Reliable AI in production
Automate and govern the full lifecycle of your machine learning models, GenAI services and AI agents — from training and deployment to monitoring, drift detection and retraining.
Building a model is the start. Running it is the job.
Data changes, accuracy slips and usage grows. MLOps and AIOps keep every model and AI service accurate, accountable and under control long after launch — automatically, and with no code.
MLOps
Lifecycle of AutoML models: train, validate, deploy, predict, version, monitor
AIOps
Operations for GenAI services and agents: monitoring, usage, controls and alerts
Observability
See accuracy, drift, failures and response time
Governance
Access controls, approvals and a full audit trail
Six stages. Fully automated.
Each stage runs automatically, with approvals where you need them.
Automatic training
Train, evaluate and approve new models.
Train & validate
Track experiments and test model quality.
Deploy & serve
Release approved models with rollback.
Automatic prediction
Run approved models on new data.
Model versioning
Track versions, compare results and roll back.
Monitor & improve
Detect drift and trigger validated retraining.
Monitoring feeds back into training, so models keep improving as your data changes.
Observability & data drift
The same controls apply to every model, GenAI service and agent on the platform.
Every invocation, logged
Log every model invocation to track usage per request, invocation counts and overall model utilisation.
Access controls and audit trail
Who trained, approved, deployed and called each model
Data drift detection
Compare incoming data with training data
Accuracy tracking
Track model accuracy and prediction changes
Execution monitoring
Monitor execution, failures and response time
Alerts & retraining
Alert on drift and trigger retraining
Operate GenAI and agents with the same discipline
GenAI services and AI agents need operating too. AIOps brings monitoring, usage tracking and governance to every prompt-based service and agent workflow.
Service monitoring
Track response time, failures and availability of every GenAI service and agent.
Usage & utilisation
See calls per service, per user and per application, and how capacity is used.
Agent run history
Review each agent run step by step: what it decided, which tools it called and the result.
Approvals & human in the loop
Require sign-off before new versions go live or before agents take sensitive actions.
Model choice per service
Switch the LLM behind a service, including Graphy LLM, and compare results before rolling out.
Audit & compliance
A complete record of requests and responses for regulators and internal review.
Faster releases. Reproducible results. Lower operational risk.
Faster releases
Automated training, validation and deployment take models from data to production without manual hand-offs.
Reproducible results
Every version, experiment and dataset is tracked, so any result can be explained and repeated.
Lower operational risk
Drift alerts, rollback and audit trails catch problems early and keep AI accountable.
Keep your AI accurate and accountable
See MLOps and AIOps monitor, retrain and govern models on your own data.