Platform
The durable AI stack.
Seven layers from deployment to governance. Each one makes the next stronger. Start with what you need today and standardize on a platform that stays relevant for the decade ahead.
01
Private Deployment
Models run on hardware you control — a DGX Spark, a Mac Studio, a phone, or a $5 MCU. Nothing phones home. Air-gapped configurations available. Your data stays in your building.
- ✓On-premise, private cloud, or edge deployment
- ✓Air-gapped and compliance-ready configurations
- ✓GPU, NPU, and MCU targets supported
- ✓No cloud dependency, no telemetry
02
Model Modules
Domain-specific models for legal, tax, finance, healthcare, industrial, and audio workflows. Each model is trained for a specific job, benchmarked on real hardware, and production-ready.
- ✓10 production edge models, 3 domain NLP models
- ✓Sub-60μs inference on latest phones
- ✓Models from 29 KB to 704 MB — fit any target
- ✓Multi-language support where applicable
03
Signed Artifacts
Ed25519 signatures on every model. Verify provenance, integrity, and chain of custody before any deployment. Know exactly what you're running and where it came from.
- ✓Ed25519 cryptographic signing on every artifact
- ✓CycloneDX SBOMs for supply chain transparency
- ✓Model cards with training lineage
- ✓Tamper-evident distribution
04
Benchmark Evidence
913 measurements from physical silicon — Snapdragon NPUs, Apple ANE, Cortex-M MCUs, Jetson, automotive platforms. Latency, throughput, energy, and thermal impact. Failures included.
- ✓Real-device profiling across 100+ platforms
- ✓Latency, throughput, energy (μJ), and thermal delta
- ✓MCU RAM/ROM and battery life estimates
- ✓Published results including negative outcomes
05
Audit Trails
Every deployment records sector, data sensitivity classification, model version, sign-off status, and incident history. Built for teams that face auditors, not just users.
- ✓Deployment risk assessment per engagement
- ✓Data sensitivity classification protocols
- ✓Model version and configuration tracking
- ✓Incident logging and response documentation
06
Governance Controls
Responsible deployment is operational, not aspirational. Stakeholder impact metrics, risk registers, and quarterly governance reviews — designed for B Corp-ready accountability.
- ✓Stakeholder impact metrics tracked monthly
- ✓Risk register for every deployment
- ✓Quarterly governance reviews
- ✓Responsible AI principles operationalized
07
Ongoing Updates
Platform subscribers get model updates, new benchmark results, security patches, and governance framework revisions. The relationship compounds — the platform gets stronger over time.
- ✓Continuous model improvement and retraining
- ✓New benchmark results as hardware launches
- ✓Security patches and vulnerability response
- ✓Governance and compliance framework updates
Long-horizon value
Today, tomorrow, and ten years from now.
Today
Deploy private AI safely
Get production models running on your hardware with signed artifacts and benchmark evidence. Immediate operational value.
Year 2–3
Reduce vendor dependence
Standardize workflows on your own AI operating layer. Stop sending sensitive data to third-party APIs. Own your inference stack.
Year 5–10
Own your AI infrastructure
A compounding platform with governance, audit trails, and continuous improvement. The trust layer for your entire AI operation.
Start with an assessment.
Tell us what you need. We'll map the architecture, risks, and ROI — then you decide.