AI Agents & Automation

Most teams can see where AI could help, but getting from a promising demo to something people rely on every day is hard.

• Prototypes work on the happy path and fail on real data.
• Nobody is sure how to keep answers accurate, private and auditable.
• The tool sits beside the real systems instead of working inside them, so people copy and paste between the two.

1. Find the right work

We look at where your team loses time today and pick the tasks where automation is both valuable and safe. Some are a fit for AI, some just need a well-built workflow.

2. Prove it on real data

We build a working version against your own data and systems, measure how well it does, and decide together whether it's ready to go further.

3. Build it properly

The ones that prove themselves get engineered for production: access control, monitoring, human review where it matters, and clear ownership.

• AI agents and copilots that answer questions from your documents and data, draft work, or take actions in your systems with the right permissions.
• Workflow automation that connects your tools and removes manual hand-offs, with or without AI.
• Document processing: extracting, classifying and checking information from emails, forms and files.
• Integration with the systems you already use, through their APIs, so automation works inside your processes.
• Evaluation and guardrails: test sets, quality checks and human approval steps so you know how the system behaves before it goes live.
• Running it: monitoring, cost tracking and improvements after launch.

• A ranked shortlist of use cases, with the value and risk of each.
• A working prototype on your own data, with a measured view of how well it performs.
• A production build with access control, logging and human review where it matters.
• A test set you can re-run whenever the model, prompt or data changes.
• Monitoring of quality, usage and cost after launch.
• Documentation and handover, so your team can run and extend it.

We usually start with a short discovery to choose the use case, followed by a time-boxed prototype on your data. If it proves itself, we build it for production as a project or with an embedded team. We agree the scope, team and pricing model with you before work starts.

Senior engineers with experience in software, data and AI work on your project directly. The same people who build the prototype build the production version.

Our team works from Ho Chi Minh City (UTC+7). That's within an hour of Singapore and Malaysia, and gives four to five hours of shared working time with the Australian east coast every day. For clients in the US we agree an overlap window at the start and handle the rest asynchronously, with written updates.