Home / Case studies / AI Restaurant Invoice Reconciliation Automation in Food Delivery
Case study · Food service · Document AI & workflow automation
AI Restaurant Invoice Reconciliation Automation in Food Delivery
A multi-brand European restaurant group checked hundreds of delivery platform invoices by hand each cycle, with no restaurant invoice reconciliation automation. Brainy Neurals built an automation pipeline that uses browser automation & layout rules to download every invoice, read its figures & fill the sheets. Runs sign in to three delivery platforms monthly or on demand, replacing the logins & retyping the finance team once did. The finance team now reviews finished spreadsheets instead of building them, & a dashboard shows every run live.
3
Delivery platforms in one run
Monthly & on demand
When statements get reconciled
Per restaurant
Failed runs retry alone
Published September 2026 · Updated October 2026
Restaurant invoice reconciliation automation at a glance
What problem did this solve?
A restaurant group’s finance team lost days every month retyping hundreds of delivery platform invoices by hand. Duplicates & gaps still slipped through.
What did Brainy Neurals build?
Brainy Neurals built one pipeline that signs in to three delivery platforms & pulls every statement. The pipeline then extracts each figure & writes the reconciliation sheets on its own.
What changed after go-live?
Invoices now flow straight from the platforms to filled sheets with no one in the loop. The finance team reviews the output while a dashboard tracks every run.
Who else could use this?
Any business billed through a platform it doesn’t control can use the same pattern. Marketplace sellers, hotel groups, freight shippers & franchise networks reconcile the same way.
Engagement facts
| Fact | Detail |
|---|---|
| Industry | Food service |
| Sub-vertical | Multi-platform delivery |
| Client | European restaurant group |
| Engagement | Reconciliation automation build |
| Timeline | Not disclosed |
| Capabilities | Document AI & workflow automation |
| Delivery | Project-based delivery |
| Platforms | Three delivery platforms |
Why did reconciliation take days every month?
Reconciliation took days because a multi-brand European restaurant group sells through three delivery platforms, & each one bills its own way. Statements arrive as PDF invoices & CSV transaction reports, often packed into ZIP archives, across several billing cycles a month. Before any restaurant invoice reconciliation automation, the finance team pulled every file down by hand, the exact grind intelligent document processing exists to end.
Where it broke
- Every billing cycle meant another round of logins, one platform & one account at a time.
- Every invoice was opened & retyped, & each keystroke was a fresh chance to be wrong.
- Each restaurant needed its own analysis spreadsheet, so dozens of restaurants meant dozens of spreadsheets.
- Nobody could tell mid-month whether an invoice was missing until the totals refused to match.
None of this was unique to restaurants. A 2019 study of robotic process automation adopters found teams targeting work that is structured, repeated, rules-based & digital[1].
What gets tried before automation?
Four routes get tried before anyone builds automation, & each one is a reasonable place to start.
| Approach | What it gets right | Where it stops | Who it still suits |
|---|---|---|---|
| Keep it manual | Full control, no new tools | Days per month, growing | One brand on one platform |
| Platform dashboards & exports | Official data, always current | One silo per platform | Single-platform operators |
| Off-the-shelf invoice OCR | Strong on standard supplier invoices | Can’t log in & fetch files | Invoices that arrive by email |
| Platform-aware pipeline, our route | Collection through reconciliation in one run | Needs upkeep when portals change | Multi-brand groups at volume |
Every route starts with the same delivery invoices, yet only the automation reaches a finished monthly total.
Researchers describe this class of software as tools that drive another system’s screens the way a person would[2].
How we designed the reconciliation pipeline
Brainy Neurals built the reconciliation pipeline for a multi-brand European restaurant group as one straight line from portal to spreadsheet. A scripted browser collects the files & an extractor turns each invoice into rows. A builder then fills the sheets. The whole run works as workflow automation, with a browser standing in for the API the platforms never offered.
The first decision was where the files would come from, portal by portal. We ruled out waiting on exports or data feeds, because the portals are the only place every invoice reliably appears. So the pipeline drives each portal itself & holds its sessions open behind a managed proxy network.
The second decision was to fix the sheet structure before writing any code around it. Every row is keyed by restaurant & billing period, so a duplicate has nowhere to land. If one invoice is missing, the month is wrong, so completeness beat speed in every call we made.
If one invoice is missing, the month is wrong, so completeness beat speed in every call we made.
Everything between the platform portals & the finished sheets runs without a person in the loop.
Reading a PDF invoice is the easy half of document AI. Collecting every file across portals & billing cycles took most of the engineering.
Which tools run the reconciliation pipeline?
Each tool in the reconciliation pipeline replaces a step the finance team once ran by hand, & we kept the client inside familiar tools. The spreadsheet is older than the web, & month end still lives in one.
Collection
Portal automation
A scripted browser runs on every platform, since the portals are the only place every invoice shows up. Waiting on data feeds was the option we ruled out.
Login sessions
Saved sessions sit behind a proxy network, because fresh logins kept getting blocked. Signing in from scratch on every run was the old way.
Storage & extraction
File storage
Files go to cloud storage & the shared drive, so nothing ever sits on a laptop. A local downloads folder was the approach we replaced.
Invoice reading
Layout rules are tuned to each statement, because each platform’s layout stays fixed. We picked them over general-purpose OCR.
Duplicate control
Keys per restaurant & billing period mean a repeat invoice has nowhere to land. Nobody has to clean up after the import.
Reporting & operations
Reconciliation sheets
Results land in the team’s own spreadsheets, where finance already works every day. A brand-new reporting tool was never needed.
Analysis sheets
One formula template per store gives every invoice a clean sheet. Pivots no longer get rebuilt by hand.
Dashboard
A web app on a Python API shows every failure live. Log files that nobody reads are gone from the process.
How does one monthly run work?
One monthly run moves through six steps, start to finish, in the order the pipeline works them.
- The run starts from the dashboard or the monthly schedule. Each run is set to one account & a date range across its platforms.
- A scripted browser signs in to each platform with the saved session & opens the reporting section.
- The browser collects every invoice & transaction report for each restaurant in the billing range, archives included.
- Each file lands in object storage & the shared drive, filed by platform, account, restaurant & period.
- The extractor reads each invoice & writes commissions, payouts, taxes & fees into the account sheet.
- A builder fills one analysis sheet per store from the transaction data, & the dashboard logs the run.
Each restaurant is its own unit of work, so a failed one can retry alone.
Six steps run in a row, & the only human touch is the click that starts them.
What nearly stopped the pipeline?
Four problems nearly stopped the pipeline, & the platforms came first. They treated the automation as an intruder, the way they treat any unknown browser. Sessions expired mid-run, & some logins met walls a person never sees.
Then volume broke the first downloader outright. A run pulling hundreds of files could die at file two hundred & lose the lot.
Billing cycles overlap, & two cycles can bill the same restaurant twice. Early sheets carried duplicate rows & silent gaps where a cycle went missing.
For weeks, failure itself was invisible too. A run could stop at restaurant sixty with nobody knowing until a sheet came up short.
Extraction that breaks when a source changes is well-studied ground. One study watched 27 web wrappers for a year & logged 37 format changes among them[3]. Weeks like these are when a client brings in specialist engineers rather than learning it slowly.
How we fixed each problem
We fixed each problem with one change to how the pipeline runs, starting with the logins.
Sessions
We moved every login onto saved sessions, carried behind a managed proxy network. The browser now behaves like the same returning user it claims to be, & the walls stopped appearing.
Volume
We made each restaurant its own unit of work, wrapped in retries. A run now finishes what it can & records what remains for the retry pass.
Duplicates & gaps
Every row is keyed by restaurant & billing period, so a second copy simply overwrites the first. A separate check counts expected cycles against received files & flags every gap.
Visibility
We built a monitoring dashboard, a web app on a Python API with role-based login. The dashboard shows live progress & full history. Failed accounts re-run on their own, without repeating the whole batch.
An AI proof of concept against one account surfaced the first two problems before anything was promised.
Each box is one restaurant, & only the two that failed go back in for a retry until every restaurant is reconciled.
What changed after go-live?
After go-live, each manual job in the monthly close moved into the pipeline, as the table below shows.
| What | Before | After |
|---|---|---|
| Collecting invoices | Log in & download by hand | The pipeline fetches every file |
| Getting the figures | Retyped from PDFs into sheets | Extracted straight from the files |
| Duplicates & gaps | Found at month end, sometimes | Blocked by the keyed structure |
| Seeing a failed run | Nobody knew until totals slipped | Visible live on the dashboard |
| Adding a restaurant | Another spreadsheet to maintain | Another row in the next run |
We haven’t published an hours-saved figure, though the client reports the monthly close got faster. A number we haven’t measured is a number we won’t print.
Day to day, the change shows in what the finance team no longer touches. Nobody signs in to a portal anymore, & nobody retypes a single commission line.
The files arrive filed & extracted, & the reconciliation sheets fill themselves.
Still retyping delivery platform invoices every month?
Tell us which statements your team reconciles by hand, or check first whether your data is ready for automation.
What is running today?
Today, restaurant invoice reconciliation automation runs in production across the client’s restaurant brands, monthly & on demand. Each run signs in & collects the files, then extracts them & writes the sheets while the dashboard watches.
Since handover, the client has pointed the same runs at more accounts, the way an e-commerce seller adds marketplaces. The sheet structure hasn’t changed once.
The finance team’s month moved from producing the numbers to checking that they hold.
What would we do differently?
We would change four things on the next reconciliation build, each learned the hard way here.
Assume the portals fight back
We treated login automation as plumbing, & it fought us longer than the extraction did. Session handling belongs in week one, & we scheduled it late.
Design for partial runs
A pipeline that only knows success loses everything when file two hundred fails. We now build the retry & the resume before the happy path.
Lock the sheet format first
The keyed sheet structure ended the duplicate problem, but we found it in week four rather than week one. The format is the contract, & the code exists to fill it.
Show the run history early
For weeks, only our own team knew when a run had failed. The client’s trust grew the day it could watch one, so the dashboard should have shipped on day one.
The format is the contract, & the code exists to fill it.
Where else does this pattern fit?
The pattern behind restaurant invoice reconciliation automation fits wherever payouts arrive faster than a finance team can check them. The pipeline collects platform statements & extracts every figure, then checks for gaps & rebuilds one set of books.
| Industry | The equivalent problem | What changes in the build |
|---|---|---|
| E-commerce marketplaces | Sellers reconciling marketplace payout statements | New portal scripts, same keyed sheet |
| Hotels & travel | Booking-site commissions against nightly ledgers | Nightly cycles instead of monthly ones |
| Logistics | Carrier invoices checked against shipped loads | Rate cards join the extraction rules |
| Insurance | Broker statements tied back to policy payments | Stricter access control on the files |
| Franchise networks | Head office consolidating royalties across outlets | More accounts, the same run |
The same automation serves any business billed through platforms it doesn’t control, with everything landing in one set of books.
Porting takes new portal scripts & retuned extraction rules, plus one pilot month run in parallel. The pattern holds whether the statements come from logistics carriers or insurance partners.
Questions buyers ask about invoice automation
How it works
How does automated invoice processing work?
Software captures each invoice, pulls out the fields that matter & checks them, then writes them into your reporting format. Here, a scripted browser also collects the files, because the invoices sit behind platform logins. Your team checks the output instead of producing it.
Can software download delivery platform invoices automatically?
Yes, when it can hold a login the way a person does. A scripted browser signs in with the account’s details & downloads every file from the reports section. Saved sessions & managed proxies keep those logins stable at volume.
What happens when a platform changes its reports?
The script for that platform needs an update, while the other platforms keep running. The dashboard shows the failure the day it happens, which leaves time to fix it before month end.
Time, cost & rollout
How long does restaurant invoice reconciliation automation take?
A working pipeline for one platform & one account takes a few weeks. Each added platform brings its own portal script & extraction rules. Plan one month of running in parallel with the manual numbers before you switch over.
How much does invoice processing automation cost?
The cost depends on how many platforms you pull from & how varied the statements are. Brainy Neurals scopes it from a short call & sample files, then quotes a fixed price. An AI readiness assessment tells you first whether your statements are consistent enough to automate.
Is automated invoice data extraction accurate enough for accounting?
Platform statements follow fixed layouts, which is exactly what rule-based extraction handles well. Every figure lands in a keyed sheet the team can audit line by line, & every number traces back to its file.
What does your finance team keep retyping?
Tell us which platforms bill you & how their statements arrive. There's no sales sequence, just one reply from the person who would architect it.
Services behind this case study
The reconciliation pipeline drew on the Brainy Neurals services & industry work listed below.
Document AI services
Extraction pipelines turn platform statements & invoices into rows a finance team can audit.
AI agents & workflow automation
Multi-step pipelines like this one run from browser to finished sheet unattended.
Generative AI applications
Language-model workflows handle documents too varied for fixed extraction rules.
RAG development
You can ask questions across thousands of stored invoices instead of opening each one.
Hire AI developers
Automation engineers extend your team for the weeks when the portals fight back.
AI in retail & e-commerce
Reconciliation & monitoring builds serve anyone selling through someone else’s storefront.
An AI proof of concept against one live account shows within weeks whether this fits your statements. AI consulting helps pick what to automate first. An AI readiness assessment checks whether your data is consistent enough, & the industries hub shows where this pattern already runs.
Similar case studies
Three more Brainy Neurals builds shipped into a working environment rather than a demo.
Overhead Line Geometry Measurement
Stereo cameras on a moving train measure wire geometry, with inference running on the train.
AI Diet Assistant for Gastroenterology
Clinical dietary guidance is generated under review gates & runs live in a healthcare setting.
Personalised AI Meal Planning for Chronic Care
Structured meal plans are built from messy personal health data, grounded & reviewable.
Cite this case study
Barot, Nandni & Shah, Rushabh. AI Restaurant Invoice Reconciliation Automation in Food Delivery. Brainy Neurals, September 2026. https://brainyneurals.com/case-studies/invoice-reconciliation-automation/
Sources cited on this page
- Kokina J, Blanchette S. Early evidence of digital labor in accounting: Innovation with Robotic Process Automation. International Journal of Accounting Information Systems. 2019;35:100431. DOI 10.1016/j.accinf.2019.100431.
- van der Aalst WMP, Bichler M, Heinzl A. Robotic Process Automation. Business & Information Systems Engineering. 2018;60(4):269-272. DOI 10.1007/s12599-018-0542-4.
- Lerman K, Minton SN, Knoblock CA. Wrapper Maintenance: A Machine Learning Approach. Journal of Artificial Intelligence Research. 2003;18:149-181. DOI 10.1613/jair.1145.