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APPLIED AI INFRASTRUCTURE

QWX Systems Applied AI infrastructure for vision, memory and efficient inference.

QWX Systems is an early-stage applied AI infrastructure company building practical systems for computer vision, external memory, and inference-efficient AI workflows.

Contact Explore technical focus
Early-stage · Applied AI · Infrastructure · Research & production
01 CONTEXT

Modern AI workflows carry hidden costs.

Long-context replay, repeated inference over large input windows, and fragmented production pipelines make AI systems expensive to operate and difficult to reason about.

QWX Systems explores practical alternatives around external memory, structured recall, computer vision, and reliable infrastructure — without reinventing the underlying models.

INFERENCE COST COMPARISON
WITHOUT EXTERNAL MEMORY
Long-context replay — repeated inference over full window
WITH EXTERNAL MEMORY
Structured recall — sparse retrieval from memory sidecar
Illustrative — relative density of inference load per query.
02 TECHNICAL FOCUS

Three engineering tracks.

QWX Systems works across three connected tracks: external associative memory research, production computer vision systems, and the backend infrastructure that ties them together.

01 / RESEARCH POC

RSMA

External associative memory for Transformer-hosted AI systems. An experimental sidecar exploring factual recall decoupled from raw long-context replay.

Recursive sparse memory →
02 / PRODUCTION ACTIVE

Computer Vision Systems

Recognition, ANPR, access control, event tracking and video analytics using regular cameras. Built on production backend infrastructure with operator-assisted edge cases.

ANPR · events · access →
03 / ACTIVE OPERATIONAL

Infrastructure Engineering

Backend systems, cloud infrastructure, DevOps automation, API integrations and production operations. Reliability discipline applied to AI-adjacent workloads.

Cloud · DevOps · APIs →
03 RESEARCH
RSMA — RECURSIVE SPARSE MEMORY ARCHITECTURE

External associative memory, decoupled from raw context replay.

RSMA is an experimental external associative memory sidecar for Transformer-hosted AI systems. The current proof-of-concept explores factual recall decoupled from raw long-context replay.

  • ●Controlled PoCActive
  • ●Streamed ingestActive
  • ●Compact memory stateActive
  • ●Controlled factual recall benchmarksActive
  • ○Validation against stronger host models and baselinesPlanned
SPARSE MEMORY GRAPH FRAGMENT 03 / 12
CONTROLLED POC BENCHMARK: RSMA SIDECAR VS VANILLA GPT-2 CONTEXT-WINDOW BASELINE
Controlled factual-recall benchmark. Baseline v0 is vanilla GPT-2 context-window truncation only, without RAG, fine-tuning, external memory, or optimized long-context architecture. This is not a SOTA comparison and not a general open-domain QA benchmark. Stronger-host validation is planned.
Fact Recall Accuracy
1.00
0.00
RSMA PoC Baseline
Measured Peak VRAM
RSMA PoC ~700MB
Baseline ~1023MB+
Query Latency (Relative)
RSMA PoC Low
Baseline High
Max Ingested Doc Size
RSMA PoC Extended
Baseline Truncated
CONTROLLED LONG-DOCUMENT FACT RECALL BENCHMARK (NOT A SOTA COMPARISON).
TEST CONDITIONS: FROZEN VANILLA GPT-2 HOST, NO RAG, NO FINE-TUNING, NO EXTERNAL MEMORY BASELINE. V0 POC.
04 PRODUCTION SYSTEMS

Practical computer vision, built for operations.

QWX Systems builds computer vision systems for recognition, event tracking, access control, and video analytics using regular cameras and production backend infrastructure.

CAMERA 03 · FRONT GATE LIVE FEED
14:31:48 · 30FPS · 1080P PROCESSING REALTIME
Recognition, tracking and access control.
01
ANPR / license plate recognition
Real-time plate detection on standard cameras.
02
Event history
Queryable logs of recognition and access events.
03
Access control workflows
Plate-based gating, operator override, audit trail.
04
Real-time processing
Low-latency pipeline for live camera feeds.
05
Operator-assisted edge cases
Manual review queue for low-confidence events.
06
Backend dashboards
Operations console for monitoring and search.
RECENT EVENTSCAMERA 03
14:32:07Plate match — KX-4821-EA
14:31:52Access granted — door 02, automatic
14:31:48Tracking — object 4F2A, 1.2 m/s
14:31:31Edge case — review queued, confidence 0.61
14:31:15Plate match — MT-9034-LO
05 ENGINEERING

Infrastructure that operates.

QWX Systems works across backend systems, API integrations, cloud infrastructure, automation, monitoring, deployment workflows, and production operations.

Cloud infrastructureProvisioning, networking, cost discipline, multi-provider exploration.
Active
DevOps automationCI/CD, infrastructure-as-code, repeatable deployment workflows.
Active
API integrationsInternal and external APIs, contract discipline, observability.
Active
AI-adjacent production systemsServing, batching, monitoring, and operational glue around AI workloads.
Active
Reliability and operational disciplineMonitoring, incident response, postmortems, capacity planning.
Core
STACK VIEW — SIMPLIFIEDV0.1
ApplicationAPI · dashboards
OrchestrationWorkflows · jobs
AI workloadsVision · RSMA
DataEvents · memory
Cloud infrastructureCompute · network
ObservabilityLogs · metrics · alerts
06 CURRENT STATUS

Where QWX Systems stands today.

An honest view of the company's current stage, technical validation, and engineering background — without inflated claims.

STAGE
Early-stage
QWX Systems is an early-stage company with active technical development.
VALIDATION
Controlled PoC
Technical validation in progress under controlled conditions and benchmarks.
INFRASTRUCTURE
Active exploration
Cloud and infrastructure programs under active exploration across providers.
ENGINEERING
Production background
Hands-on production engineering experience across backend, DevOps and operations.
NOTE Public technical materials can be expanded over time. The current stage is early, the technical track is active, and validation is ongoing. No claims of production-ready products, customer traction, or formal partnerships are implied.
07 ECOSYSTEM

Infrastructure programs under exploration.

QWX Systems is actively exploring cloud and infrastructure programs across multiple AI infrastructure providers. The list below reflects exploration, not formal partnerships.

AWSKiroOVHcloudNVIDIAAlibaba Cloud+ other providers
DISCLAIMER Programs listed above are under active exploration. QWX Systems does not claim official partnership, sponsorship, or endorsement unless explicitly stated in writing. Provider names are referenced for technical context only.
08 TEAM
Maksim Danilov
Founder & CTO, QWX Systems
RoleFounder · CTO
FocusInfrastructure · Vision · Memory
BackgroundProduction engineering
StatusActive

Engineering-led, hands-on.

Maksim is a fullstack/software engineer and technical lead with hands-on experience in backend architecture, frontend systems, DevOps, automation, API integrations, computer vision workflows, and production system operations.

His background spans production engineering across cloud infrastructure, API integrations, and AI-adjacent systems — the operational discipline that QWX Systems brings to applied AI infrastructure.

Backend architecture Frontend systems DevOps Automation API integrations Computer vision Production operations
09 CONTACT

For cloud infrastructure, research, product or partnership discussions, contact QWX Systems.

[email protected]
PARTNERSHIPSCloud & infrastructure programs
RESEARCHRSMA · memory systems · validation
PRODUCTVision systems · access control · ANPR
RESPONSEWithin 48 hours, Monday–Friday
Direct contact
EMAIL[email protected]
RESPONSE TIMEWithin 48 hours, Monday–Friday
SCOPEPartnerships, research, product, infrastructure discussions
COMPANYQWX Systems — early-stage, applied AI infrastructure