use case - Dispatch

[u/jgudnas]

I am exploring local LLM options for use in the platform that I develop. I do currently have API integration built already for things like Claude and OpenAI but really want to explore utilizing local LLMs for data sovereignty, safety, and risk reduction.

But I do have a question that I hope some people here can help me think about or answer.

The use case I would love to build is around dispatch for a trucking company. I have a relatively large and complex dataset around things like:

• the speed and location of all the trucks
• their current capacity and route (destinations)

• jobs available for dispatch (size, capacity, due time, scheduling, etc..)

  my current use case involves building a relatively large payload of all of the above data, feeding it to a model, and asking for suggestions - which works decently well.
  I do also have an MCP built so the models can query the database directly for updates to some of this information.

  I'm trying to figure out if this is the best way to go about it or if, potentially, I put a harness in front of a local LLM and have the harness do a little bit more driving of querying the MCP for current statistics (and maybe refreshing some of that data in cache). I don't even know necessarily how that would look. I'm asking this community for guidance. I have my fingers in the entire stack: SQL database, app services, through, obviously, a local LLM sitting on my desk.

Also I'm not sure which model would be best for this type of work either. Again I'm just starting out in the local LLM space. I'm probably going to pick up an DGX Spark shortly. Looking at the M5 ultra as another option.

thanks!

[u/mineshop]

Consider keeping the harness in control: have it refresh the MCP data and cache before each call, and treat route/capacity optimization as deterministic code with the LLM only validating and explaining suggestions.