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Keep your AI
infrastructure
right-sized.

The platform automatically reassesses your AI needs, model mix, routing and infrastructure requirements as your work changes.

You get recommendations on what to adjust, why and what each change would save.

Get started Follow the loop
The continuous optimisation loop Monitoring identifies workload, quality, cost or capacity changes. Assessment benchmarks the options and automatically recommends model, routing and infrastructure changes. Approved updates are deployed and compared with the baseline. Classifier retraining is a separate path when new learning is needed. CONTINUOUS OPTIMISATION Monitoring Workloads · quality · capacity Routing outcomes + cost changes Assessment Re-benchmark affected workloads Compare models + capacity costs Recommend Models · routing · capacity Cost + impact + deployment plan Approved deployment Apply the agreed change Verify against the baseline Automatic reassessment. Model, routing and infrastructure choices. REASSESS WHEN WORK OR ECONOMICS CHANGE The continuous optimisation loop Monitoring identifies workload, quality, cost or capacity changes. Assessment benchmarks the options and automatically recommends model, routing and infrastructure changes. Approved updates are deployed and compared with the baseline. Classifier retraining is a separate path when new learning is needed. CONTINUOUS OPTIMISATION Monitoring Workloads · quality · capacity Routing outcomes + cost changes Assessment Benchmark the changed workloads Compare model + capacity options Recommend Models · routing · capacity Cost + impact + deployment plan Approved deployment Apply the agreed change Verify against the baseline VERIFY THE CHANGE IN PRODUCTION

Automated recommendations.
Not another audit to book.

 View decision detail
Decision What the platform reassesses What comes back
Model mix Which tasks are growing, which models pass their quality tests and what each suitable option costs. A recommended mix of private and approved API models, with the work each should handle.
Inference capacity How changed workload volumes and model requirements fit the capacity in the deployment. Whether to retain, resize or change the private inference setup, rather than leave capacity mismatched to the work.
Routing Whether current destinations and routing choices still fit the workload, prices and data requirements. An updated routing recommendation. A classifier retrain only follows when the model needs new information for decision making.
Deployment The cost and workload fit of staying with certain providers, APIs, running privately or using a mix. A model and deployment plan. Keep your current provider or infrastructure where it remains the better fit.

The Platform responds to material changes.

Workload mix and volume

New, unknown or previously rare tasks become a meaningful share of traffic. The capacity and model choices are reassessed against the changed mix.

Quality and routing

Task outcomes or routing success fall below the configured requirement. The evidence shows whether to change model choice, routing policy or classifier training.

New model options

A new cloud or open-weight model becomes a credible alternative for existing work. 

Economics and constraints

Provider pricing, infrastructure use, energy conditions or approved data requirements change enough to alter the deployment decision.

From evidence to deployment.

  1. Observe the deployment.

    The Monitoring Engine collects workload, cost, quality, routing and infrastructure evidence. A configured trigger identifies which part of the current setup needs another look.

  2. Reassess the affected work.

    The Assessment Engine updates the baseline, checks quality and data requirements, and compares model and deployment options before recommending a change.

  3. Prepare the right update.

    Price or capacity changes may only need a different model mix or routing policy. New tasks or drifting routing may need an updated classifier. 

  4. Approve and deploy.

    Infrastructure changes require your approval. Classifier updates can deploy automatically if enabled, after evaluation and shadow testing.

  5. Verify the results

    Monitoring watches the new deployment. Live and pilot costs and task outcomes show whether the change should be retained, refined or rolled back.

For the training data, fine-tuning and release checks behind self-improving routing, follow the classifier’s own cycle.

How the classifier improves

Find out what your AI
should be costing you.

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