Memory for Edge AI

On-Device Vector Search for Edge AI

A drop-in retrieval SDK for local LLMs — no cloud database, no embedding API, no network round-trip.

BUILT FOR ROBOTICS, IoT & EDGE AI

Cloud vector databases weren't built for robots, IoT gateways, or embedded devices.
Clace was.

Efficient memory footprint

Cloud vector databases use more RAM as your data grows. Clace stays flat at ~200MB — whether you store 10K vectors or 10M. It runs inside your app, with no separate database to provision or manage.

Total data privacy

Cloud embedding services log every query and every document you send them. Clace runs the embedding model on the same device as the index. No telemetry, no third parties, nothing crosses the network.

Sub-200ms local retrieval

Cloud retrieval burns 300–500ms on network calls before your model sees the context. Clace returns results in under 200ms, fully local — so the answer reaches your user that much sooner.

Compliance-ready by default

HIPAA, SOC 2, attorney-client privilege, ITAR — every framework hates sending data off-device. Clace runs air-gapped by default: no cloud calls, no telemetry, no internet required. Legal signs off in days, not quarters.

Feature	Cloud Vector DB	Clace
RAM Footprint	Scales with data	~200MB constant
Network Calls	Embedding API + DB queries	Zero
Query Latency	300–500ms	<200ms
Infrastructure	Vector DB + embedding service	Single SDK binary
Deployment	Cloud-dependent	Air-gap ready

A few lines of code. Zero infrastructure.

python

from clace_sdk import Clace
 
# 1. Initialize the engine — constant ~200MB footprint
clace = Clace(index_path="./local_index", bicameral_mode=True)
 
# 2. Ingest compliance rules and user/episodic memory
clace.ingest_ruleset(document="HIPAA_Guidelines.pdf", title="Strict Rules")
clace.ingest(data_path="./user_chat_logs/")
 
# 3. Retrieve context locally — no API calls, no data egress
context = clace.get_context(query="Summarize patient history", top_k=5)
 
# 4. Pass to your local LLM of choice
response = local_llm.complete(prompt=context + user_question)

Built for the places where your AI actually runs

Local Copilots

Ship AI features as a single offline binary.

Code assistantsDesktop chatNote appsKiosks

Robotics

A memory layer that fits beside perception and planning models.

DronesMobile robotsAVsIndustrial arms

Voice & Wearables

Sub-200ms semantic recall, no cloud round-trip required.

Smart speakersHearing aidsAR glassesVoice agents

Retail & POS

Customer-facing AI where data has to stay inside the store.

In-store assistantsSignagePOSSelf-checkout

Industrial IoT

Run semantic search where bandwidth is slow, costly, or absent.

Factory floorsOilfieldsField sensorsVehicles

Regulated AI

Build retrieval pipelines where data egress isn't an option.

HIPAASOC 2Attorney–clientAir-gapped

Ship local AI that actually remembers.

Start with the quickstart guide. No infrastructure, no cloud, no data egress — just a few lines of code.

Read the Quickstart