Competitors retrofit AI onto systems designed for a pre-AI world. Cyrus is AI-native from the first line of code — which is why it does things their architecture structurally can't.
Two kinds of tools dominate today: computer-vision software where vision is the product, and data platforms where operational telemetry is the product and video is thin or absent. Here's how each stacks up.
| Capability | Industrial CV Vision software |
Data Platforms Telemetry & dashboards |
Cyrus AI-native, from scratch |
|---|---|---|---|
| Best AI model for each task | ~ Custom CNNs, per task | ~ Thin vision | Frontier & open models, per task |
| Multi-model orchestration | Single-model stack | Single-model stack | Routed for cost, latency & accuracy |
| Memory of your site | No retained context | ~ Data history, no reasoning | Context, history & policy retained |
| Planning & prediction | Detection only | ~ Batch analytics | Real-time anticipation of failures & risk |
| Frictionless adoption | ~ Often hardware-locked | Months of integration | Existing sensors, deploys in days |
| Configurable per use-case | ~ Single-vertical | ~ Heavy services per build | One platform, verticalized by config |
| Self-improving over time | Static after training | No feedback loop | Expert feedback compounds accuracy |
Competitors retrofit AI onto legacy systems. Cyrus is built from scratch with agentic AI at its core.
Frontier AI is powerful but expensive to run on live video. Cyrus's edge preprocessing and smart routing make it viable in real time — measured against both specialized small models and a direct frontier-model wrapper on real CCTV footage.
Internal benchmarks on real CCTV footage (altercations, firearm possession, industrial sites, conference venues), versus a commercial small VLM and a direct frontier-model wrapper. Results vary by deployment.
Incumbents were built for a pre-AI world. Catching up means rebuilding their core stack and re-staffing their teams — slow, expensive, and risky while Cyrus ships.
Built from scratch with AI and agentic workflow at the core — not bolted onto a pre-AI codebase that wasn't designed for it.
Orchestration over any model lets Cyrus ride every frontier gain instead of being locked to a single stack that ages.
Memory and the expert feedback loop make every deployment sharper — widening the lead with each site you add.
Each of these shifted within the last 24 months. The window to build the AI-native operations layer is open now.
Vision-language models now beat task-specific models on visual reasoning — with no per-site model training required.
Rapidly falling cost per token makes always-on, multi-model sensor monitoring economically viable at full facility scale.
Most facilities already run cameras and sensors — and reject costly rip-and-replace hardware refreshes.
Memory, planning, and tool-use have matured enough to turn passive detection into autonomous, context-aware response.
The fastest way to understand the gap is to watch Cyrus run side-by-side with what you have today.