AI Deployment
We put AI systems into production — in weeks, not quarters.
Models, data, cloud platform and agents — designed, engineered and operated inside your own environment. We are measured on outcomes, not effort.
What we do
Three commitments, from first conversation to steady state.
Strategy that ends in a build
We size the outcome, pick the one system worth doing first, and commit to a date.
Learn moreEngineering inside your stack
Data, platform and application work done in your cloud, with your keys and your controls.
Learn moreOperations after go-live
Monitoring, evaluation, retraining and support — the system stays owned, not abandoned.
Learn moreServices
The work required to make AI operational.
AI-ready data
Most AI programmes stall on data, not models. We build the pipelines, contracts and semantics that make your data usable by systems, not just dashboards.
- Ingestion and pipeline engineering
- Lakehouse and vector stores
- Data quality, lineage and contracts
- Access control and masking
Cloud and platform engineering
The runtime your AI depends on: reproducible infrastructure, GPU and inference capacity, networking and cost control, delivered as code in your own account.
- Infrastructure as code
- Kubernetes and container platform
- Model serving and inference
- FinOps and capacity planning
AI systems build
Models, retrieval, orchestration and interfaces assembled into a working system that touches a real business process end to end.
- Retrieval and knowledge systems
- Workflow orchestration
- Integration with core systems
- Human-in-the-loop interfaces
Evaluation and governance
Nothing goes live without a baseline and a way to prove it. Policies, approvals and audit trails are part of the build, not a later project.
- Baselines and acceptance criteria
- Automated evaluation harnesses
- Policy, approvals and guardrails
- Immutable audit trail
Managed operations
After go-live we run it: monitoring, incident response, drift and cost management, and a roadmap for the next system.
- 24/7 monitoring and alerting
- Drift and quality management
- Continuous improvement releases
- Named engineering support
Sectors
Where we deploy.
Financial services
Risk, servicing and back-office workflows with a defensible audit trail.
Energy and utilities
Asset, field and grid operations where downtime is expensive.
Healthcare and life sciences
Clinical and administrative workloads under strict data control.
Manufacturing and supply chain
Planning, quality and maintenance built on operational data.
Public sector
Citizen services and casework inside your own jurisdiction.
Professional services
Knowledge-heavy delivery work made faster without losing review.
How we work
A delivery path that ends in an operating system, not a slide deck.
Discovery and design
We map the process, agree the measurable outcome and design the system around it.
Build and integrate
Engineering in your environment, connected to the systems the process already runs on.
Evaluate and harden
Tested against a baseline, with guardrails, approvals and security review before anyone relies on it.
Deploy
Controlled rollout to real users, with the fallback path defined in advance.
Managed service
We operate the system, report on the outcome and keep improving it.
of AI pilots never reach production
Industry research, 2024
to the first system live in your environment
CerebrixOS delivery baseline
of workloads run inside your own cloud
Standard engagement model
Partnering with the industry's best
Start with one system worth paying for.
A 30-minute conversation is enough to size the outcome and agree what live looks like.