Your orders arrive on WhatsApp. Your agent keys them in.

step 1 · scope

Start with one workflow, not a transformation programme

We don't sell a platform. We pick one workflow with a cost you can measure — unmatched payments, manual order entry, a support queue — and scope it against your real volumes and real documents. You get a fixed price in RM, a delivery plan measured in weeks, and a clear line between what the agent does and what stays with your team.

• Discovery against your actual documents and systems
• Claude-first model selection — another model where it fits better
• Integration with the ERP, CRM or core system you already run
• Fixed-price build, agreed before work starts

step 2 · grade

Every agent gets a grade before it goes live

An agent that works in a demo and an agent that works at month-end close are different machines. Every build is scored with Agent GPA — accuracy, groundedness, safety and cost against a test set built from your data — then load-tested at your peak volumes, with failure modes written down. You see the scorecard before go-live, not after something breaks.

• Agent GPA scorecard shared with your team
• Load tested at peak volumes, not average
• Failure modes documented with fallback behaviour
• Re-scored after every model or prompt change

step 3 · run

Run by engineers who carry the pager

Go-live is the start, not the finish. Under AgentOps every agent runs against SLOs with tracing, spend monitoring and continuous evaluation — and when something breaks at 2am, it pages us, not you. The team behind AI Deploy has handled 300+ production incidents; that experience is built into how each agent is instrumented from day one. Compliance is designed in the same way: PDPA-aware data flows, access control and human-in-the-loop checks where they belong.