LLM Integration

It depends on your requirements. GPT-4o offers the best general reasoning; Claude 3.5 Sonnet excels at long-context tasks and coding; Gemini 1.5 Pro handles very large contexts (1M tokens); Cohere Command is cost-effective for classification. We benchmark on your data and often route different tasks to different models.

We implement token-bucket rate limiting with queue-based overflow, distribute load across multiple API keys, and use semantic caching so identical or near-identical requests never hit the LLM twice. For burst scenarios, we pre-warm the cache and use cheaper models as overflow capacity.

Semantic caching stores previous LLM responses indexed by their embedding. When a new query arrives that is semantically similar to a cached query, the cached response is returned instantly — saving tokens, latency, and cost. Cache hit rates of 30–60% are typical for support and FAQ use cases.

For sensitive data, we deploy open-source models (Llama 3, Mistral) on your own infrastructure so data never leaves your environment. For cloud APIs, we implement PII scrubbing before sending data to the LLM and review provider DPAs for PDPL and NCA ECC compliance.

Yes. Our standard integration is a Node.js service layer using the OpenAI SDK (works with all OpenAI-compatible APIs) with streaming, retry, and observability built in. We follow the route → controller → service pattern and integrate cleanly without rewrites.