Engineering Hub

Technical
Deep Dive

Explore the engineering principles and patterns behind our solutions. We don't just use APIs; we build the robust infrastructure required to run them in production.

LLMOps & Scale

Managing the lifecycle of LLM applications from development to production. Focusing on latency, cost-control, and observability.

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Latency Engineering

Techniques like Semantic Caching and Speculative Decoding to reduce TTFT (Time to First Token) by 60%.

Cost Optimization

Model cascading routers that send simple queries to cheaper models (Flash) and complex ones to Pro models.

Integration Patterns

Connecting AI with existing enterprise systems using standard protocols (REST, gRPC) and Event-Driven Architectures.

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Event-Driven AI

Async processing via SQS/Lambda

Streaming Response

WebSockets & SSE for real-time feel

Client

Gateway

LLM

Quality & Security

Ensuring robustness and reliability through automated evaluation pipelines (RAGAS) and Red Teaming.

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RAGAS

Automated Evaluation Metrics

PII Scan

Real-time Data Redaction

Trace

LangSmith / LangFuse Logs