AI on Edge
Run the Model Where the Data Lives
Not every workload can, or should, go to a hosted model. Edge deployment suits places with strict data residency rules, poor connectivity, or response-time needs that a network hop cannot meet. We select and tune small models for your task, quantize them for the target hardware, and package them with update and monitoring tooling so a fleet of devices or sites stays manageable.
None required
Data leaving the site
Optional
Network dependency
1B–7B class
Model size
Device / Server / Air-gap
Targets
Use Cases by Industry
Visual quality checks on the line, where latency and connectivity are limited
Document classification inside secured networks
Analytics close to the network element instead of a central data lake
Clinical text assistance on local hardware so records stay in the facility
How It Works
Constraint Mapping
Record the hardware, memory, latency and connectivity limits at each site.
Model Selection & Tuning
Pick a compact model and fine-tune it on your domain data.
Optimization
Quantize and benchmark on the real target hardware, not a lab machine.
Fleet Operations
Ship signed updates, collect health signals and roll back safely.
BringUsOneWorkflowThatNeedsFixing
Send us a process, a dataset or a customer journey. In a 30-minute call we will tell you whether AI is the right tool, what it would take, and what we would build first.