Service

LLM Integration

We integrate LLMs into existing and new products with intelligent model routing, conversation memory, streaming responses, and the backend infrastructure to support them at scale.

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The Business Problem

Adding an LLM to a product requires more than an API call — it needs memory management, context handling, cost optimization, and fallback strategies.

Our Solution

Structured LLM integration with ChatCompletionService patterns, multi-model routing, caching, and production monitoring.

Our Process

1Integration architecture design
2Model routing and prompt system development
3Memory and session management implementation
4Performance optimization and cost management

Typical Use Cases

In-app AI assistants
Code generation tools
Content creation platforms
Domain-specific copilots

Key Benefits

Reliable multi-model AI routing
Session continuity and context management
Token streaming for responsive UX
Cost-optimized model selection

Technologies

Semantic Kernel

OpenAI API

Azure OpenAI

Google Gemini

Redis

ASP.NET Core

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