Our technology expertise
The right platform.
Chosen for your workload.
Explore the cloud, data and AI platforms we build with. Start with the workload, understand the tradeoffs and see how each technology fits into a system your team can operate.
01 / Run the system
Cloud platforms
AWS · Microsoft Azure
02 / Make data usable
Data platforms
Databricks · dbt · Snowflake
03 / Add evaluated intelligence
AI platforms
Azure OpenAI · OpenAI
These platforms can work together. The map shows their roles, not a requirement to adopt every layer.
Explore by platform role
Find the expertise your stack needs.
Cloud platforms
Choose the infrastructure, identity and operating environment for your applications and data.
AWS
Application hosting, data services and cloud infrastructure with explicit account, network and operating boundaries.
- Cloud architecture
- Application hosting
- Infrastructure automation
Evaluate first
Service complexity, account ownership and the cost of operating the selected architecture.
Microsoft Azure
Cloud applications and data workloads connected to enterprise identity, subscriptions and operating policies.
- Enterprise identity
- Cloud foundations
- Application integration
Evaluate first
Tenant boundaries, existing Microsoft dependencies, network access and workload economics.
Data platforms
Organize storage, processing and transformation around trusted datasets and analytical workloads.
Databricks
A lakehouse environment for data processing, analytical workloads and machine-learning workflows over governed datasets.
- Lakehouse engineering
- Data processing
- ML workflows
Evaluate first
Workload size, compute patterns, data governance and the skills needed to maintain the platform.
dbt
Versioned analytical transformations, model dependencies and data tests within a supported data platform.
- Analytics engineering
- SQL models
- Data tests
Evaluate first
Source readiness, metric ownership, adapter compatibility and responsibility for scheduled runs.
Snowflake
A managed analytical data platform for governed datasets, SQL workloads and shared consumption.
- Data warehousing
- SQL analytics
- Workload isolation
Evaluate first
Query patterns, warehouse consumption, data access and refresh requirements.
AI platforms
Connect model capabilities to approved context, application controls and measurable evaluation.
Azure OpenAI
OpenAI model integration within an Azure environment, connected to enterprise identity, data and operational controls.
- Azure AI integration
- Enterprise context
- Model evaluation
Evaluate first
Deployment availability, region, capacity, identity and data-processing requirements.
OpenAI
Model and API capabilities integrated into applications with approved context, controlled actions and task-specific evaluation.
- API integration
- Retrieval workflows
- Evaluation
Evaluate first
Task quality, data handling, model availability, latency and usage costs.
Technology follows the decision
Keep what works.
Change what holds you back.
Start with the constraint
Identify the latency, data quality, delivery or operating problem before shortlisting platforms.
Account for the whole cost
Include migration, licensing, usage, team skills and the responsibility of running the system.
Test a representative workload
Use your data and access requirements to compare options against agreed acceptance criteria.
Choose a delivery path
Translate the platform decision into a scoped implementation, migration or operational improvement.
Have a platform. Need a delivery plan?
Our service pages explain the work, acceptance criteria and handover. Bring your current stack and the outcome you need.