LLM INTEGRATION

Production-grade LLM
intelligence in your product.

Not just an API wrapper — a battle-tested integration with streaming, structured outputs, cost controls, evals, and the reliability production demands.

LLM INTEGRATION SCOPE

Everything after "call the API".

Streaming & Real-time UX

Token streaming with proper client-side buffering, partial rendering, and error recovery. The AI feels fast, not frozen.

Structured Outputs

JSON mode, function calling, and Pydantic validation to guarantee your LLM returns structured data you can use programmatically.

Context Management

Conversation memory, context window optimization, summarization for long conversations, and token counting to avoid surprises.

Cost Optimization

Model routing, semantic caching, prompt compression, batch processing. We reduce LLM costs without sacrificing quality.

Eval & Monitoring

Automated test suites that catch regression when you update prompts or switch models. Latency, quality, and cost dashboards.

Model Abstraction

Provider-agnostic architecture that lets you switch models with a config change. Avoid vendor lock-in from day one.

PRODUCTION CHECKLIST

What we ship vs. what most teams skip.

LLM INTEGRATION STANDARDALL ITEMS REQUIRED

streaming_support✓ implemented// token streaming with error recovery

structured_output_validation✓ implemented// JSON schema enforcement + Pydantic

prompt_versioning✓ implemented// all prompts version-controlled + tested

eval_suite✓ implemented// automated regression tests for quality

cost_monitoring✓ implemented// token tracking + spend alerts

model_fallback✓ implemented// backup provider on 503/rate limit

context_truncation✓ implemented// graceful handling at context limit

semantic_cache✓ implemented// cache repeated queries to save cost

latency_monitoring✓ implemented// p50/p95/p99 tracked in production

hallucination_controls✓ implemented// grounding, citations, or structured constraints

SUPPORTED MODELS

Model-agnostic. Benchmarked honestly.

OpenAI

GPT-4oGPT-4o minio1o3-mini

Best for: complex reasoning, function calling, JSON mode

Anthropic

Claude 3.5 SonnetClaude 3.5 HaikuClaude 3 Opus

Best for: long-context, code, careful instructions

Google

Gemini 1.5 ProGemini 1.5 FlashGemini 2.0

Best for: multimodal, large context windows

Open-source

LLaMA 3.3 70BMistral LargeQwen 2.5 72B

Best for: data privacy, cost at scale, fine-tuning

Cohere

Command R+Command R

Best for: RAG, enterprise search, document tasks

Custom fine-tuned

Your domain modelLoRA adaptersMerged models

Best for: specialized tasks, cost optimization

FAQ

LLM integration questions.

All major providers — OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini), Cohere, Mistral, and open-source models (LLaMA, Qwen, Phi). We pick the best model for each use case based on benchmarks, cost, latency, and privacy requirements.

Through prompt compression, intelligent caching (semantic similarity cache), model routing (cheap models for simple tasks, powerful for complex), batching, and usage monitoring with alerting. Cost optimization is part of every LLM engagement.

Through structured output enforcement (JSON mode, function calling), multi-step validation pipelines, output post-processing, automated evaluation against test sets, and human review sampling. We establish a quality baseline before you go live.

Yes. We support private deployment (on-prem Ollama, private cloud), fine-tuning on your data, and RAG architectures that keep your data in your infrastructure. No data needs to leave your control.

ADD AI TO YOUR PRODUCT

LLM integration done right.

Tell us what you're building. We'll design the integration and give you a fixed timeline.