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LLM INTEGRATION

Claude, GPT, Gemini — working in your app.

LLM integration is more than calling an API. Production needs streaming, caching, model routing, structured output, eval pipelines, and cost controls. Dezvo wires LLMs into your product via Vercel AI Gateway or LiteLLM so swapping models is one config line.

AI Integration Overview
Providers we work with
  • Anthropic (Claude 4.x)
  • OpenAI (GPT-5, 4.1)
  • Google Gemini
  • Open-source via Together
  • Vercel AI Gateway / LiteLLM
WHAT WE BUILD

The production stack around the LLM call.

Model routing

Cheap model first, escalate on confidence or task complexity. Cuts cost by 60-80% versus always using the top-tier model.

Streaming

Vercel AI SDK streaming — tokens appear word-by-word. Time-to-first-token under 500ms. Critical for chat UX.

Caching

Prompt caching (Anthropic), semantic caching (custom), CDN-level response caching. Pay for compute once, serve thousands of times.

Evaluation

Frozen eval set scored on every deploy. Accuracy, latency, cost — tracked across model versions, not guessed.

FAQ

LLM questions, answered.

It depends on the task. Claude (Opus, Sonnet) for reasoning-heavy and long-context work. GPT (5, 4.1) for general-purpose and cost-sensitive. Gemini for multimodal and Google ecosystem. Open-source (Llama, Mistral) via Together / Replicate when data residency matters. We route through Vercel AI Gateway so swapping models is one config change.

Prompt caching, semantic caching, model routing (cheap model first, escalate on confidence), per-tenant budget caps, structured output to cut tokens. We bound worst-case spend, not just the average.

Standard. We stream via Vercel AI SDK or LangChain — tokens appear word-by-word for chat UX. Works across web, mobile, and server-sent events.

Yes — LoRA fine-tuning on open-source models, RFT on OpenAI, prompt tuning where applicable. But we always try RAG and prompt engineering first — they solve 90% of use cases without the cost and ops burden of fine-tuning.
RELATED SERVICES

The full AI stack.

AI Agent Development

Autonomous agents using tool calls and multi-step workflows.

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RAG Development

Ground the LLM in your actual data.

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AI Chatbot Development

Conversational interfaces on top of the LLM layer.

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Now integrating LLMs

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