LLMOps
Our LLMOps (Large Language Model Operations) services are designed to help organizations deploy, manage, and optimize large language models efficiently and securely. We specialize in building scalable LLM deployment pipelines, optimizing model inference for performance and cost, implementing fine-tuning and Retrieval-Augmented Generation (RAG) integrations, and setting up robust monitoring and feedback loops. By streamlining the operational lifecycle of LLMs, we ensure faster deployment, continuous improvement, and real-world alignment of your AI solutions, enabling your business to fully leverage the potential of large-scale AI models.
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Case Study
Scalable AI Service Launch for FinTech Startup
Customer Challenges:
A FinTech startup wanted to launch an AI-powered financial advisory service but struggled to reliably deploy and update large language models (LLMs) across multiple environments (development, staging, production). Manual deployments were error-prone and slow.
Solution Delivered:
Avashya Tech built an automated LLM deployment pipeline using CI/CD principles, integrating with Kubernetes for scalable orchestration. The pipeline included model versioning, environment segregation, validation stages, and rollback capabilities, ensuring seamless updates without downtime.
Results/Outcomes:
- Deployment time reduced from several days to under 2 hours.
- Zero downtime during model updates.
- 3x faster release of model enhancements and new features.
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Case Study
Latency Reduction for AI Customer Support Bot
Customer Challenges:
A retail company using an LLM-driven chatbot noticed unacceptable response delays during peak traffic hours, leading to poor customer satisfaction and dropped conversations.
Solution Delivered:
Avashya Tech optimized inference by:
- Converting the model to a quantized version (FP16/INT8).
- Implementing dynamic batching and caching frequently requested responses.
- Using GPU acceleration and optimizing model server configurations for concurrent requests.
Results/Outcomes:
- 60% reduction in average response time (from 3.5 seconds to 1.2 seconds).
- 40% lower infrastructure cost due to optimized resource usage.
- Customer satisfaction scores (CSAT) improved by 18% during peak hours.
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Case Study
Customized Knowledge Bot for Legal Firm
Customer Challenges:
A law firm needed a legal research assistant capable of providing highly accurate and up-to-date responses. General-purpose LLMs lacked access to the firm’s proprietary legal databases and recent case law updates.
Solution Delivered:
Avashya Tech fine-tuned a foundational LLM on the firm’s internal legal documents and integrated a
RAG system that fetched and injected the latest legal information dynamically into the LLM prompts during inference, ensuring responses were always updated and relevant.
Results/Outcomes:
- 90%+ accuracy in answering complex legal queries.
- Legal research time reduced by 55%.
- Lawyers reported a significant increase in trust and usability of the AI tool for daily research tasks.
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Case Study
Quality Assurance System for Healthcare AI Assistant
Customer Challenges:
A healthcare provider deployed an LLM-based assistant for patient queries but lacked a robust way to monitor its output for factual accuracy, patient safety, and compliance with healthcare regulations (HIPAA, etc.).
Solution Delivered:
Avashya Tech implemented a comprehensive LLM monitoring system including:
- Real-time logging of inputs/outputs.
- Accuracy and safety scoring with automated flags for sensitive cases.
- Human-in-the-loop feedback mechanism for continuous model improvement.
- Dashboards tracking drift, hallucinations, and harmful outputs.
Results/Outcomes:
- 98% safe response rate achieved within 3 months.
- Continuous improvement cycles reduced hallucination incidents by 45%.
- Regulatory compliance audits passed without major findings.