Transform verified environments into trusted spatial intelligence.
A spatial intelligence layer — not another scanning tool.
Mission-relevant spatial intelligence requires closing three gaps.
Capture-to-Meaning
Capture produces point clouds, photogrammetry, and mesh geometry. None of those are decisions. Reality Engine adds the metadata, semantic context, segmentation, and AI-assisted interpretation that turn geometry into something operators can actually plan against, train on, or audit through.
Trust vs. Generative
The boundary between verified capture, derived reconstruction, AI-assisted interpretation, and generative approximation cannot be a footnote. Reality Engine builds the four-tier trust taxonomy directly into the environment, so users see at a glance what's source-of-record and what's modeled, interpreted, or synthesized.
Edge / On-Prem Deployment
Federal, defense, and intelligence missions often operate in secure, edge, or air-gapped environments. Reality Engine deploys as software, as a Dell-compatible appliance configuration, or as a multi-node enterprise architecture — fitting where the mission already is, not forcing the mission into someone else's infrastructure.
What Reality Engine does today.
Trust Layers Enforced
Verified capture, derived reconstruction, AI-assisted interpretation, generative approximation — each rendered with explicit provenance badges and confidence indicators.
Capture Source Types
LiDAR, photogrammetry, mobile mapping, UAS imagery, ground photography, video, structured metadata. Multi-source fusion under one governed environment.
Provenance Tracked
Every object, surface, and label carries source attribution, capture timestamp, operator, and trust-tier classification. Audit-ready by default.
Deployment Modes
Software deployment, Dell-compatible appliance, edge, on-prem, secure customer-controlled environment, multi-node enterprise. The environment fits the mission.
Honest Bounds — What's Still in Scope
Production-scale ingestion of multi-petabyte capture archives, real-time collaborative editing of spatial environments across distributed teams, and automated validation of AI-assisted interpretation against ground-truth datasets remain active development scope. Reality Engine is presented as a credible, deployment-ready spatial intelligence layer — with bounded activation, processing, and sustainment packs designed to grow alongside customer mission scale, not as a finished platform that pretends every capability is mature.
From verified capture to mission-ready environments.
- Mission planning & rehearsal on governed environments where every label, geometry, and annotation carries explicit provenance.
- Cultural heritage preservation that maintains verified-capture authority while permitting AI-assisted interpretation as a clearly separated layer.
- Training & simulation built on environments operators can trust — and audit — down to individual asset attribution.
- Physical security & facility intelligence with secure deployment options that fit federal, defense, and intelligence access models.
- Robotic & agentic AI readiness — spatial environments that AI agents can reason over without inheriting unverified or hallucinated geometry.
Today, conditional, and roadmap.
Software, appliance, & edge deployment
Aperio software deployment, Dell-compatible appliance configuration, edge, on-prem, secure customer-controlled environments, and multi-node enterprise architectures. Available through Carahsoft procurement pathways today.
NVIDIA accelerated reconstruction
Pair Reality Engine with NVIDIA accelerated computing for higher-throughput reconstruction, AI-assisted segmentation, and OpenUSD-aligned interchange. Subject to integration scope and customer infrastructure.
Agentic AI environment access
API surfaces and trust-tier-aware access patterns that let AI agents reason over governed spatial environments without inheriting hallucinated geometry. Planned post-pilot once base trust enforcement is validated at customer scale.