RAG was built for the cloud.Local AI needs local memory.
200MB footprint. 100% local. Built for edge AI.
Hardware is ready for local AI.But it needs memory and context.
Running open-weights models locally is the new standard for privacy-bound enterprises. Clace is a drop-in AI memory layer designed for edge compute, ensuring AI stays contextual in constrained environments.
| Feature | Traditional Vector DB | Clace |
|---|---|---|
| RAM Footprint | Scales with Data | ~200MB Constant |
| Privacy | Cloud-Dependent | 100% Local |
| Latency | 300-500ms | < 200ms |
Drop-in Infrastructure. Zero Ops.
python
from clace_sdk import Clace # 1. Initialize the Zero-Copy Engine (Footprint: ~200MB)fetcher = Clace(index_path="local_data/clace", bicameral_mode=True) # 2. Ingest Rulesets (strict rules) and Episodic Memory (user history)fetcher.ingest_ruleset(document="HIPAA_Compliance_Guidelines.pdf", title="Strict Rules")fetcher.ingest(data_path="user_local_chat_logs/") # 3. Retrieve context instantly without RAM bloatcontext = fetcher.get_context_bicameral(user_input="Summarize patient history", top_k=5) print(context)Purpose-Built for Privacy and Performance
Compliance-First RAG
Deploy retrieval pipelines entirely on-device to maintain strict HIPAA, SOC2, and attorney-client privilege. Your data never leaves the machine.
Zero-Infrastructure Retrieval
No external vector database to provision, no embedding API to call. The SDK handles storage, indexing, and retrieval in a single local binary.
Lightweight by Design
Constant 200MB RAM regardless of index size. Embed semantic retrieval into any application without worrying about resource overhead.