LLM Integration Services
Connect state-of-the-art Large Language Models into your existing web applications, ERPs, CRMs, and backend databases. CookMyTech engineers resilient API connectors, semantic caching, token optimization, and deterministic tool-calling pipelines.
Model Routing & Caching
Cut inference latency by up to 70% and reduce token bills by 50%+. We engineer intelligent query routers that triage requests to optimal models and cache frequent semantic embeddings.
Deterministic Schema Outputs
Guarantee structured JSON, Pydantic, and Zod responses. We enforce schema validation layers so LLM outputs can be piped directly into your production databases without parse errors.
Private Cloud Deployments
Deploy open-source weights (Llama 3, Mistral) in your own AWS VPC or GCP project with vLLM and Triton inference servers for complete regulatory compliance and IP sovereignty.
// The LLM Integration Lifecycle
Architectural Audit
Review data schemas, user workflows, security constraints, latency requirements, and expected token volumes.
Connector & Middleware
Develop secure API gateways, authentication proxies, rate limiters, and prompt orchestration templates.
Guardrails & Testing
Implement evaluation suites, automated regression testing, prompt injection guards, and human-in-the-loop triggers.
Telemetry & Scaling
Set up LangSmith / OpenTelemetry monitoring for token usage tracking, response latency, and output drift.
Frequently Asked Questions
Which LLM foundation models does CookMyTech integrate?
We integrate both proprietary models (OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini 1.5 Pro) and open-source models (Meta Llama 3, Mistral, DeepSeek) deployed securely on AWS Bedrock, GCP Vertex AI, Azure AI, or private GPU clusters.
How do you control high API inference costs in production LLM systems?
We implement semantic caching (Redis / GPTCache) to avoid redundant completions, model routing (sending simple queries to smaller, faster models like Llama 3 8B and reserving GPT-4o for complex reasoning), prompt token compaction, and batch processing.
Can LLMs interact directly with our SQL databases and backend APIs?
Yes. Through structured tool/function calling and LangGraph orchestration, LLMs can generate parameterized SQL queries, call internal REST endpoints, validate schema responses, and execute workflows safely behind authorization layers.
Upgrade Your Software With LLMs
Schedule an architecture discovery session with our senior engineers today.