Service · 06
Custom AI Integration — MCP + LLM
Drop AI into your existing product — MCP-powered, with the model that fits your use case.
How it's built
(1) Model fit — benchmark 2–3 models on your task for price/latency. (2) Bridge design — MCP server into your stack with a permissions model. (3) Integration — feature build inside your product with streaming UX. (4) Safety & cost — guardrails, budgets, fallback chains. (5) Launch — usage analytics and prompt iteration.
Core fundamentals
- model chosen by benchmark, not hype
- fallback model wired for outages
- per-user budgets
- AI features measurable from day one
Build blueprint
Deliverables
- integrated feature
- MCP bridge
- benchmark report
- analytics events
Stack
ClaudeOpenAIGeminiMCPAPIs
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