Customer Case Study
Safeguarding Enterprise LLM Applications: Enhancing Reliability with RagaAI's Guardrails
LLM
SaaS, E-commerce, Support, IT, B2B
Client Profile
RagaAI’s client, a leading e-commerce company, offers a vast range of products across various categories, serving millions of customers worldwide. Known for their customer-centric approach and innovative solutions, the client aimed to enhance their chatbot to improve customer engagement and streamline operations. They turned to RagaAI Catalyst for enhancement of their chatbot.
Challenge
The client's chatbot struggled with maintaining high response accuracy using precision-focused NLP models, delivering relevant recommendations based on real-time inventory levels with inventory-aware algorithms, and ensuring efficient query resolution during high-traffic periods with load balancing and dynamic resource allocation, impacting the overall shopping experience.
Solution
RagaAI Catalyst provided a holistic evaluation of the client's models. At the core of this solution was our propritary foundational model meticulously tuned for evaluating LLMs. This specialised model developed as RagaAI's intellectual property, became the linchpin for resolving challenges unique to the LLM domain.
Approaches
Precision-Focused NLP Models
Utilized advanced evaluation metrics and fine-tuning techniques to enhance the accuracy of the chatbot's responses.
Inventory-Aware Algorithms
Implemented real-time data integration and dynamic recommendation algorithms to ensure the chatbot could deliver relevant product suggestions based on current inventory levels.
Load Balancing and Dynamic Resource Allocation
Implemented model comparison and real-time usage monitoring, help identify and address bottlenecks, ensuring smooth performance during peak times.
Automated Validation Techniques
Deployed automated validation techniques using guardrails and firewall to continuously monitor and validate the accuracy of the chatbot's responses, reducing misinformation.
Multi-Turn Conversation Handling
The platform's semantic parsing evaluation metrics helped the system understand and accurately respond to complex, multi-part questions, ensuring coherent and contextually appropriate interactions.
At a Glance
Challenges
The client's chatbot struggled with maintaining high response accuracy delivering relevant recommendations based on real-time inventory levels with inventory-aware algorithms.
Solutions
RagaAI catalyst for comprehensive model evaluation
RagaAI’s specialised foundational model for LLM testing applications.
Results
70% Initial Response Accuracy
92% Post-Optimization Response Accuracy
15% Customer Satisfaction Rate Increase
25% Reduction in Average Query Resolution Time
20% Increase in Relevant Product Recommendations
Conclusion
Transformation:
RagaAI's solutions significantly enhanced the client's e-commerce chatbot, making it more effective and reliable for customer interactions.
Gains Achieved:
Improved accuracy and efficiency in handling customer queries, resulting in a better overall shopping experience and increased customer satisfaction.
Cost-Effective:
RagaAI provided a cost-effective alternative to in-house development, reducing operational costs while improving performance.
Other Case Studies
Safeguarding Enterprise LLM Applications: Enhancing Reliability with RagaAI's Guardrails
Safeguarding Enterprise LLM Applications: Enhancing Reliability with RagaAI's Guardrails
Read the article
Evaluating and Monitoring an Enterprise LLM Application
Evaluating and Monitoring an Enterprise LLM Application
Read the article
Advancing Geospatial Semantic Segmentation through Automated Testing with RagaAI
Advancing Geospatial Semantic Segmentation through Automated Testing with RagaAI
Read the article
