AI agents built for retail at Malaysian volume

workflow: mapped

What retail ops in Malaysia actually looks like

Stock truth is split across a POS at each outlet, three marketplace seller centres, and an AutoCount or SQL Account ERP at HQ — and they rarely agree. Customers ask about price and availability on WhatsApp at 11pm. Dealers send purchase orders as photos of handwritten forms and half-formatted Excel files. Finance downloads settlement reports from Shopee, Lazada and TikTok Shop, then reconciles them against bank statements line by line. Every one of these touchpoints is a queue of structured, repetitive work — which is precisely the shape of work agents handle well, and precisely where Klang Valley retail teams are bleeding hours.

agents: matched

The four agents that fit this industry

We don't sell a platform; we ship the specific workflow that hurts. For retail and distribution that is almost always one of four: a product-enquiry agent grounded in your catalogue, price list and outlet stock; a support-triage agent that classifies and drafts before a human ever reads the ticket; an order-intake agent that turns dealer POs and marketplace orders into clean ERP entries; and a cash-application agent that matches settlement files and franchisee payments to bank credits. Each is a documented pattern with a worked example, model-cost estimate in RM, and an evaluation scorecard — linked below.

• Product enquiry: catalogue + POS grounding, multilingual replies
• Support triage: classify, draft, escalate only real exceptions
• Order intake: PO photo or marketplace export to ERP entry
• Cash application: settlement report to bank credit, matched

data: contained

Customer data stays yours — PDPA by design

Retail agents see names, phone numbers, addresses and purchase history, so we design for the Personal Data Protection Act from the first architecture diagram. The agent receives only the fields the task needs — an enquiry agent gets catalogue data, not customer records; a triage agent gets the ticket, not the CRM. Model calls run under commercial API terms where your data is not used for training. Access is logged, retention is explicit, and cross-border processing is documented so your DPO can answer an audit question with a diagram instead of a guess.

evals: enforced

Evaluated before launch, load-tested before 11.11

A demo that answers ten questions nicely is not a production system. Every build passes an Agent GPA evaluation before go-live: a scored test set built from your real enquiries, POs and settlement files, with explicit thresholds for accuracy, refusal behaviour and escalation. Then we load-test it, because retail traffic is not flat — 11.11 and payday weekends will find whatever breaks. That discipline comes from operating experience: 300+ production incidents handled, 50+ load-test audits, 30+ resilience assessments. Your agent inherits all of it.

start: scoped

How an engagement starts

Bring one workflow — the WhatsApp enquiry queue, the CS backlog, dealer POs, or settlement reconciliation. In a scoping call we map the actual flow, name the systems involved, and give you a written estimate: build cost and monthly model cost, both in RM. If an agent is the wrong tool for your case, we say so on that call. From signed scope, most single-workflow builds reach a production pilot in six to ten weeks, running against live traffic with your team reviewing the agent's work before it goes fully autonomous.