Built and running today

Systems we've built and run.

Eight real systems in daily use. Clients, brands and buyers stay private, so the screens below are drawn with dummy data. The scale figures are real.

01 · Stock & replenishment

Daily reorder plans for four brand accounts

A multi-brand group selling on Amazon as seller and vendor, plus quick-commerce.

Before
Reorder quantities were worked out by one person in a spreadsheet, with no record of who approved what.
What we built
A platform that turns sales speed, stock and inbound into a daily plan per product and fulfilment centre, with SOP rules and approve-before-order.
What changed
Planners adjust, approvers sign off in the app, and the warehouse gets a clean order. Old data is flagged before anyone plans on it.
19 modules23 screensrole-based access
Stock & replenishment: how we build it
reorder plan · week 39
Sales basis: last 12 weeksTarget cover: 8 weeksStock data 2 h old
Send backApprove & e-mail PO
SKUProductSold / wkOn handInboundCoverOrder qtyStatus
SKU-1042Wireless earbuds, black184310—1.7 wk1150proposed
SKU-1107Soundbar 2.1422601209 wk0no order
SKU-2210Smartwatch strap, 22 mm961404005.6 wk220proposed
SKU-3018Neckband, blue7135—0.5 wk540urgent
SKU-3301Party speaker 40 W1895—5.3 wk50approved

Calculation used: (8 wks × sold/wk) − on hand − inbound, rounded to carton size. Every change is logged.

Illustration · dummy data

02 · Orders & POs

One queue for purchase orders from five buyer channels

A brand group supplying marketplaces and quick-commerce buyers.

Before
Every buyer sent a different file. Stock was checked by hand, so a unit could be promised twice.
What we built
An order system that reads every buyer's PO, checks it against stock, reserves it and sends it to the warehouse; vendor POs checked against the portal.
What changed
Operations work from one queue instead of five inboxes. A new buyer takes a settings file, not a project.
16 modules5 buyer channels25 users
Orders & POs: how we build it
purchase orders · today
5 file formats read automaticallyNo stock promised twice
BuyerPOLinesValueFill-rateStatus
Quick-commerce APO-8821342₹3.84 L
96%
stock reserved
Marketplace 1PPO-4471-19118₹11.2 L
81%
14 lines short
Quick-commerce BPO-1059227₹1.96 L
100%
sent to warehouse
Retail chainPO-2025-33164₹5.40 L
88%
stock reserved
Quick-commerce CPO-7701219₹1.12 L
63%
hold: below 70%

Illustration · dummy data

03 · Reporting & MIS

A weekly business dashboard that builds itself

A multi-brand online seller reporting across several channels.

Before
Reports were downloaded by hand from seven logins and pasted into a master workbook every Monday.
What we built
A pipeline that pulls every account, cleans and combines the data, runs 24+ checks and publishes before the team arrives.
What changed
The team starts the week reading numbers, not building them. If a check fails, nothing goes out.
7 account logins24+ checksruns unattended
Reporting & MIS: how we build it
weekly-review · week 38
24 / 24 checks passed7 accounts · 4 channelsvs last week

Net sales

₹48.6 L

▲ 8.2%

Ad spend

₹6.1 L

▼ 3.4%

Ad cost of sales

12.6%

▼ 1.9%

Units

9,412

▲ 5.7%
BrandChannelSalesWoWCover (wks)
Brand AAmazon 3P₹21.4 L▲ 11.2%6.1
Brand AAmazon 1P₹9.8 L▼ 4.1%3.2
Brand BFlipkart₹7.2 L▲ 14.8%5.4
Brand BQuick-commerce₹6.3 L▲ 22.5%1.8 · reorder
Brand COwn store₹3.9 L▼ 2.2%8.9

Net sales, last 12 weeks

What needs a decision: Brand B on quick-commerce is growing 22% a week with 1.8 weeks of stock. Reorder before Thursday.

Illustration · dummy data

04 · Reconciliation

Marketplace settlements matched to the rupee

A brand selling on Amazon with a finance team closing every month.

Before
Settlement and tax reports were matched by hand, with a paid tool doing part of the job.
What we built
An accounting module that matches settlement and tax reports to orders every day, read-only.
What changed
Matching runs daily instead of at month-end, and it replaced the paid tool.
read-onlydaily reconciliation
Reconciliation & margin: how we build it
integrity checks · run 06:37
Publish blocked. Check #14 failed: Brand C sales from ads do not match the sales report (₹31,250 apart, tolerance ₹1). Nothing went out. The owner was told at 06:41.
BrandSales reportAds reportDifferenceCheck #14
Brand A₹31,24,880₹31,24,880₹0pass
Brand B₹13,51,420₹13,51,421₹1pass
Brand C₹3,92,300₹3,61,050₹31,250fail · blocks publish
Row counts vs last run
Week boundaries
Tax treatment 3P vs 1P
Totals vs source files

Illustration · dummy data

05 · Customer support

A customer case desk with WhatsApp updates

A consumer brand handling warranty and service complaints.

Before
Complaints were tracked across inboxes and spreadsheets, and customers chased for updates.
What we built
A case desk where every case has an owner and a deadline, with brand-scoped access, global search and WhatsApp updates.
What changed
Every case has a name against it, and customers hear back without calling.
live in 2 weeksbrand-scoped accessglobal search
Customer support & WhatsApp: how we build it
case desk

Open

38

Escalated

4

Waiting on customer

11

Resolved this week

126

CaseSubjectOwnerAgeStatus
C-20931Replacement not receivedTeam East3 hnew
C-20914Warranty claim, earbudsAsha1 dwaiting on customer
C-20887Wrong item deliveredRohit2 descalated
C-20860Refund statusTeam West2 dopen

Illustration · dummy data

06 · Dashboards & alerts

Ad analytics with approvals

A multi-brand seller running ads on several accounts.

Before
Campaign reports were exported into pivot tables, and problems were spotted days late.
What we built
Spend tracked against results with alerts and rule-based actions; a person approves anything that spends money.
What changed
Problems are seen the same day, and no rule spends money without a person saying yes.
multi-brandrules + alertsapproval before spend
Dashboards & alerts: how we build it

07 · Listings & catalogue

Listing and content checks against the master list

A brand with a large range across marketplaces.

Before
Someone clicked through product pages by hand, so most of the range was never checked.
What we built
Daily checks of live product pages and attributes against the master list.
What changed
Problems are flagged before they cost sales, and the team fixes a ranked list instead of hunting.
daily checksmaster-list diff
Listings & catalogue: how we build it

08 · Marketplace intelligence

Shelf and availability tracking on quick-commerce

A brand selling through quick-commerce apps.

Before
Nobody could see where products were missing from the shelf or how visible they were.
What we built
Tracking of shelf presence, stock availability and share of voice on quick-commerce.
What changed
Gaps on the shelf are visible the same day, by location.
shelf + OSA + SOVquick-commerce
Dashboards & alerts: how we build it

Also running: per-product margin engines, buy-box tracking, data-health audits and a control console for all the scheduled jobs.

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