Workload first
Start with latency, context size, users, model behavior, tool needs, uptime, portability, and the consequence of a failure. A favorite processor comes last.
Core specialty: edge AI
Put capable private AI where the work happens.
Redacted AI Solutions designs governed local AI from portable edge nodes to secure on-site systems, for environments where latency, connectivity, sovereignty, or operational control rule out a routine cloud path.
Core specialty · edge to on-premisePortableEdge option
On-siteData boundary
OfflineOperating mode
ScalableWorkstation to cluster
The point
Edge is a different operating reality, and it punishes cloud assumptions.
Field and on-site systems demand careful choices around model size, memory, power, thermals, storage, physical protection, updates, recovery, and user experience. Grace preserves control while R.A.I.S.E. brings inference and data inside the local boundary.
Right-size the model
Select a practical local model and quantization for the evidence task and available infrastructure. Choose a larger model only when it earns its operational cost.
Engineer the lifecycle
Plan provisioning, local storage, identity, isolation, offline updates, backups, telemetry boundaries, physical handling, and recovery alongside inference performance.
Scale without replatforming
Move from a demonstration edge node to a secure workstation, server, or GPU cluster while keeping the Grace control model and R.A.I.S.E. execution boundary.
Work with us
Show us the place the system must work and the network it cannot assume.
We translate field constraints into a model, hardware, governance, and operating profile you can evaluate on the merits.