Technology
The most advanced planner on Earth
HyperC combines the latest advances in AI-enabled planning with the most powerful compute to make the easiest-to-use solver yet. By deeply analyzing the supplied business rules and combining them with learned knowledge HyperC core offloads most of the math heavy-lifting to the CPU while releasing the brainpower to focus on the task.
Describe the task and let the machine solve
Describing the problem in HyperC is done with business rules that create a digital copy of some process or sub-process that may take place in your operations. For example, the doctor may get assigned to a calendar date; the truck may be ordered to some location. Then the AI takes all possible business activities that may take place and finds an optimal sequence of these activities to satisfy all requirements - revenue, regulatory, account balances, etc.
Built for business people, not data scientists
Unlike sophisticated AI tools, HyperC does not require a Ph.D. and expert software development skills. It allows business users to define the task through no-code interactions. The ease and flexibility of HyperC allow users to tackle real business problems like budgeting resources, optimizing the use of limited assets, planning operations. Simply input process data, business rules, and constraints “as-is” in existing formats, and HyperC will process them to find the best solution.
Industry Leaders Trust HyperC
I’ve been in the space of software development and no code approaches for a long time now and have never seen anything like this! Amazing! This is by far the most superior low code technology I have ever seen.
This could be a must have feature forevery Kubernetes installation.
Fascinating convo today with @Andrew_Gree, co-founder and #CTO of @theHyperC, which uses radically advanced #AI that can figure out how to use code to solve problems, little to no coding required.
This is bigger than Turing Machine because we're solving NP-complete problems in a multiverse, searching through multiple turing machines”, “this is super-meta programming”, “Current academic thinking is that a big NP-hard problem has parts that are hard and that are easy. The problem is to find the parts that are hard, and brute force them with a computer”, “There is a proof that logistics problems are polynomial-hard and can be efficiently solved
I am impressed. This looks like a 20-year story but the approach is working already today
NASA is spending tens of millions of dollars on formal methods and what you did is super impressive”. “Security experts, Aerospace real-time algorithms developers urgently need to get proof that the systems are correct, and this technology makes it so much easier to do
This is a very intriguing idea!”, “We’ve been using SAT solvers to synthesize programs but never thought about applying AI planning to the problem
I’ve been thinking about something like this for a while now. This is very exciting.
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