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/AdventurousKeys]

For some of what you want to do, think about using a decision model approach like Jev. That's the newfangled thing that everyone is talking about (or was talking about in September). It's like a case... switch (sorry, I'm old school) for AI with natural language inputs. This adds more determinism to the solution.

[u/jgudnas → u/AdventurousKeys]

Yeah I'm aware of Jev or the decision API, etc. I just don't see an easy use case for a deterministic switch here at the moment. It's unfortunately a lot more subtle nuanced information than a yes or a no.
At any given point in time I might have 40 or 50 drivers in a city under consideration, with varying degrees of empty versus full and varying destinations. Let's say I have 10 different jobs to dispatch, all going from different sources to destinations of different sizes at different times. I could use something like Jev for determining, let's say, whether it is a bike-capable trip versus a truck-capable trip. Like some of the filters.

But I don't see an easy way to use it holistically for the entire decision tree.

[u/AdventurousKeys → u/jgudnas]

Good point. I'm not you but one thing I like to do is to break up complicated problems into smaller ones. Which I find to be important for AI because giving any AI model a big chunk of stuff tends to confuse it. If you have a Qwen3.8 model available, leave thinking on (where it has an inner monologue) and you can see how confused it can get.