On-Device AI
Backboard is building toward a full-stack AI environment that can run directly on local devices, including the model, memory, retrieval, coding tools, and workflows.
The goal is simple: bring capable AI closer to the user, reduce dependence on centralized infrastructure, and make advanced systems available even when cloud access is limited, expensive, or not permitted.
Private, local AI for the next generation of work.
Move AI from the data center to the device.
Today, most advanced AI depends on remote infrastructure.
That creates cost, latency, privacy, and availability constraints. It also limits where AI can be used.
Backboard is exploring a future where users can run capable AI systems directly on laptops, desktops, workstations, and specialized edge hardware.
Not just a small local model.
A complete AI stack with local memory, local retrieval, coding agents, workflows, and tools.
Private AI that does not require sending sensitive data externally
Lower operating costs for high-volume workloads
Faster response times with reduced network dependency
AI that works in disconnected or unreliable environments
More control over data, models, and user experience
Better use of increasingly powerful local hardware
A full AI stack, running locally.
The opportunity is larger than running a model on a laptop.
Useful AI requires context, memory, knowledge access, tools, and the ability to complete multi-step work.
Backboard is working toward an on-device stack that brings these systems together.
The future local Backboard stack
Local Models
Run optimized open-weight and custom models directly on supported hardware.
Persistent Local Memory
Retain context, preferences, project history, and useful knowledge without requiring external storage.
Private Retrieval
Search and use approved local files, codebases, documents, and organizational knowledge.
Coding Agents and CLI Workflows
Give developers local AI systems that can understand projects, use tools, generate code, and complete software tasks.
Local Orchestration
Coordinate models, tools, memory, and workflows directly on the device.
This creates AI systems that are more private, more resilient, and more useful in real working environments.
Built for a new generation of developer hardware.
Local hardware is becoming capable enough to run increasingly advanced AI workloads.
As models become more efficient and quantization improves, more AI tasks can move from centralized compute to devices already used by engineers, researchers, and knowledge workers.
Backboard is experimenting with efficient model deployment, quantization, memory architecture, and coding workflows designed for this shift.
A developer can work with AI in a private environment
A company can reduce recurring model inference costs
Teams can operate in air-gapped or restricted networks
Sensitive projects can stay entirely within local infrastructure
AI capabilities can continue functioning without an internet connection
The long-term direction is clear: more intelligence will run closer to the user.
Built for private, capable AI workflows.
Software Development
Run coding assistants, CLI agents, project memory, and local code intelligence directly on developer machines.
Enterprise and Regulated Teams
Keep sensitive files, internal documents, and workflow data inside local or managed corporate environments.
Defense and Disconnected Operations
Operate AI systems where connectivity is restricted, unreliable, or unavailable.
Research and Technical Teams
Experiment with local models, private datasets, and reproducible workflows without depending on external APIs.