Problem we solve
Deploying Claude Code, Codex, or GitHub Copilot doesn't automatically make them useful. Without connecting them to your workflows, permissions, internal knowledge, and development standards, productivity gains tend to stay at the individual level.
AI Agent Adoption Support goes beyond tool selection. We help you design the rules for safe use, connect AI to your internal knowledge, build prompt and operation patterns, and see adoption through to the team and organisation level.
Who it's for
Primarily for engineering, IT, and DX teams that want to move from individual experimentation with AI agents to team-wide or organisation-wide adoption.
In particular, for organisations using Claude Code, Codex, or GitHub Copilot for development, research, documentation, or support workflows who also need to manage information-security risk and maintain output quality.
Key features
- Adoption policy and use-case design
We audit your existing workflows and identify which tasks to delegate to AI, which require human judgment, and which to avoid. We design an adoption scope small enough to validate quickly.
- Claude Code, Codex, and Copilot enablement
We establish the right tool split, configuration, and operation patterns for development, research, spec comprehension, code review, and document update use cases.
- Internal knowledge connectivity
We design how AI agents can reference the internal knowledge they need, including organising Confluence, Slack, and Jira content and connecting to a search platform. Integration with Gatepath is also supported.
- Safe operation rules and change management
We define what information can be sent to AI tools, what output requires review, how permissions and logs should be handled, and team usage rules — then support rollout from PoC to full adoption.