---
source_url: "https://www.clace.ai/?utm_source=openai"
title: "Clace — Vector Search SDK for Edge AI, IoT & Embedded"
mirrored_at: 2026-08-07T03:38:50.454Z
host: www.clace.ai
cited_in_42a: true
mirror_canonical: "https://index.42a.ai/www.clace.ai/index__q__utm_source_openai"
---

> **Original source:** https://www.clace.ai/?utm_source=openai

Memory 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 footprintclace = Clace(index_path="./local_index", bicameral_mode=True)# 2. Ingest compliance rules and user/episodic memoryclace.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 egresscontext = clace.get_context(query="Summarize patient history", top_k=5)# 4. Pass to your local LLM of choiceresponse = 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](https://www.clace.ai/docs/installation)