Qeasy Smart Reconciliation · Running in production at large-scale e-commerce companies
Multi-platform statements, refunds, subsidies, commissions, shared expenses — every penny a platform pays you is automatically matched line by line against your own books. Whatever doesn't match comes with the business reason and a suggested action; whatever does flows straight into Kingdee, Yonyou or SAP. No more 'manual exports + row-by-row Excel checks' for your finance team.
If books are marked as matched while differences lack a business explanation, the issue is only hidden. Statements, orders, refunds, fees and internal books need line-by-line matching under shared rules.
At month-end, three people eyeball six platform statements against one supply-chain export, row by row
Statements from every platform are collected automatically and matched order by order — people only review what doesn't match
Platforms only give you the net amount after deductions — subsidies, coupons and compensation all lumped together
Every deduction type is itemized; each difference is auto-attributed to a concrete business reason with a suggested action
Refund overhang and cross-period settlement: money refunded before goods return, this period's cash for last period's orders
Refund-only, instant refund, FBA refund and cross-period income are auto-identified and attributed to the right accounting period
Shared expenses allocated by gut feel — nobody knows which SKU actually makes money
Shared expenses are auto-allocated to stores and SKUs, revealing true per-item profit after allocation
Reconciliation is only the starting point. Every reconciled datum feeds fine-grained accounting across five dimensions — store, SKU, gross profit, tax and cash — ready for business decisions.
Ten JD stores, twenty Douyin shops, three Amazon marketplaces — all unified into one financial view, with cross-store consolidation and combined statements in one pass.
Trace every order down to each item: per-SKU revenue, cost and allocated expense, with per-item profit precise to the cent — loss-making SKUs at a glance.
Seven deduction layers from GMV to net profit: net receipts → platform commission → service fees → ads → logistics → refund losses → shared expenses. Every layer evidenced.
Platform-withheld product tax, shipping tax and promo-rebate tax are separated automatically; cross-border multi-currency (USD / EUR / JPY), EU VAT and export rebates covered.
Sales collections, refund recoveries, platform rebates, procurement, logistics, commissions, taxes — four inflows and four outflows, finely modeled, with collection forecasts and close cadence under control.
From reconciled to posted: sales, refund, commission and adjustment vouchers generated automatically; provisional receivables pushed down to financial receivables; out-of-the-box integration with Kingdee, Yonyou and SAP.
Many platforms, one set of books
JD nets whole orders; Douyin matches in five rounds; Alipay has three statements (aggregate settlement, account flow, Yulibao); Amazon reconciles receipts and refunds separately; AliExpress has five bill types plus fully-managed JIT. Every platform's rules are built in as ready-to-use capabilities — new statement formats are adapted within days, so your finance team never has to study each platform's statement documentation again.
Visibility during campaign peaks
Before campaigns, pre-run rules, inspect data and simulate collections; afterwards, review results by task. The CFO cockpit brings together sales, margins and collection cycles, while alerts for failed matches and refund overhang help finance focus on exceptions.
AI finance assistant
'How many unreconciled differences are left on Douyin this month?' 'Adapt the new platform's statement format for me.' Business users state needs in natural language; the AI assistant reconnoiters, writes and tests on its own — humans confirm once before launch. When a platform changes its rules, you respond the same day instead of queueing for development.
Choose a complete settlement period and use manual review as the baseline. Validate ingestion, matching, variance handling and ERP posting on the same real statements before expanding to more platforms.
Finance, business and implementation teams should agree on acceptance criteria and retain sampled rows and review records.
One-click import from every platform — no manual exports, no missing statements
Three-way match across platform statements ↔ orders ↔ your books; matches pass automatically
Mismatches are auto-tagged with 23 business reasons, each with a suggested action
Shared expenses auto-allocated to stores and SKUs, writing back true profit
Results pushed to Kingdee / Yonyou / SAP in one click, vouchers auto-generated, fully audited
Supports 'daily clear, daily close': daily reconciliation, weekly margins, monthly P&L, quarterly balance sheet, annual tax settlement — the cadence follows your management needs, not just a month-end sprint.
23 built-in e-commerce variance reasons. Every unmatched line is no longer 'difference ¥245.04, handled manually' but a clear reason plus a clear suggested action — readable by new hires, acceptable to auditors.
Platform-funded coupons and instant discounts, auto-accepted within ¥0.01
Platform compensates the buyer after dispute rulings and deducts from you — auto-linked to the order
This period's refund against last period's order — income reversed in the right period
Refund overhang — money returned, goods not — flagged and tracked
Platform pays in installments — settled and pending amounts booked separately
Postage / price make-up orders identified, differences auto-billed as expense
Zero-price gift lines flagged, with a reminder to check for missed receivables
Product, shipping and promo-rebate taxes itemized separately
Finance states needs in plain language; the assistant reconnoiters, writes and tests on its own — humans confirm once before launch. Four assistants, each with its own job, dispatched from one entry point.
bill-parse-agent
When a statement arrives in a new platform or format, describe it and the assistant learns to read it — validated before it goes live.
reconcile-script-agent
Runs line-by-line matching under your rules, explains every mismatch in business terms, and verifies results before you rely on them.
expense-allocate-agent
Allocates shared expenses to stores and SKUs under your rules, writes back true per-item profit, with verifiable results.
general-assistant
'How many unreconciled differences on Douyin last month?' Ask in plain language — it queries, delegates and answers from one entry point.
The screens below come from the real system running in production (demo account) — what you see is what you get.
Domestic coverage spans JD POP, Douyin Shop, Pinduoduo, Xiaohongshu, WeChat Channels, VIP.com and the Alipay funds system; cross-border coverage spans Amazon, Temu, Shopee, Lazada, eBay, AliExpress, Shopify storefronts and Newegg. Every platform's reconciliation rules are built in, with new platforms onboarding continuously.
JD POP
Douyin Shop
Alipay
Amazon
Pinduoduo
Xiaohongshu
WeChat Channels
VIP.com
Temu
Shopee
Lazada
eBay
AliExpress
Shopify
NeweggPost results in one click: provisional receivables pushed down to financial receivables, expense variances auto-billed as payables, vouchers generated under your chart of accounts. Kingdee Cosmic receivables can join reconciliation at line level — a closed loop across platform statements ↔ business orders ↔ ERP accounting, with no manual data shuttling between systems.
Continuously updated from the production frontline: multi-platform reconciliation manuals, variance-handling playbooks, fine-grained accounting methods, cross-border specials and customer cases.
某年 GMV 9.6 亿的运动品牌在天猫、抖音、京东、唯品会、得物 5 个平台经营 16 个店铺,SKU 1 800+,直营门店 240 家、加盟店 380 家。2025 年 9 月之前,新招的财务专员平均需要 6 个月才能独立完成月结对账;门店新店长的 ERP 操作上手期平均 11 周;老员工离职时带走的不是「人」,是 5 年的对账经验与门店运营手感。2025 年 10 月上线 RAG 知识库后,**财务新人独立上岗周期从 6 个月压到 11 周(−46%)、门店新店长 ERP 上手从 11 周压到 4 周(−64%)、老员工经验沉淀覆盖率从 18% 提升到 87%**。本文还原这家运动品牌如何把「老员工脑子里的经验」沉淀成「企业可调用的 AI 知识资产」,以及这套机制背后 5 个反直觉的发现。
京东 POP 对账完成后，账单上的每一笔订单最终都要落到金蝶。但金蝶不是直接"Save 一张财务应收单"——本账套是暂估模式，财务应收单只能由暂估应收单通过 Push 下推生成。本文讲透这条最容易踩坑的链路：合并出单 v1.7.0 的口径翻转（G1 缺口消解）、推送字段完整性契约、Push 协议本身的硬约束、状态机回写，以及 2026-07 实测的 45 张单如何与手工 Excel 逐分录对平。
电商企业的「利润黑洞」从来不在 GMV 上，而在 5 大成本项的分摊颗粒度上。商品成本、物流成本、平台佣金、广告费、运营成本——每一项都有 3 套分摊维度（按 SKU / 按订单 / 按店铺）、2 套分母口径（净额 / 绝对值）、1 套反直觉规则（推广费退货不冲减分母）。本文按 5 大成本项逐项拆：每项给出「业务口径 + 分摊维度 + 反直觉点 + 落地踩坑」，并用一份 38 店 × 1200 SKU 的真实口径，说明 CFO 月底为什么「账面赚 18%、口袋只有 11%」。
一家年 GMV 14 亿元的多平台电商，2026 年 Q1 利润表净利润 6800 万元，账上现金却只剩 1200 万元——CFO 在 4 月 28 日经营分析会上被问到「利润去哪了」时哑口无言。复盘发现 4 大问题里有 3 个出在「现金流预测」环节。本文给出 4 大流入 + 4 大流出的精细化建模方法论：销售回款 × 退款回收 × 平台返利 × 其他流入 / 采购付款 × 物流费 × 平台扣点 × 税费，每一条都按平台 × 店铺 × 月份 × 周 4 级颗粒度建模。
已经把京东 POP 账户流水上传到对账系统、解析也跑完了，下一步「对账」应该怎么点？本文用 5 步走完新用户的第一个收入对账计划——从「找到入口」到「整体确认 + 推送转换单据」，每个步骤都对应一个真实的按钮、一段可见的状态变化。读完这篇，你就能把一张 600 单的京东 POP 账户流水从「解析完成」变成「对账成功、推送到金蝶」，并掌握 7 态状态机、23 个 diffReason 业务标签码、整体确认与转换单据的协作方式。
作为 CTO，你不必像财务经理那样「一步一步操作对账」，也不必像 SRE 那样「凌晨 3 点盯监控」。但你必须能回答 5 个问题：① 怎么部署上线 ② 怎么看出系统健康度 ③ 性能基线是什么 ④ 安全防御有几道闸 ⑤ AI 知识怎么维护。本文把 5 件事拆成 5 大常用操作，每步配真实架构图与 B 级干货。
Your ERP keeps your internal books, but platform statements live outside it — how much commission was deducted, which coupons were funded, how much tax was withheld. The platform reconciles platform statements, business orders and ERP entries three-way, then pushes results back into the ERP for posting. It doesn't replace your ERP; it covers the part your ERP can't see.
Fifteen platforms are supported: JD POP, Douyin Shop, Alipay (Tmall), Pinduoduo, Xiaohongshu, WeChat Channels, VIP.com, Amazon, Temu, Shopee, Lazada, eBay, AliExpress, Shopify and Newegg. For other platforms, share sample statements and API details so the team can assess adaptation scope and timing.
Adaptation rules are visual and version-managed. The AI assistant can help adjust them, with validation before activation. Changes are recorded so business and implementation teams can review and roll back.
The AI assistant works through controlled paths: validation runs in an isolated environment, execution requires human confirmation, operations are audited, and query, authoring, testing and execution permissions are separated. AI proposes; humans decide.
Start with one platform and a complete settlement period. Validate statement ingestion, matching and variance attribution before connecting ERP posting and expanding. Compare manual and system results, variance handling and processing time on the same statements; timing depends on data readiness and scope.
Our core product Qeasy focuses on enterprise data integration and application building. This smart reconciliation platform is Qeasy's flagship enterprise AI deployment, serving real financial reconciliation at large-scale e-commerce companies — not a proof of concept, but a pipeline running in production every month, turning reconciliation from a month-end sprint into a daily business routine.
Monthly reconciliation pipelines running in production at large-scale e-commerce companies
15 platforms' rules built in; 23 variance reasons codified
Auditable steps, rollbackable versions, tests isolated from real books
New platforms and rules adapted fast; the AI assistant keeps lowering the bar
