// Edge AI Decision Platform

Tell us your deployment. We'll tell you what hardware to buy.

Describe your cameras, model, power, and environment. Get a recommended platform, sized infrastructure, bottleneck analysis, and a BOM your procurement team can use. Jetson, Coral, Hailo, RK3588 — vendor-neutral.

// Decisions teams use EdgeAIStack for
// Bottleneck Workbench — live preview
INCLUDED IN SYSTEM DESIGNER

What breaks first when you
double the cameras?

After every recommendation, push the limits. Double cameras, switch codecs, change models — see compute, memory, power, and storage shift in real time. Find the bottleneck before you buy the hardware.

  • Live compute, memory, power, network, and storage gauges
  • One-click stress scenarios — 2× cameras, worst case, H.265
  • Baseline vs modified comparison
  • Engine-validated with confidence scoring
Open System Designer →
BOTTLENECK WORKBENCH
EdgeAIStack Bottleneck Workbench — live resource utilization simulator for edge AI deployments
// Platform Catalog — 19 platforms, 38 model variants indexed
Jetson Orin Nano
Compute40 TOPS
Power7–15W
Max Streams4× 1080p
Est. Cost~$499
Select in engine →
Jetson AGX Orin
Compute275 TOPS
Power15–60W
Max Streams16× 1080p
Est. Cost~$999
Select in engine →
Google Coral TPU
Compute4 TOPS
Power0.5–2W
Max Streams2× 1080p
Est. Cost~$60
Select in engine →
Hailo-8
Compute26 TOPS
Power3–8W
Max Streams8× 1080p
Est. Cost~$400
Select in engine →
RK3588
Compute6 TOPS
Power5–15W
Max Streams4× 1080p
Est. Cost~$149
Select in engine →
// Under the Hood

10 deterministic engines.
4-tier validation. Zero hallucination.

Every recommendation is computed by specialized sizing engines backed by source-attributed benchmarks and confidence-scored data. No LLM generates the numbers — engines are deterministic functions validated against 137 rules across 1,033 input combinations.

ENGINE LAYER
10 Sizing Engines
  • Hardware Selector
  • GPU Sizing & Stream Capacity
  • Network Bandwidth
  • Storage Endurance
  • Power Budget & Module TDP
  • Inference & Memory Estimator
  • Deployment Cost (BOM)
sub-second per full analysis
VALIDATION
4-Tier Framework
  • L1 — Individual engine unit tests
  • L2 — Orchestrated engine validation
  • L3 — Cross-engine consistency
  • L4 — Architecture validation (AVE)
137 rules 1,033 combinations tested
RESEARCH
Benchmark Pipeline
  • 500+ validated benchmarks
  • Source-attributed, confidence-scored
  • 75 research topics completed
  • 10-stage research-to-production pipeline
900+ research artifacts
// Start with your deployment type
// Agent-Native Architecture

Any AI assistant can call these engines.

EdgeAIStack exposes all sizing engines as tool calls. MCP server for Claude, Cursor, and Windsurf. OpenAPI spec for GPTs and custom agents. One API call runs 8 engines in parallel and returns a complete deployment specification with confidence scores.

MCP Model Context Protocol — 6 tools
OPENAPI OpenAPI 3.1 spec — REST endpoints
RAG 2,721 vectors — engine + research layers
// MCP tool call design_system
{
  "tool": "design_system",
  "input": {
    "model": "yolov8n",
    "camera_count": 8,
    "resolution": "1080p",
    "retention_days": 30,
    "optimize_for": "balanced"
  }
}
// Featured Guides

Still researching?

The engines give you answers. The guides give you context — Jetson power modes, storage endurance, PoE budgeting, thermal constraints, and deployment checklists. Start with the hardware guide or browse all 35+ engineering guides.

READ THE GUIDES →