Services
AI Agent Build & Operations
From promising prototype to a reliable operational asset.
Learn moreOverview
We start by designing and implementing an AI agent around its users and operational context. After release, changes to models, prompts, tools, permissions, and data are managed through one loop of evaluation, observation, control, and improvement.
Alongside AgentOps for individual systems, we design and establish an Agent Management Office (AMO) to govern the organisation's agent portfolio.
Built to keep improving.
Evaluation, observability, access, and cost designed into operations.
Explore capabilitiesExpertise & Deliverables
Design, Implementation & Lifecycle
Use cases, interaction and task flows, models, prompts, connected tools, data and human review, followed by clear ownership, versioning, and retirement rules.
Evals & Quality Assurance
Success criteria, evaluation datasets, regression tests, human-in-the-loop review, and evidence-based release gates.
Observability & Incident Response
Traces, tool calls, latency, errors, quality, token use, and cost monitoring with detection and escalation paths.
Security & Governance
Identity, least privilege, data boundaries, audit logs, approvals, guardrails, and red-team readiness.
Agent Management Office
An AMO operating model for intake, prioritisation, accountability, standards, review, KPIs, vendors, and enablement.
Multi-Model, Multi-Tool Operations
Claude, Google Gemini, ChatGPT/Codex, Microsoft Copilot, and other tools managed by use case, quality, access, and cost.
How We Work
Inventory & Risk
Inventory agents, data, tools, permissions, and owners, then classify criticality and risk.
Controls & Operations
Design evals, observability, guardrails, approval gates, and incident response.
Operate & Improve
Use runtime evidence and business outcomes to improve quality, cost, access, and lifecycle decisions.
