Concept buildSelf-initiated, not client work. The figures below are design targets we set ourselves, not results delivered for a customer.
APEXANALYTICS
A self-initiated concept build by Windson — AI Data Telemetry Platform, on React 19, Python, LLMs, built to test the approach rather than for a client.
The brief we set ourselves: an operations team drowning in telemetry from distributed cloud nodes, where rule-based alerts are either too noisy to read or quiet enough to miss a silent failure, and raw data costs engineers hours before an incident is even understood. We wanted to see what an AI-native dashboard would actually look like.
We designed an AI-native telemetry dashboard on React 19 Server Components and streaming hooks, fed by a Python pipeline over WebSockets. An orchestration agent analyses abnormal spikes with fine-tuned LLMs and writes a readable incident summary as the data arrives.
A reference build for machine-readable operations — the incident explains itself before anyone opens a log.
SYSTEM DELIVERABLES
What we actually built, and why each piece had to exist. Nothing here is a mockup — the concept was taken far enough to prove the approach works.
React 19 Telemetry Dashboard
Real-time streaming charts over WebSockets and React 19 server actions, with view filters that update without a round trip.
AI-Agent Root Cause Pipeline
An orchestration pipeline using LLMs to isolate infrastructure anomalies and draft repair steps.
High-Performance Data Ingest
A Python/FastAPI ingestion service designed for thousands of sensor pulses per second into a PostgreSQL store.
