Services
AI & Agentic Systems
Move AI ideas into bounded, observable production systems with evidence for their cost, quality and behaviour.
What this delivers
Useful AI systems depend on far more than a model endpoint. We start with the operational outcome and design the retrieval, data, evaluation and hosting path needed to support it, making latency, quality, privacy and cost trade-offs visible from the outset.
For agentic workflows, permissions and tool access are constrained deliberately, with human gates where consequences demand them. Evaluation and telemetry make behaviour reviewable, giving organisations a practical route to adoption without treating experimentation as a production control model.
AI & Agentic Systems Services
From strategy and architecture through to implementation and ongoing operation, we help you build the capabilities needed to deliver secure, reliable digital services. Engage us for consultancy and design, a complete project deliverable, embedded expertise alongside your team, or as your managed service provider.
AI Platform Enablement
The infrastructure, data pipelines, and controls that let AI workloads run safely alongside everything else you operate.
AI experiments often reach a production boundary where data freshness, model access, evaluation, latency and cost have never been designed as an operating system. Without that foundation, promising prototypes become difficult to govern and unreliable under real demand.
We build the hosting, retrieval and data path around measurable use-case requirements, adding evaluation and control points before scale amplifies weak assumptions. Teams can release AI capabilities through known platform practices, while leaders gain visibility of quality, spend, access and operational risk.
What we deliver
- Model hosting across managed endpoints and self-hosted inference, with cost and latency trade-offs made explicit
- Vector search design: embedding strategy, index selection, and retrieval evaluation
- Data pipelines for ingestion, chunking, enrichment, and refresh
- Governance covering data residency, access control, prompt and response logging, and evaluation gates
- Enterprise use cases: knowledge retrieval over internal documentation, support triage, and code assistance
- Azure-native AI foundations with Azure AI services and AKS, Azure AI Search, Data Factory, Azure Functions, Azure Policy, RBAC, Azure Monitor, and AI Studio
- Backstage integrations for AI-service onboarding, endpoint provisioning, retrieval pipeline creation, governance checks, CI/CD, identity, operations, and delivery workflows
- Azure
- Backstage
AI Agentic Development
Agentic systems built with the same engineering rigour as production infrastructure — bounded, observable, and reversible.
Agents can coordinate complex work, but autonomy increases the consequence of ambiguous goals, excessive permissions and weak failure handling. Production adoption requires a clear account of what an agent may decide, which tools it may invoke and where human approval remains essential.
We decompose workflows into bounded responsibilities, constrain tool contracts and introduce evaluation, audit and recovery paths around each consequential action. The organisation gains useful automation that can be inspected and improved, without surrendering accountability or making failures impossible to unwind.
What we deliver
- Multi-agent orchestration: task decomposition, planning, and hand-off patterns
- Tooling integration with your existing APIs, data stores, and internal services
- Workflow automation for triage, remediation, and routine operational tasks
- Safety and governance: permission scoping, human-in-the-loop gates, and full audit trails
- Agentic workflow examples: incident triage and enrichment, pull-request review, and infrastructure change proposals
- LangChain and Python for agent workflow implementation
- .NET and the Copilot SDK for production agent development
- Azure AI Foundry for managed agent capabilities
- Copilot
- LangChain
- Azure
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