Post
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AI fails to scale because it wasn't trained on the data it is expected to work.
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
β You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
β You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
β You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
β The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemory
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
β You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
β You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
β You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
β The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemory
