Sovereign AI infrastructure delivered through a unified API.
Backboard gives organizations one configurable infrastructure layer for running AI across cloud, private cloud, on-premise, and on-device environments.
Build AI without rebuilding the stack
Most AI teams start with a model API.
Then they add memory. A vector database. A retrieval layer. Routing logic. Prompt management. Agent frameworks. Evaluation tools. Monitoring. Security controls. Deployment infrastructure.
Over time, what looked like a feature becomes a fragmented AI stack that is expensive to operate, hard to secure, and difficult to change.
Backboard brings those core capabilities together behind one unified API.
Use the models you want. Deploy where you need. Keep control over your data, infrastructure, and architecture.
One platform for the AI stack
01
Model access and routing
Access frontier, open-source, custom, and locally deployed models through a single infrastructure layer.
Route workloads based on quality, cost, latency, privacy, hardware availability, or deployment requirements.
Avoid hard-coding your product around one model provider or one cloud platform.
02
Persistent memory and context
Give AI systems access to durable memory, retrieval, organizational knowledge, and context across sessions, models, workflows, and users.
Build systems that retain useful information instead of starting from zero every time.
03
Retrieval and knowledge infrastructure
Connect documents, files, internal knowledge, and structured information to AI workflows without assembling a separate retrieval stack.
Backboard helps organizations build private knowledge systems, internal copilots, document intelligence tools, and long-running agents on a common foundation.
04
Orchestration and agents
Coordinate tools, models, memory, workflows, and multi-step reasoning through a unified infrastructure layer.
Build AI systems that can do more than answer a prompt. Build systems that can retrieve information, use tools, follow workflows, evaluate results, and operate across real business processes.
05
Optimization and quantization
Run capable models more efficiently across the hardware available to your organization.
Backboard helps reduce model footprint, improve speed, lower inference costs, and create more deployment options across private infrastructure, edge systems, and devices.
06
Evaluation and observability
Evaluate models, prompts, agents, retrieval strategies, and deployment architectures using measurable performance criteria.
Compare tradeoffs across quality, cost, latency, privacy, memory performance, and hardware constraints before committing to a production architecture.
Deploy AI wherever your organization needs it
01
Cloud
Use managed infrastructure and access leading frontier and open models without locking your applications to a single provider.
02
Private cloud
Deploy within your own cloud environment with stronger control over networking, access, data boundaries, and architecture.
03
On-premise
Run AI inside your own infrastructure for sensitive workloads, regulated operations, and systems that cannot depend on external services.
04
On-device
Deploy optimized models on laptops, workstations, edge hardware, and field devices for lower latency, stronger privacy, and resilient disconnected operation.