An AI stack built for production.
Metabytes designs AI systems across models, orchestration, company knowledge, tools, governance, evaluation and infrastructure. The stack changes with the workload, but every layer has a clear job.
Built to change, control and improve.
The layers are deliberately separated so models can change, permissions stay enforceable, tools stay controlled and the whole workflow can be evaluated and operated in production.
These five rules shape every layer that follows.
Where people and systems meet the AI.
The experience layer is where AI becomes usable — in an app, a workflow, search, chat or an API. Every entry point connects to the same orchestration, controls and company context.
The interface can change without rebuilding the intelligence underneath.
The control plane of the AI system.
Orchestration manages workflow state, assembles context, selects models, calls tools, applies policy, handles retries and decides what should happen next.
The model provides intelligence. Orchestration turns it into a reliable workflow.
The right model, ready to change.
Different workloads trade off reasoning quality, latency, cost, context, modality and deployment. The routing layer keeps those choices outside the application, so models can change without rebuilding the product.
The right context, not everything.
The knowledge layer connects AI to the information it needs without exposing the whole organisation. Sources are indexed, permissions preserved and only the context the task needs is assembled.
Grounded, permission-aware and traceable back to source.
Controlled access to real systems.
The tool layer turns AI decisions into controlled business actions. Each tool is narrowly scoped, typed and permissioned, so the system can only read, write or trigger what the workflow requires.
Higher-risk writes are explicit, auditable and approval-gated.
Governance runs inside the stack.
Governance is enforced as the system runs. Identity, data policy, permissions, approvals and audit shape what context is accessed, which tools are used and what actions are allowed on every request.
Rules are executable, observable and changeable after launch.
Test the workflow, not the answer.
Evaluation checks whether the system completed the task, used the right context and tools, respected policy and approvals, and produced an acceptable outcome.
Tests cover routine work, edge cases and when the system should stop or escalate.
Visible behaviour, versions and cost.
Once a system is live, every workflow should be traceable. Prompts, models, tools, sources and evaluations are versioned, cost is visible per workflow and failures trigger clear signals.
Easier to debug, compare releases, control spend and improve reliability.
Deploy where the data allows.
AI can run through provider APIs, cloud AI platforms, private open-model infrastructure or a hybrid gateway. The right route depends on the data, performance needs and control model.
Choose it deliberately, not by default from the first provider used.
Optimise the path, not the token.
Model price is only one part of the economics. Real cost and performance are shaped by routing, context size, cache reuse, tool calls, retries, latency and fallback across the whole workflow.
Use the least expensive path that still meets quality, control and resilience.
The stack adapts to the work.
Different tasks need different models, context, tools and controls. The Metabytes stack is configured around the work, rather than forcing every use case through the same architecture.
The architecture stays consistent. The configuration changes.
Buy the commodity. Engineer the advantage.
Not every layer needs custom engineering. We use proven platforms for standard capabilities, then invest where workflow fit, control, data handling or differentiation actually matter.
Decided component by component, so the stack moves fast without unnecessary custom code.
Build one layer, or the whole stack.
Each layer can be designed, built or improved on its own, but production AI usually spans several. Our services map directly to the architecture, so you can start with the problem, the layer or the capability you need.
Work on one part of the stack, or hand us the full system.
See how the complete Metabytes AI stack fits together.
Five core layers handle the work. Governance, evaluation and operations run across all of them.
Questions about our AI stack
Bring us the workflow.
Show us where work is slow, repetitive or difficult to scale. We can help identify whether AI, automation or a simpler system is the right next step.