Finance & Reconciliation

Cash on delivery (COD) means the customer pays the courier in cash; your logistics partner later remits that money to you, often net of their fee and after deducting failed or returned orders. The pain is matching thousands of delivered orders to bank credits that arrive in batches, sometimes with vague references or partial settlements.

FireAI ingests courier remittance files, order-level delivery status from your OMS, and bank statements. For each order it tracks: expected COD amount, actual remittance date, deductions (RTO, partial returns, courier charges), and variance. Exceptions — orders marked delivered but not remitted, or amounts that don't match after agreed rules — surface in exception queues instead of buried in Excel.

What you can ask FireAI:

• "Show COD orders from last week where remittance is still pending beyond SLA"
• "Which courier hubs have the highest variance between expected and remitted COD?"
• "List orders delivered in March but settled in April — impact on month close"

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Marketplaces charge referral fees, closing fees, shipping fees, and ad-hoc adjustments — and fee structures change by category and season. It is easy for a rate to be applied incorrectly for a subset of SKUs, or for a promotional fee waiver to stop applying without anyone noticing.

FireAI loads your contracted fee schedules and compares them to every line on marketplace settlement reports. The system flags orders where the charged commission, FBA/FBM fees, or tax-on-fee differ from what the contract implies — at SKU and order level. You get a ranked list of leakage by marketplace, category, and time window, with drill-down to example orders for dispute or credit notes.

What you can ask FireAI:

• "Which ASINs on Amazon had a higher referral fee than our category cap in Q1?"
• "Compare Flipkart settlement deductions vs our expected fee model this month"
• "Total estimated overcharge across all channels last 90 days"

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Revenue in your analytics tool is not the same as contribution margin per order. To know what really worked, you need revenue minus product cost, payment fees, marketplace commission, shipping, packaging, returns allocation, and sometimes influencer or affiliate cost — broken down by sales channel (D2C site, Amazon, Nykaa, etc.) and SKU.

FireAI builds an order-level economics model from connected data: COGS from your ERP or inventory system, shipping and RTO from carriers, fees from marketplace reports, and discounts from your promo engine. Each order gets a net contribution number; you can aggregate by channel, campaign, or SKU to see where you are subsidizing growth.

What you can ask FireAI:

• "Top 20 SKUs by negative contribution margin on Amazon last month"
• "D2C website vs Meta shop — average contribution per order for skincare"
• "Orders where discount + shipping cost exceeded gross margin"

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Unit economics ties marketing spend and customer value together: how much you pay to acquire a customer (CAC), how much gross profit they generate over their life (LTV), and how many months until you recover acquisition cost (payback). For D2C brands, these metrics drift with channel mix, iOS attribution changes, and cohort quality — so a static annual model is rarely enough.

FireAI combines ad platform spend, attributed orders, and repeat purchase behavior from your store and CRM. You see CAC by channel and campaign, LTV by cohort, and payback curves — with the ability to slice by product category or first product purchased. This is the language finance and growth can share when deciding whether to scale or fix a channel.

What you can ask FireAI:

• "CAC and LTV for Meta-acquired customers vs Google in Q4"
• "Payback months by cohort for customers who started on a subscription SKU"
• "Blended CAC trend vs new customer revenue — last 12 weeks"

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