AI agents that solve operational problems, not demo problems
Design and build practical AI agents for triage, retrieval, workflows, reporting and decision support — with human oversight and a clear business purpose.
Most teams do not fail on AI because the model is “weak.” They fail because the problem is vague, the workflow is undefined, the data is messy, or the organisation is asking the model to fix a broken process. Barberry treats AI as a systems problem first, a model problem second.
What “good” looks like
Examples: internal Q&A over approved documents, operational handoff from lead form to human, exception triage with a next step, first-pass analysis of technical inbound information, and assistants that retrieve the right context at the right time.
How engagements run
- Define the job to be done and success measures.
- Clarify data and system boundaries.
- Decide what the agent may do, may recommend, and what stays with a human.
- Build retrieval, the action layer, and handoff paths for real operations.
- Measure time saved, response quality, and error rate — or stop.
Discovery from R12,000. Projects and managed agents are quoted after scoping. Barberry will not sell “AI for everything” — a lightweight pilot can grow into a production AWS-native design when the model of work is sound.
Not sure where to start? An opportunity map, a scoping sprint, or a review of an existing concept are strong entry points.