AI Infrastructure · Edge-first

Scalable AI Infrastructure for Industrial Automation

Rayantra provides high-performance AI infrastructure designed for autonomous robotics, warehouse intelligence, and industrial automation — quantized, distributed, deterministic.

Start BuildingRequest Demo
0k
QPS sustained
0ms
p99 inference
0.00%
Availability
RYN-Edge · NPU
v3.4.1
NPU · Vision78%
GPU · Policy62%
CPU · Orchestration34%
Memory · Working set48%
Active models
  • vision-skU-v860 fps · 6.2 ms
  • intent-pred-v3120 Hz · 1.8 ms
  • anomaly-clf-v210 Hz · 0.9 ms
Nodes
412
Sites
12
p99
8 ms
Infrastructure Overview

A vertically integrated stack — silicon to policy.

From silicon-aware inference runtimes to cross-site model orchestration, every layer is engineered, owned, and tunable.

  • Inference latency
    8 ms p99
  • Sustained QPS
    120k
  • Model size cap
    12 GB
  • Concurrent models
    32 / node
  • Quantization
    INT8 · FP16
  • Hardware
    NPU + GPU + CPU
Core Pillars

Six pillars, one cohesive infrastructure.

01

Edge AI Computing

Quantized inference on dedicated edge nodes. Local-first, network-tolerant.

02

High-Performance Processing

INT8/FP16 mixed-precision runtimes with hardware-aware batching and KV-cache reuse.

03

Distributed AI Architecture

Cross-region model orchestration with versioned policies and zero-downtime rollouts.

04

Real-Time Data Intelligence

Streaming pipelines with sub-second materialization and anomaly detection built in.

05

Vision Infrastructure

Multi-modal perception — depth, semantic, OCR, SKU re-identification — at 60 fps.

06

Industrial Automation Systems

Closed-loop control models tuned per-line with continuous, safe online learning.

Architecture

An end-to-end AI infrastructure pipeline.

Stage 01
Sensors
Cameras · LiDAR · IMU
Stage 02
Edge Node
RYN-Edge · NPU
Stage 03
Inference
Vision · Policy
Stage 04
Coordinator
Fleet · Auction
Stage 05
Operator
Dashboard · API
Capture · ingest8 ms end-to-endDecision · actuate
Infrastructure Workflow

Train, validate, deploy — on a closed loop.

  1. 01

    Train

    Offline + simulation. Cluster-scale training pipelines.

  2. 02

    Validate

    Replay, simulation, shadow inference, policy gates.

  3. 03

    Deploy

    Versioned models pushed to edge via zero-downtime rollouts.

  4. 04

    Observe

    Live telemetry, drift detection, automated rollback.

Deployment Infrastructure

Four deployment models — your topology, your rules.

Model 01

Cloud-managed

Rayantra-hosted control plane. Edge nodes shipped pre-provisioned.

Model 02

Hybrid

Customer cloud + Rayantra edge. Best for compliance-bound workloads.

Model 03

On-premise

Fully customer-managed deployment with Rayantra support.

Model 04

Air-gapped

Zero outbound network. Signed releases via removable media.

Security Infrastructure

Industrial-grade security at every layer.

Designed for regulated operators. SOC 2 Type II, ISO 27001, GDPR-ready.

  • 01

    Signed firmware + hardware-rooted attestation

  • 02

    mTLS everywhere — no plaintext on the wire

  • 03

    Customer-managed encryption keys (BYOK)

  • 04

    Air-gap deployment with zero outbound network

  • 05

    SAML/OIDC SSO + SCIM provisioning

  • 06

    Full audit log streamed to your SIEM

Infrastructure Analytics

Live observability — every model, every node.

Inference / s
118,420
live
p99 latency
8.2 ms
↓ 1.2 ms
Drift index
0.04
stable
Rollouts / 7d
32
auto
AI Infrastructure

Run industrial AI at the edge, at any scale.

From a single edge node to a global mesh — Rayantra's AI infrastructure is built for the realities of the warehouse floor.

Start BuildingRequest Demo
Pilot in 6 weeksHardware leasing availableGlobal field engineering
Platform · Live
Edge nodes
412
Active fleet
248
Sites
12
p99 latency
8 ms