Inventory Discrepancy

The system says you have it. The shelf says otherwise.

We encode your match rules against the stock data you already keep, so reconciliation runs itself and every unresolved mismatch lands with its evidence.

  • AI implementation services
  • Your match rules, encoded
  • The reconciliation runs itself
  • Only unresolved mismatches reach a person

Built with the leading AI platforms

NVIDIAAnthropic
Peter Enestrom, founder of Zaigo

Led by Peter Enestrom and the Zaigo AI & engineering team

YaleColumbia UniversityMicrosoft

The weekly hunt

The ERP says one number. The 3PL portal says another.

From operators we have sat with: mid-market manufacturers, distributors, and multi-location retailers between $100M and $500M — one ERP plus 3PLs, terminals, or branches, reconciled by hand. Not shopping for software; mid-audit or mid-month-end.

  • The weekly VLOOKUP

    “Weekly stock comparison between the 3PL warehouse system and SAP, done by hand in Excel with VLOOKUPs” — a reconciliation required for audit compliance, currently manual.

  • Two formats, married by hand

    One 3PL sends stock as a weekly emailed Excel report; the other has an online portal — two formats to marry before any comparison can run.

  • Blank location, quantity on hand

    “Blank location with quantity on hand — someone has to go look.” The system says the item exists; a person hunts the aisle before any rule can run.

  • The stock position is a spreadsheet

    “Product on hand spreadsheet.” No live view across terminals and suppliers, so every circulated copy is already wrong.

  • 374 warehouses, nothing ties out

    “374 active warehouses including technician vehicles.” Parts scattered across branches and tech cars — duplicate shipments because no record ties out.

  • Parts used but never billed

    “Parts leakage — parts used but never billed; no real-time visibility.” Blind-received items surface as discrepancies weeks later, past the freight-claim window.

What manual costs

The weekly hunt costs more than the inventory adjustment.

What the manual way looks like at an operator marrying the ERP, the 3PL portal, and the shelf by hand. Your numbers will differ — the discrepancy audit puts figures on yours before anything gets built.

WeeklyThe hand-run stock comparison between 3PL and ERP — required for audit, not optional
Month-endWhen a discrepancy is found the manual way — instead of the day it happens, by rule
374Active warehouses, technician vehicles included, at one equipment dealer with no record that ties out

From anonymized engagements — mid-market manufacturers, distributors, and multi-location retailers, $100–500M in revenue

How it works

We encode your exceptions.

No new system for your team to learn. The match rules your best person carries in their head become the rules every export runs against.

  1. 01

    Trace one month of mismatches

    One warehouse/3PL pair, one month: every mismatch traced to its cause — receiving cutoffs, UOM conversions, emailed reports, transfer lag, blank locations.

    First pair
  2. 02

    Encode the match rules

    Tolerances, identifiers, unit-of-measure conversions, lot traceability — which system wins when two disagree, and what needs human approval before an inventory adjustment posts.

    Per system pair
  3. 03

    Reconcile by exception

    The rules marry the exports every cycle; your people see only the discrepancies the rules can’t resolve, each with its evidence attached.

    Every cycle
Not another tool

A guide can list the causes. It can’t find yours.

A guide can list the causes. It can’t find yours. The software holds the stock ledger — it doesn’t hold your match rules, your tolerances, or which system wins when two disagree. We encode those.

Our AI does the reading — every 3PL export, emailed stock report, and ERP ledger line — against your encoded rules. Your rules do the judging; your people see only what the rules can’t reconcile.

Peter Enestrom, founder of Zaigo
Who builds it

Led by Peter Enestrom.

Co-Founder — leads AI & Engineering

Pete Enestrom

Every engagement is led personally by Pete, working with the Zaigo AI & engineering team from the two-week audit through the production handover. The person who scopes the work is the person who builds it.

Education
Yale & ColumbiaGraduate
Background
Microsoft & IntelFormer
Experience
Exited FounderVenture-Backed

Background

Questions

Asked by inventory managers and controllers.

The straight answers, before you book anything.

What’s holding your business back?

A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.

Talk to Zaigo