Edge-native runtime
Designed for local and offline execution, not cloud lock-in.
Edge-Native Deep-Tech Startup
AI systems engineered for offline intelligence and real-time decisions.
We develop edge-native AI solutions focused on computer vision and real-time decision systems, designed to work offline in low-resource environments.
At QUVANet, we build practical, deployment-ready models that keep performance high while reducing cloud dependency and infrastructure overhead.
- Computer vision and real-time decision systems
- Offline-first inference in low-resource settings
- Edge deployment with privacy-first architecture
Vision + decision loop
Computer vision pipelines connected to real-time decision logic.
Low-resource optimized
Built to perform in constrained compute and bandwidth conditions.
About QUVANet
We are a deep-tech startup focused on real-world AI execution.
We develop edge-native AI solutions focused on computer vision and real-time decision systems, designed to work offline in low-resource environments.
Deep Learning Engineering
Architecture-first model design with performance and efficiency as co-equal goals.
Computer Vision Systems
Vision pipelines optimized for real-time behavior and resource-constrained hardware.
Cloud-Independent AI
Offline-capable deployment patterns for privacy, data sovereignty, and predictable cost.
Developed Models
Model lineup (coming soon)
Inspired by model-card style launches, each model highlights achievements and practical ability.
Coming Soon
DSPA Model
Dynamic Sparse Pattern Attention for stronger context-aware sequence modeling.
- Achievement: 12% relative validation-loss reduction vs strong baselines.
- Ability: Learns dynamic top-k sparse attention patterns from input content.
- Benchmarks: Wikitext-103 and TinyShakespeare character-level modeling.
Coming Soon
EcoConvNet Model
Transformer-like vision architecture using efficient temporal 1D convolutions.
- Achievement: Shifts sequence complexity from quadratic to linear.
- Ability: Delivers strong accuracy-per-FLOP for Green AI computer vision.
- Use case: Resource-constrained edge and low-power deployment settings.
80 MB Lightweight Model
QNano
A compact local model designed for speed, low memory footprint, and private execution.
- Achievement: Ultra-light 80 MB package for easier on-device deployment.
- Ability: Local AI generation pipeline [coming soon].
- Direction: Practical AI performance without mandatory cloud runtime.
Research
Currently visible research paper: DSPA
We are showcasing one research paper right now while additional work moves toward release.
Dynamic Sparse Pattern Attention (DSPA)
DSPA introduces a dynamic, content-aware sparse attention mechanism that focuses compute on the most relevant token interactions instead of using fixed input-agnostic patterns.
- Reported result: 12% relative validation-loss reduction over strong attention baselines.
- Key benefit: Higher sample efficiency with competitive time-to-accuracy.
- Research area: Efficient Transformer architectures for high-quality language modeling.
Library
DeepCaptcha
Our currently highlighted public library focused on robust, human-readable CAPTCHA generation.
DeepCaptcha
A Python library for generating CAPTCHAs with configurable AI resistance, optimized for practical integration and low-latency use.
- Install:
pip install deepcaptcha - Capability: Adjustable distortion/noise control with readable outputs.
- Status: Active public library in the QUVANet ecosystem.
Flagship Project
SpeakUp
AI-powered communication mastery with an offline-first architecture.
Partner Deployment
SpeakUp x BringUpEdu
SpeakUp uses a tri-modal assessment stack (audio, video, and NLP) to help learners improve interview communication with structured, repeatable feedback loops.
Current tie-up: BringUpEdu
Offline-first processing for institutional data privacy
Audio, visual, and content-level evaluation in one workflow
Unlimited practice loop to build confidence and consistency
Built for real educational deployment and outcomes
Contact
Build efficient AI systems with us.
For model collaborations, product pilots, and startup partnerships: