Thank you for your kind words! Your marketplace idea definitely sounds interesting to me, if not for payments, it would definitely provide a way to improve visibility through easier data exchange due to a common platform.
Hosted on premise for enterprise, VPC for mid market customers. For enterprise customers, FDE model makes most sense for a product like this and they would assist with implementation and training. For mid market customers, I'm still exploring options to make it cost effective.
For domain-specific optimization, the value is in the solver integrations, specific constraints that form the seed, and the modular simulation that powers the visuals. The software is monetized, not the services around it.
> How will you stop LLMs from recreating it?
Having worked this space for a while now I think there are two ways to ensure reliability (the real moat here), first is going deep into five-six problems that are complex enough that out of box solutions/simple prompting don't work well. Second, tightly coupling a simulator to provide rapid feedback that actually helps change manage and solve the "people" problem when optimizing operations.
Developing LogiModel AI (https://www.logimodel.com/) which is an agentic supply chain optimization engine that forecasts demand, optimizes operations, and simulates scenarios to reduce costs while keeping your network reliable and customers satisfied. It integrates seamlessly with your ERP and document repositories to learn your business context, then acts as an intelligent decision engine, freeing you to focus on strategy while it handles execution.
Always interested in possibilities of LLMs interfacing with MIP solvers.
this was a great read to start the new year! having worked extensively with mixed integer programs, it is always a bit disheartening to see them not used enough for everyday decision-making. one of my goals this year is to create a layer to make it easier to formulate mips and test them, via plain text input. this would hopefully increase adoption through a lower barrier to entry.
Great product and congratulations on the launch. Who is the target user vs customer? On the surface, and I may be wrong here, this feels like a LLM layered on top of a typical AutoML structure eg: TPOT, Caret. Is that the correct mental model for a tool like this? And if so, do you see a similar problem that these tools faced in broader adoption at companies?
I've always had the impression that Mathematical programming esp. Mixed integer programming/Integer programming is largely "unknown" outside of core engineering and operations research. It's an excellent framework to solve a whole host of problems that arise in business and elsewhere, which are solved using suboptimal (hah) heuristics instead.
Okay, maybe I was a bit harsh, but it definitely doesn't pop up as often as deep learning and statistical machine learning. For those who wish to get deeper into this, I highly recommend Optimization over Integers by Bertsimas and Weismantel.