KFaaS is a knowledge fabric backend engineered for serious AI deployments.
It ingests documents, code, logs and datasets, compresses them through a GPU-first pipeline, and exposes a structured
knowledge interface for any model you choose to run—locally or in the cloud.
Designed for organizations that require precision, privacy, and repeatable intelligence.
Most AI systems operate on unstructured text and improvised RAG pipelines. They search blindly, hallucinate under pressure,
and collapse when scaled or replicated.
Enterprises cannot rely on systems that vary from region to region and cannot be reproduced with confidence.
KFaaS transforms raw, unstructured data into a replicable knowledge fabric ready to serve thousands of downstream systems. The same architecture runs as a cloud fabric or as a dedicated appliance inside your own datacenter.
The Ingestion Gateway and Parsing Engine normalize files, URLs, repositories and datasets into clean semantic chunks. Every dataset is versioned, allowing the exact same knowledge state to be reproduced on additional machines or regions.
GPU-based embedding and long-context compression generate multi-level summaries, entity graphs and symbol maps. KFaaS does not store “chunks”—it builds a structured fabric optimized for reasoning, precision and efficiency.
The Retrieval Orchestrator serves context bundles to any LLM. The fabric layout is identical across hosted, hybrid and on-prem deployments, making replication and scale-out trivial.
KFaaS is designed as installable infrastructure—not a hosted-only product. A single machine can power knowledge for thousands of applications, and additional servers can be cloned and synchronized as your footprint grows.
A minimal interface for developers and enterprises alike: ingest a dataset, let the fabric compile it, and request structured knowledge. The workflow is identical whether KFaaS runs in your racks or as a managed fabric.
AI systems evolve, but their understanding of knowledge should remain stable. KFaaS creates a persistent, versioned foundation that models can rely on—regardless of the LLM provider, deployment environment or scale.
• Deterministic Knowledge States: the same dataset version always yields the same fabric. No drift.
• Isolated and Secure: hosted tenants are isolated; on-prem deployments give full confidentiality and audit control.
• LLM-Neutral: KFaaS is not a chatbot. It is a knowledge layer that any model can consume—OpenAI, Qwen, DeepSeek, Llama, or your own in-house models.
KFaaS is engineered to be installed, replicated and integrated into real enterprise environments. A well-designed appliance can serve as the knowledge backbone for thousands of internal tools, agents and applications.
Developed by undefine.cc, KFaaS is a complete stack: ingestion gateway, fabric compiler, index engines and reasoning interface. The architecture is intentionally designed for replication across datacenters and regions.
We are not seeking compute donations. We are selecting distribution and infrastructure partners who want to deploy KFaaS appliances or offer the managed fabric to their enterprise clients.
Current Offerings
• Managed knowledge fabric via API
• Dedicated on-prem KFaaS appliances
• Joint deployments with datacenters and integrators
We are onboarding:
• Enterprises requiring a private, deterministic knowledge backend.
• AI product teams integrating copilots, agents or knowledge-heavy systems.
• Infrastructure partners deploying KFaaS appliances or managed fabrics in new regions.
If you operate racks, design AI tools, or deliver enterprise infrastructure, KFaaS becomes the backbone your models cannot provide by themselves.
✈️ Open Telegram · KFaaS Team