Nobody’s checking your systems still line up.
Integration tools move data between Shopify, your OMS, the WMS, and the processor — then call the job done. Darpan keeps checking they still line up afterward, row by row, while there’s still time to act.
The stack got specialized — and every tool is a new seam.
Retailers traded the monolithic suite for best-of-breed point tools, each excellent at one job. Gartner projects ≥70% of organizations will be mandated to acquire composable technology by 2026, up from 50% in 2023. The trend creates the problem: the more tools you connect, the more seams there are for data to drift through.
Integration platforms scale the number of connections — not the assurance the connected systems still reconcile.
Every specialized tool is a new seam.
Five best-of-breed apps have far more places to disagree than one suite. Going composable multiplies the surfaces where data drifts.
Legacy reconciliation was built for the monolith.
The incumbents assume a finance-centric, suite-or-ERP world and a retrospective close. They were never designed to continuously verify a dozen independent operational systems.
Darpan is a specialized tool too — not a new monolith.
We are the one specialized job nobody else owns. We don’t replace your tools; we make the stack trustworthy.
“Isn’t this my integration platform’s job?”
A successful sync moves a record from A to B.
Integration platforms are built to transfer a record and confirm the transfer succeeded. That is the guarantee they make.
A correct check proves A and B still line up afterward.
Across timing lags, partial syncs, retries, schema mismatches, and one-off exceptions. A different guarantee entirely.
Drift accumulates between successful syncs — exactly where nobody is looking. The integration ran fine; the numbers are still wrong.
The cost of systems not lining up is large, real, and measured.
of inventory records were inaccurate in a study of ~370,000 records across 37 stores. Operational data is chronically wrong — and operations owns it.
DeHoratius & Raman, Management Science (2008); Auburn RFID Lab field work lands in the same range.of global inventory distortion attributed to “data disconnects and systems that are not integrated” — its own root-cause category.
IHL Group / OrderDynamics (2015). Vendor-sponsored and dated — directional, not primary.Splitting a marketplace payout back into sales, fees, and refunds is the single largest source of material misstatement in ecommerce financials.
EcomCPA (2026), practitioner account.We cite where each figure comes from, and flag what’s sponsored or dated. The verification layer verifies its own claims too.
One specialized job. Nobody else owns it.
Map each system once.
The fields, the types, the keys that anchor a record. No six-week implementation — onboard a new system in days.
Every record paired, every mismatch surfaced with the rows behind it.
Drift, missing objects, and resolved pairs out of one continuous run, with the source line attached to every call.
Cause and fix — not a pile of unmatched items.
A discrepancy is named and explained while it’s still cheap to act on — before it’s a stockout or a leak, not at month-end.
AI-native is the reason we can do this — never the pitch. We demonstrate the capability and let the work speak.
You decide when your systems line up. The engine enforces it, every run.
Darpan matches records by the keys you name, within the tolerances you set, across formats that never quite line up. Rules are declarative — readable, versioned, changed in minutes, not a six-week re-implementation. A new system or a messy field shows up, you adjust a rule, not rewrite an integration.
Match
Pair records by the keys you name — order id, SKU, payout — across systems that label them differently.
Tolerate
Define what counts as lined up: exact, within ±n, inside a date window. Drift is anything outside it.
Normalize
Trim prefixes, fix casing, convert currency and units — so messy, novel retail data still lines up.
Resolve
When a rule fails, name the cause and propose the fix — not just a pile of unmatched items.
Built for the operator. Reassuring to finance.
Trust the numbers now, in time to act.
Operations makes daily decisions — reorder, chase a 3PL discrepancy, catch leakage this week — on numbers that have to be right today. The lag finance tolerates is intolerable here.
- Problem
- Systems disagree; decisions made on numbers nobody trusts.
- Why buy
- Stop leakage and stockouts before they cost money.
A cleaner, audit-ready close, as a byproduct.
Anything touching money keeps finance in the room. Darpan feeds a faster, cleaner close with the evidence behind every line — without threatening anyone’s controls.
- Problem
- The close is slow and manual.
- Why buy
- Less manual effort, fewer errors, a clean audit trail.
Keep your best-of-breed tools. Add the layer that proves they line up.
Don’t go back to one system that does everything okay-ish. Add the one specialized job nobody else owns: continuous proof your systems still reconcile.
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