Every order and payout, reconciled automatically
Orders flowed in from several delivery aggregators alongside the chain's own POS, and across multiple stores there was no reliable way to confirm that what the POS registered matched what the platforms reported and paid out. FireAI built a reconciliation engine on top of the existing data that made every order, every variance, and every discrepancy visible across all stores and all platforms, without the manual effort it used to take.
- POS ↔ platform
- orders matched automatically
- Every store
- in one consolidated view
- Missing orders
- and variances flagged
- Dispute-ready
- evidence for aggregators
Trusted by 200+ orgs to boost business insights.
A multi-outlet food chain in the UAE takes orders through its own POS and several third-party delivery platforms, with no reliable way to confirm that POS records matched what the platforms reported and paid out. FireAI built a reconciliation engine that matches POS orders against platform data automatically across every store, flags missing orders and amount variances, and produces dispute-ready reports, with an AI agent answering questions about any store, platform, or period instantly.
Orders and payouts that did not always match, with no way to see the gaps
With orders flowing in from multiple aggregator platforms alongside the chain's own POS, there was no reliable way to confirm that what was registered on their end matched what the platforms were reporting and paying out. Orders recorded in the POS were sometimes missing entirely from platform data, and payout amounts did not always match what the POS had registered.
With multiple stores running simultaneously, there was no consolidated view of where the gaps were, how large they were, or which platform they were coming from. Raising a discrepancy with an aggregator required clear evidence, and building that case manually was a significant effort every single time.
- No reliable way to confirm POS records matched what platforms reported and paid out
- Orders recorded in the POS were sometimes missing entirely from platform data
- Payout amounts did not always match what the POS had registered
- No consolidated view across stores of where the gaps were, how large, or which platform
- Building the evidence to raise a discrepancy with an aggregator was a manual effort every time
What reconciliation used to take
Confirming orders and payouts, and disputing the gaps, was a manual, per-store, per-platform effort with no shared view underneath it.
Chasing discrepancies by hand
Mismatches between POS records and platform reports were tracked down manually, store by store and platform by platform, with no single place that showed where the gaps were.
No consolidated view
Because each store and each platform was looked at on its own, there was no way to see across all of them at once how orders and payouts were tracking.
Disputes built from scratch each time
Raising a discrepancy with an aggregator needed clear evidence, and assembling that case, the missing orders and the amount variances, was rebuilt manually every single time.
One reconciliation layer across every store and platform
FireAI brought all of the chain's order and payout data into a single reconciliation layer, making mismatches visible, traceable, and easy to act on across every store and every platform.
Every order now sits alongside its reconciliation status, and every mismatch is caught and documented automatically. Teams can ask what is happening with a specific store, platform, or period and get a complete, structured answer instead of building the analysis themselves.
Reconciliation engine
POS orders are matched against platform data automatically, flagging every missing order and every amount variance, so nothing slips through unnoticed.
AI agent for instant answers
Teams ask a simple question about a specific store, platform, or time period and get a complete, structured answer instantly, without manual digging or spreadsheet comparisons.
Complete order listings
A detailed view shows every order alongside its reconciliation status, so teams always know exactly what is matched, what is missing, and what needs to be followed up on.
Dispute-ready reports
Auxiliary reports are generated with everything needed to approach an aggregator with confidence: clear summaries of missing orders and amount variances, backed by the data and ready to use.
Why they chose FireAI
- The reconciliation engine was built on top of the existing POS and platform data, with no re-platforming
- Every mismatch is caught and documented automatically, across every store and every platform at once
- The AI agent turns a question about a store, platform, or period into a structured answer in seconds
From chasing discrepancies to catching them automatically
Reported as the capability delivered rather than as recovery or financial figures.
The chain went from manually chasing discrepancies across stores and platforms to having every mismatch caught, documented, and traceable automatically. Across all locations, leadership had a single consolidated view of how orders and payouts were tracking, updated and ready whenever it was needed. The conversations that had to happen with aggregators were now backed by complete, well-organised data from the start, making follow-ups faster, cleaner, and far more effective. What made the biggest difference was the AI intelligence built into the process: teams could simply ask what was happening across a store, a platform, or a specific period and get a clear, structured answer grounded in their own data, without anyone building the analysis for them. Investigation that used to take days was now a question away.
How it came together
Built on the existing POS and platform dataThe reconciliation layer was built on top of the chain's existing order and payout data rather than requiring new systems, so the focus was on matching logic and surfacing the gaps clearly.
- 1
Order and payout data, unified
POS records and the data from each delivery platform were brought into one reconciliation layer spanning every store, as the foundation for matching.
- 2
Automatic matching, with variances flagged
The engine matches each POS order against platform data, flagging orders missing from a platform and amounts that do not reconcile with what the POS registered.
- 3
An AI agent over the data
A plain-language agent was layered on top, so a question about a store, platform, or period returns a complete, structured answer without manual digging.
- 4
Dispute reports, generated
Reports were set up to summarise missing orders and amount variances per platform, assembled and ready to take to an aggregator.
Key takeaways
- Multi-platform reconciliation is a matching problem across POS and several aggregators, not a per-store spreadsheet exercise
- FireAI matched POS orders against platform data automatically, flagging every missing order and amount variance
- A single consolidated view showed how orders and payouts tracked across every store and platform
- Dispute-ready reports turned aggregator follow-ups from a manual rebuild into a ready-to-use case
Who should consider FireAI?
Multi-outlet F&B and retail chains taking orders through their own POS plus several third-party delivery platforms, where confirming that platform reports and payouts match the POS, and disputing the gaps, is a manual effort across every store.
Frequently asked questions
See every order and payout reconciled, across every platform
If you take orders through your own POS and several delivery aggregators, and confirming the payouts means chasing discrepancies by hand, FireAI can match them automatically across every store and hand you dispute-ready evidence. Book a demo on your own data.
Want results like F&B / Multi-outlet retail?
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