Challenge: Need solution recommendations from natural language inputs, with accurate product identification and configurations based on customer needs.

Solution: RAG-based chatbot engine for centralized knowledge repository and natural language querying (Gen AI, Azure\AWS\GCP Vector Search, RAG, Rest APIs)

Outcome: 90% faster service delivery through reduction of manual effort from 4 hours to minutes. Led to improved customer satisfaction via personalized recommendations and simplified data access.

Challenge: Planning inefficiency in warehouse operations due to missing product information, resulting in bottlenecks and space allocation issues.

Solution: Agentic data retrieval, online search, web scraping, and extraction of accurate product data. (Crew AI, Vertex AI, Gemini, Custom Search Queries, REST APIs)

Outcome: Enhanced warehouse efficiency with optimal space utilization and improved collaboration. Reduced operational costs by minimizing errors and streamlining inventory management.

Challenge: Streamline post-meeting processes with AI agents that can join live discussions and summarize critical information.

Solution: Agentic notetaker that combines proprietary and commercial models for conversational analytics (neural networks, fine tuned LLMs, dynamic RAG, etc.)

Outcome: 70% time savings on post-meeting tasks and faster access to information via chatbot and integration to existing systems.

Challenge: Reduce time and cost of ServiceNow catalog item creation, testing, and deployment.

Solution: Multi-modal input RAG model on ServiceNow capabilities with chat interface and browser use agent. (RAG, Vector Store, GPT API Expertise, Customized Browser Use Agent)

Outcome: 80% faster configuration of ServiceNow catalog items, freeing up developer time and bringing new service catalog items to production faster.