AI that sees, remembers and runs lean.
QWX Systems is an early-stage technology startup founded in early 2026 in Uzbekistan. We build production computer-vision and automation systems while conducting applied R&D in AI infrastructure and external memory for large language models.
- Vision in production
- RSMA in R&D
- Founded 2026, Uzbekistan
Move the cursor through the core. Click anywhere to send a pulse.
Long context is a tax you pay on every query.
Models re-read the whole document each time you ask something. It's slow, it's expensive, and anything past the window is simply gone.
The rest of the document streams past, unread.
RSMA folds the document into compact memory.
RSMA is a model-agnostic external memory architecture designed to preserve reusable context outside the Transformer, reducing repeated long-context processing across requests.
It is a working R&D proof of concept, not a production product yet. The document is streamed in once; the model recalls facts from memory instead of replaying the text.
Next: frontier-model validation with Claude
- Working RSMAImplementation built
- Local experimentsPromising initial results
- Local limitModels we can run ourselves aren't enough
- Frontier validationScale and capabilities beyond local deployment
- ClaudeThrough the Anthropic API
Frontier model validation
RSMA has already been implemented and tested locally on models we can run ourselves, producing promising initial results. The next stage is validation against current frontier models whose scale, capabilities, and weights are not available for local deployment.
Claude evaluation
Claude is one of the primary frontier model families we want to evaluate with RSMA through the Anthropic API. This will allow us to measure context reuse, token consumption, latency, retrieval quality, and memory effectiveness under realistic frontier-model workloads.
Controlled benchmark, v0
- Query latency, relativeLow vs high
- Largest document ingestedExtended vs truncated
Frozen vanilla GPT-2 host. Baseline is context-window truncation only: no RAG, no fine-tuning, no external memory. Not a state-of-the-art comparison. Validation on stronger hosts is planned.
- Controlled proof of conceptActive
- Streamed ingest and compact memory stateActive
- Controlled factual-recall benchmarksActive
- Frontier-model validation, including Claude via the Anthropic APINext
Recognition on the cameras you already have.
In production: software-based license plate recognition (ANPR), access control and automation on standard IP cameras. Every event is logged and searchable; uncertain reads go to an operator instead of being guessed.
- License plate recognition
- Software-based ANPR, real-time on standard cameras.
- Access control and automation
- Gate rules, automation, operator override, audit trail.
- Event history
- Searchable log of every recognition.
- Operator review
- Low-confidence reads go to a queue.
From camera to barrier
- CameraIP camera at the gate
- FrameCaptured as the vehicle approaches
- ServerFrames ingested and processed
- Software platformDetection and recognition using computer-vision models
- Plate readNumber checked against access rules
- BarrierOpen signal sent to the gate
Low-confidence reads go to an operator for review instead of opening the gate. Every step is logged.
Infrastructure that stays up after the demo.
Backend, cloud, DevOps and API work that keeps AI workloads running in production.
- Cloud
- Provisioning, networking and cost control across providers.
- DevOps
- CI/CD, infrastructure as code, repeatable deploys.
- APIs
- Internal and external integrations with clear contracts.
- AI in production
- Serving, batching, monitoring and the glue around models.
- Reliability
- Incident response, postmortems, capacity planning.
- Application
- Orchestration
- AI workloads: vision, RSMA
- Data
- Cloud
About QWX Systems.
QWX Systems is an early-stage technology startup founded in early 2026 in Uzbekistan. We build production computer-vision and automation systems while conducting applied R&D in AI infrastructure and external memory for large language models.
Our current work includes production software-based ANPR systems and RSMA, a model-agnostic external-memory architecture for reducing repeated long-context processing in Transformer-based AI systems.
Company facts
- Company
- QWX Systems
- Founded
- Early 2026
- Stage
- Early-stage technology startup
- Legal entity
- QWX Systems LLC
- Country
- Uzbekistan
- Founder & CTO
- Maksim Danilov
- Funding
- Bootstrapped / self-funded
- Website
- qwxsys.com
- ceo@qwxsys.com
Production
- Software-based ANPR / license plate recognition
- Computer vision systems
- Access-control and automation software
- Systems already deployed in real production environments
Research & development
- RSMA (working proof of concept)
- External associative memory for Transformer-based systems
- Long-context efficiency
- Inference efficiency
Some QWX Systems technologies are already deployed in production environments, while other initiatives, including RSMA, remain active research and development projects.
Startup ecosystem
- AWS Activate
- NVIDIA Inception
- Kiro
- Cloudflare for Startups
Infrastructure support and cloud credits are provided through startup programs. Program participation does not imply investment, endorsement, or formal partnership unless explicitly stated.
Engineering-led, hands-on.
Full-stack engineer and technical lead with hands-on experience in backend architecture, frontend systems, DevOps, automation, API integrations, computer vision workflows and production operations.
That production background is the operational discipline QWX Systems brings to applied AI.
Talk to us.
Cloud infrastructure, research, product or partnership questions. Write directly to the founder.
ceo@qwxsys.com