Case Studies
Real results from successful AI implementations
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RAG Copilot Implementation for Policy & Compliance Research
Client: Top-10 U.S. enterprise (risk & compliance division)
The client needed to streamline policy interpretation, evidence gathering, and internal knowledge lookup across multiple governance frameworks. Manual processes were time-consuming and error-prone, with reviewers spending significant time searching, cross-referencing, and documenting decisions.
Agent Automation for Media Content Operations
Client: Global Media Network
Content review and approval workflows were manual and slow, creating bottlenecks in content production pipelines. Content teams spent 40% of their time on review and approval tasks.
LLMOps Foundation for Enterprise AI Platform
Client: Fortune 500 enterprise
Multiple AI applications lacked centralized monitoring, evaluation, and governance, making it difficult to ensure reliability and compliance. Incidents were frequent and deployments were slow due to manual processes.
Production-Grade LLMOps Infrastructure
Client: Enterprise AI Platform (Ishtar AI Case Study)
Building production-ready LLM infrastructure requires careful hardware selection, cost optimization, and scalable deployment patterns. Traditional infrastructure approaches don't account for LLM-specific requirements like GPU partitioning, token economics, and hybrid cloud deployments.
Continuous Evaluation & CI/CD for LLM Systems
Client: Enterprise AI Platform (Ishtar AI Case Study)
LLM systems require continuous evaluation to catch regressions and hallucinations, but traditional CI/CD pipelines don't account for non-deterministic model behavior. Deployments were risky without proper evaluation gates, leading to production incidents.
Advanced Observability & Monitoring for LLM Applications
Client: Enterprise AI Platform (Ishtar AI Case Study)
LLM applications require specialized monitoring beyond traditional application metrics. RAG systems need retrieval-specific metrics, prompt flows require detailed tracing, and quality degradation must be detected before user impact. Existing monitoring solutions lacked LLM-specific capabilities.
Scaling LLM Production Systems
Client: Enterprise AI Platform (Ishtar AI Case Study)
Scaling LLM deployments requires sophisticated techniques beyond simple horizontal scaling. GPU partitioning, distributed inference, batching optimization, and capacity planning are critical for cost-effective scaling while maintaining latency SLOs. Traditional scaling approaches don't account for LLM-specific constraints.
End-to-End LLMOps Implementation
Client: Enterprise AI Platform (Ishtar AI Case Study)
Building a complete, production-ready LLMOps platform requires integrating infrastructure, CI/CD, monitoring, scaling, and governance into a cohesive system. Each component must work together seamlessly to deliver reliable, auditable, and scalable LLM applications.