I will integrate jev ai api, build ai agent routing, and claude workflows


About this gig
Are high LLM costs and slow latencies breaking your AI agents?
Most workflows waste budget sending basic classification and routing tasks to heavy models like Claude or GPT-4. Jev AI functions as a fast, dedicated machine-decision layerdelivering structured decisions and model routing at a fraction of standard API costs.
I will integrate the Jev AI API directly into your backend, agent, or automation stack for reliable decision pipelines.
What I Build: Intelligent Model Routing: Route tasks dynamically to fast models vs. Claude/GPT. Deterministic Guardrails: Real-time confidence scoring and tool-permission gates. Data Classification: Fast email triage, lead scoring, and ticket routing. Multi-Agent Systems: Robust tool routing via LangChain, LangGraph, or CrewAI. Claude Code & MCP: Setup decision layers inside Model Context Protocol servers.
Tech Stack: Jev AI | Python | TypeScript | FastAPI | LangChain | LangGraph | Claude | OpenAI | Gemini | n8n
Every build includes clean source code, integration guides, and testing.
Message me before ordering to discuss your workflow!
Get to know Favour M
AI Video Creator for Brands Consistent AI Avatars
- FromNigeria
- Member sinceOct 2025
Languages
English, Korean
My Portfolio
FAQ
What makes Jev AI different from conversational models like Claude or GPT?
Jev AI is built specifically for deterministic machine decisions—such as classification, scoring, routing, and guardrail validation—rather than open-ended dialogue. By handling logic at this layer, your agents run significantly faster with reduced latency and lower API costs.
Can you integrate Jev AI alongside Claude or Claude Code?
Yes. Jev serves as the decision gatekeeper. It evaluates the user's intent, chooses tools, and decides whether an expensive Claude call is necessary, allowing Claude Code or Claude 3.5 to focus purely on high-context generation.
How does Jev AI reduce my LLM token costs?
Instead of running 1,000-token system prompts through GPT-4o just to classify an inquiry or decide which tool to trigger, Jev evaluates the input at a fraction of the cost and only calls large models when strictly needed.
Can you integrate Jev AI using Python or TypeScript?
Yes. I work fluently with both Python (FastAPI, LangChain, LangGraph) and TypeScript/Node.js to embed Jev seamlessly into your microservices or existing codebase.
Can you build custom guardrails and agent verification using Jev?
Absolutely. I can configure Jev to validate user input against risk thresholds, check structured outputs for compliance, and enforce human-in-the-loop approvals when confidence scores drop below your target.
Do you support Model Context Protocol (MCP) server setup with Jev?
Yes. I can integrate Jev decision logic directly into custom MCP tools and servers, enabling developer workflows and desktop agents to query Jev decision endpoints natively.
Can Jev be used with no-code automation platforms like n8n or Make?
Yes. As long as the workflow supports HTTP webhooks or API requests, I can configure custom API nodes to send payload data to Jev AI and return structured routing decisions back into your automation flows.

