FORGE is a private second brain that learns what it can prove. We are building a trust-aware AI system that combines smaller local models with evidence-verified knowledge, persistent memory, and reusable solved work — testing whether practical business AI can run effectively on modest, customer-controlled hardware.
Can evidence-verified external knowledge, private memory, and trusted solved-work reuse allow smaller quantized models on modest hardware to approach the practical usefulness of substantially larger AI systems for defined workloads — while reducing recurring inference compute, cost, and data exposure?
FORGE does not treat generated text as truth. Permanent knowledge is tied to source evidence, provenance, verification state, freshness, and review policy.
The local model handles reasoning. Retrieval, memory, tools, verification, and persistent knowledge externalize work that does not need to live inside model weights.
Customer code, documents, memory, and private context stay local by default. External services are reserved for approved public research or explicitly permitted stateless work.
Authoritative sources → frozen evidence → atomic claims → independent verification → deterministic quality gates → clean retrieval. Corpus size is not the objective; useful coverage is.
Exact and semantic trusted-answer caches are designed to turn repeated verified work into cheap retrieval instead of paying the inference cost again and again.
Current path: scale the clean corpus toward ~5K quality cards, benchmark against larger-model baselines, add demand-driven continuous learning, then validate with real design partners.