Investment & partnership
The hard part of AI in Southeast Asia is delivery, not models
Anchor Sprint builds and runs production AI agents for businesses in Malaysia and Singapore. This page is for investors and partners who want to understand that business.
Where we sit
Both countries have national AI agendas and neither is short of models or cloud capacity. What is scarce is the layer in between — teams who can take a frontier model and turn it into something a finance department will let near a ledger.
That work needs integration with the systems these businesses already run, evaluation a risk function will accept, and someone on call in the same time zone when it misbehaves at month-end. It does not scale by hiring model researchers. It scales by having shipped enough production systems to know which parts fail.
What is already true
We operate in both markets rather than serving one from the other — Anchor Sprint Sdn Bhd in Malaysia and Anchor Sprint Pte. Ltd. in Singapore. Very few companies in this segment have both, and it is why a regional rollout is a normal engagement for us.
We are a member of the Anthropic Claude Partner Network with Claude Certified Architects on the team, hold Malaysia Digital status certified by MDEC, and are an HRD Corp approved training provider — the checks a procurement team runs before it signs.
Four products run in production today: EzyChat, EzyTeam, EzySQL and MySalesFlow. Alongside them, fourteen agent builds are published with their evaluation scorecards — cash application, invoice matching, bank reconciliation, contract review, support triage and more. Most of this field publishes outcomes. We publish the measurement.
An AI-native product ships in weeks, and not because a model writes the code. Most of a production agent is integration, permissions, an evaluation harness and operational plumbing — built enough times now to stop rediscovering them, on top of more than fifteen years of software and product delivery.
What we will not do
We turn work down. If a workflow needs no judgement — the same fields moving between two systems on a schedule — a conventional integration is cheaper and will not surprise anyone. If the source data is wrong today, an agent produces confidently wrong answers faster.
We do not train frontier models and have no plans to, and we would rather report a build that scored badly than quietly ship it. That costs revenue in the short term. It is also why clients stay, and why our scorecards are worth reading.
Ways to work together
Investment. If you back regional AI services and infrastructure businesses and want to understand ours, we are open to the conversation. The detail that belongs in a data room stays in one.
Product partnership. Distribution, joint builds, or embedding our agent stack in your platform.
Enterprise and government programmes. Both national AI agendas need delivery capacity more than another model. If you are assembling a panel or a programme in Malaysia or Singapore, that is squarely what we do.
More on the company is on about us, and the day-to-day work is on AI agent development.