We are looking for a partner
We are not looking for investment but a viable partner to integrate our ARCEngine into the current Al environment.
We have built a product demonstration that will allow a human to view the humanised version of the engine and have a simplified set of actions within the ARCEngine, as it was built for AI interaction only.
Over the past two years of development and implementation of both a POC and a POP, we now have a finished product., working under an AI controller Matrix.
In essence, we are letting Al train itself, but under a rule defined control matrix.
As the text data is part of the contextual category, all of the dataset is relevant to the implied topic. ARCTags add specialised efficiency for training focused data preparations.
What we are creating are TSLLMs: Topic Specific Large Language Models.
ARCTags could also feature the concept of parallel tokenisation using multiple entangled/correlated datasets for greater topic specific relevancy.
Cost efficiency
As token count impacts the cost of running Al models, (as more tokens require more computational resources), ARCTags will provide concise, topic-specific datasets, so the number of tokens processed is optimised-fewer irrelevant tokens mean lower processing costs without sacrificing quality.
Our datasets are configured to also provide highly relevant topics for the design of Application Specific Integrated Circuits (ASIC’s).
An ARCTag turns “unstructured data-set” specifications into a laser-focused blueprint. You will know what to build, how big to make it, and how to verify it – yielding an ASIC that’s smaller, faster, and more power-efficient than any general-purpose chip could be.
See how we influence the environment
Greener AI Tokenisation & Creation of ASICs
ARCNet is an environmentally conscious technology company using AI to decrease both resource requirements (WATER) and costs for AI tokenisation and the design as well as development of Application Specific Integrated Circuits (ASIC).
Water
By 2027 global AI water requirements could account for the use of 4.2 to 6.6 billion cubic metres of water annually (up to 1.25 trillion imperial galleons)
Power
Estimates predict that in 2026, AI operations will consume over 40% of a data centre power requirements, estimated to be at 4% of the worlds total production.
Data
Data centre capacity is projected to rise at an average rate of 33% annually between 2023 and 2030, with AI workloads expected to constitute around 70% of total data centre demand by 2030.