Features: If available, leverage user interaction data (e.g., clicks, ratings) to personalize the retrieval and generation process based on user preferences and past interactions
User Query Feature Engineering
- Query Segmentation: Break down user queries into smaller units (e.g., keywords, phrases) to understand the different concepts and intents behind the query.
- Query Paraphrasing: Generate alternate representations of the query using synonyms, semantic equivalences, and query expansion techniques to improve retrieval accuracy.
- Intent Detection: Classify the user's intent behind the query (e.g., information seeking, action request, comparison) to guide the retrieval and generation process.
App Construction
- Data pipeline: Combine the abovementioned techniques to build an automated, robust data pipeline that regularly processes the incoming data and prepares them for the application.
- UI: Build powerful and easy-to-use UI tailored to specific business needs
Are you ready to leap forward with your data?
No matter where you are in your data cloud journey or what industry you come from, our team of experts is ready to embed themselves into your existing structure, pinpoint the value in your data, and help you achieve your business goals.
True innovation with your data awaits. Are you ready?