Put one AI agent into production. With the tests to prove it works.
We pick one workflow with you, agree the test cases before building, and build the agent on your data and systems. It goes live only if it passes.
- Test set signed off by your process owner
- Pass rate above your threshold
- No critical failures in the final run
- Low-confidence answers reach a named person
- Every answer and action logged
- Runbook and rollback handed over
Good first workflows. And ones to leave for later.
We'll tell you on the first call whether a workflow is a good first agent.
Good fit
- Repetitive work with a clear right answer, such as invoice matching, ticket triage, document checks or report drafting
- Data the agent can reach through a database or an API
- A person who owns the outcome and can review edge cases
- Enough volume that the time saved is worth measuring
Not a fit yet
- Decisions with no agreed right answer to test against
- Data you can't let the system read, even inside your own cloud
- Processes that change every few weeks
- Work where a wrong answer can't be caught before it causes harm
How the pilot runs. The bar is set before the build.
The baseline and the test set are agreed before any build work, so the go-live decision isn't a matter of opinion.
Baseline the workflow
Volume, time per task and error rate today. The pilot is judged against it.
Agree the test set
Real examples with expected outcomes, signed off by your process owner.
Build with validation
All four validation layers, least-privilege access and logging from the first version.
Test and review
We run the test set and go through every failure with you, then fix it or fence it off.
Go or no-go
Live only if the agreed thresholds are met. Otherwise, a written reason and what it would take.
Agents that read, check and act within limits. They retrieve from your documents and databases, call approved tools through APIs or MCP connectors, and hand anything uncertain to a person.
We build on the platform you already use, including OCI Generative AI and Select AI on Oracle Autonomous Database, Azure and AWS AI services, or open-weight models when data must stay in your environment.
The working agent
Deployed in your environment, on your data.
Test set and test report
What was tested, what passed and what was fenced off.
Guardrails and thresholds
The confidence levels that decide when a person reviews.
Logging and traces
Every answer and action traceable after the fact.
Runbook and rollback
Plus a handover session for your team.
Cost per task
What the agent costs to run at your volumes.
Risk and system documentation
Drafts your compliance team can build on.
Malaysia's proposed AI Governance Bill would put duties on companies that deploy AI, not only on those that build it. The pilot's documentation is designed to cover what the consultation paper lists.
See the mapping