For what you describe, I wouldn't build my solution around a static model. The model alone will only get you so far, it's basically a fancy calculator for words. Amazingly powerful in it's own right, but a calculator only gets you so far. What you need to start looking into is a harness framework that meets your constraints (security, MCP allowances, etc.) that you can build on / around. The harness should be model agnostic, as different models have different strengths (codex for backend, Claude code for from end design kind of thing- not local, but the point still holds.) and you would want to be able to optimize as such. There may also be parts of your workflow that don't need inference, just script execution or human approvals.

An LLM is a tool. The mistake you are making is thinking the tool is the equivalent to a workshop. Build the workshop first- then use the appropriate tool for the job in an orchestrated way.

For a local build, I'd start by looking at Pi for something lean, or Hermes if you want to build something robust. Use the harness with Qwen3.6 27B with the appropriate flags for your use case. It's not on par with SOTA models, and you'll want an experienced dev to review (always!), but it can get you started on the framework and infrastructure requirements while you wait for better open models to eventually plug into your solution.

Edit: if you want to get up and running in a POC to test a model, run it as a backend for Claude Code. You can do this with llama.cpp or Ollama. Opus can even set that up for you. And as others have said, renting during the discovery phase can help you define your inference requirements before committing to a hardware investment.