Ad spend, stock and returns are watched in different tools, and a campaign can look profitable while quietly losing money after shipping and returns. The founder gets one view of true contribution margin by channel, with stock and fulfilment risks flagged before a sale, not after.
A worked example for a typical Malaysian Direct-to-Consumer Brand, not a description of any real company. Go through the six stages in order: paste each prompt into Claude, check the answer against how your business really works, then continue. Change the profile at the start to match yours.
Stage 1 of 6
Map how the operation really runs
Act as an operations consultant. I run a Direct-to-Consumer Brand in Malaysia. Profile: A Malaysian D2C brand selling across its own Shopify store, Shopee, Lazada and TikTok Shop — ~400 SKUs, ~RM 30M annual revenue, a small in-house team plus a 3PL.
Interview me, one question at a time, to map how decisions actually get made today. Cover: the recurring meetings and reports, who decides what, which numbers people trust, and where decisions are late or made on gut feel.
Known friction: Sales growth vs. Profit per order; Fast delivery vs. Shipping bill; Cash in stock vs. Empty shelves; Promotions vs. Brand image; Marketplaces vs. Own store.
After 8 to 10 questions, summarise the five decisions that cost us the most when made late, in a table: decision, current owner, current delay, cost of delay.
Stage 2 of 6
Design the specialist agents
Using the decision map above, design 5 specialist AI agents for our Direct-to-Consumer Brand: Ad Efficiency, Stock Placement, Packing Floor, Returns, Range.
For each agent give: the one question it answers every day, the data it reads, what it may recommend, what it must never do alone, and the format of its output.
Starting points for what each should watch:
- Ad Efficiency: ROAS and contribution margin by channel, creative fatigue, promo calendar
- Stock Placement: stock by node, ship-time promises, courier performance
- Packing Floor: pick/pack throughput, temp-labour needs before campaigns
- Returns: return reasons, review sentiment, refund exposure
- Range: SKU margin, slow movers, channel dependency
Keep every agent narrow. If an agent covers two jobs, split it.
Stage 3 of 6
Wire up data and compliance
For the agents above, list the data feeds needed. Likely systems: Shopify and marketplace seller centres; Ad platforms (Meta, TikTok, Google); 3PL and courier dashboards; Support inbox and review feeds.
Output a table per agent: field, source, how often it refreshes, data-quality risk, and what the agent should do if the feed is stale or missing. Mark every personal-data field and say how to minimise it.
Rules we operate under: PDPA 2010 for customer data, marketplace seller policies, consumer-protection rules on pricing and returns, SST where applicable.
End with a short list of actions that always need human sign-off.
Stage 4 of 6
Design the morning brief
Design the single daily brief that pulls the 5 agents together for the Founder, ready before the first meeting of the day.
Specify how the brief merges overlapping or conflicting recommendations, how it ranks items (impact, urgency, reversibility, confidence), and the exact fields per item: what to decide, recommended action, evidence, owner, deadline, and the cost of doing nothing.
Then write a realistic sample brief of five items. Items might resemble: Shift RM 8k/day from Meta prospecting to Shopee Ads — contribution margin is 11 points higher / Pre-position 600 units to the Penang node before the 11.11 surge / Pause the SKU with a 19% return rate pending a supplier check.
Stage 5 of 6
Build and automate it
Turn the design into a working automation for our Direct-to-Consumer Brand.
Needs: a scheduled daily run; the 5 agents run in parallel and each saves structured JSON; a final step reads them all and sends the brief by email or chat; every run is logged with inputs, outputs and cost.
Propose the build with Claude plus a workflow tool (for example n8n or the Claude Agent SDK). Give the steps in order, what happens when a data feed fails, and a human-approval step before anything that spends money or makes a customer commitment.
Stage 6 of 6
Roll out and measure
Write a rollout plan for the Direct-to-Consumer Brand agent team that starts small and earns trust.
Stage 1: one agent, advisory only, run alongside the current process. Stage 2: add the other agents. Stage 3: switch on the daily brief. Stage 4: let agents prepare low-risk actions for approval.
For each stage give duration, owners, what "ready to move on" means, and the metrics to track: time to decision, how often recommendations are accepted, hours saved, and errors caught. Close with the three biggest risks and how we contain them.
