Client engagements stay confidential. These systems are from our own product portfolio, built, launched, and operated end to end by Ashton Group, and they demonstrate exactly the engineering we bring to client work.
Build a consumer product on top of some of the most demanding public data in existence: 10,000+ USGS stream gauges, 800+ snowpack telemetry stations, and NOAA weather models, all updating continuously, and make it fast, reliable, and discoverable without a marketing budget.
We engineered real-time geospatial ingestion pipelines with daily automated map tileset publishing and fallbacks so a late upstream feed never shows users a gap. Decade-deep station histories were joined to live readings to compute percent-of-normal analytics competitors don't have. Growth came entirely from SEO architecture: thousands of location pages built from the data itself, performance-tuned to rank.
The platform has operated continuously since 2019 and serves 100,000+ monthly active users, with growth driven entirely by organic search: zero paid marketing over the platform's life.
Add AI features across a fleet of products without inheriting the standard failure modes: provider outages becoming product outages, and API bills that surprise everyone at month-end.
We built a centralized gateway routing all LLM traffic: intelligent failover across Anthropic and OpenAI, caching, retries, rate limiting, and per-feature, per-session cost attribution with hard caps. On top of it, an orchestration framework (LangGraph and FastAPI) runs multi-agent workflows against more than twenty internal tools with persistent memory and concurrency-safe execution.
Provider incidents degrade gracefully instead of taking features down, every AI feature has a known cost per use, and new AI capabilities ship against the gateway in days rather than weeks.
Deliver camera-guided rehabilitation coaching, pose analysis, rep counting, form feedback, for a health context where uploading video to the cloud would be both a privacy liability and an adoption killer.
All computer vision runs on-device using Apple's Vision framework: skeletal tracking, compensation detection, and range-of-motion metrics computed locally at interactive frame rates, with spoken cues and an accessibility-first interface designed for users with aphasia. Only derived metrics ever sync; the video does not exist anywhere but the user's phone.
A clinical-grade privacy posture achieved through architecture rather than policy: there is no health video to breach, and the privacy story doubles as the product's most persuasive feature.
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