Use Cases
Edge & On-device semantics.
Run semantic workloads on constrained devices using compact symbolic representations.
Compact semantic representations
Low memory and bandwidth footprint
Offline and intermittent operation
Deterministic local matching
The Problem
Why embeddings don’t scale to the edge
Most semantic systems assume cloud-scale resources and continuous embeddings.
At the edge, memory, bandwidth, power, and latency constraints make high-dimensional floating-point vectors impractical to store, transmit, and compare.
As a result, semantic intelligence remains centralized.
The Approach
Symbolic semantics for constrained environments
SEMQ maps embeddings into compact symbolic codes that preserve semantic structure.
These representations are small enough to store locally, transmit efficiently, and compare deterministically on constrained devices.
This enables semantic matching and memory without relying on cloud infrastructure.
Capture
On-device — no cloud roundtrip
Capabilities
Compact Semantic Footprint
Symbolic representations drastically reduce storage requirements.
Offline Operation
Semantic matching works without continuous connectivity.
Low Power & Latency
Local symbolic operations avoid network roundtrips.
Edge-to-Cloud Interoperability
Symbolic codes sync cleanly with centralized systems.
Developer Flow
How it works on-device
input → embed → semq.encode → store(code)
query → semq.encode → local_match(code)
optional: sync symbolic state
Evaluation
Evaluation and experiments
Storage footprint, matching behavior, and synchronization trade-offs are evaluated under constrained resource scenarios.
View benchmarks →“Semantic intelligence should not depend on constant connectivity.”