Restaurant kitchen pass with sealed delivery orders queued for courier pickup from food delivery platforms
Case study · Document AI and workflow automation

Restaurant invoice reconciliation automation across delivery platforms

A multi-brand European restaurant group was reconciling delivery platform payouts by hand, across hundreds of invoices arriving every billing cycle. Brainy Neurals built an automation pipeline that signs in, downloads every invoice, extracts the figures, and writes the reconciliation sheets. The finance team now reviews finished spreadsheets instead of assembling them.

Delivery platforms in one pipeline
3
Invoices downloaded by hand
0
Analysis sheets per invoice
1
Written by
Nandni Barot, Brainy Neurals

Nandni Barot

AI Engineer, Brainy Neurals

Rushabh Shah, Brainy Neurals

Rushabh Shah

AI Engineer, Brainy Neurals

Published September 2026 · Last updated September 2026 · 12 min read

02 · Quick answers

At a glance

What problem did this solve?

01

Days lost every month to retyping invoices by hand.

A restaurant group’s finance team downloaded and retyped hundreds of delivery platform invoices each cycle, and duplicates and gaps still slipped through.

What did Brainy Neurals build?

02

One pipeline that collects, extracts, and reconciles.

It signs in to three delivery platforms, pulls every statement, extracts each figure, and writes the reconciliation sheets on its own.

What changed after it went live?

03

Portal to finished sheet, with no one in the loop.

Invoices now flow straight from the platforms to filled sheets, and the finance team reviews the output while a dashboard tracks every run.

Who else could use this?

04

Anyone billed by a platform they don’t control.

Marketplace sellers, hotel groups, freight shippers, and franchise networks all reconcile the same way.

Engagement facts

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
03 · The problem

Why did reconciliation take days every month?

A multi-brand European restaurant group sells through three delivery platforms, and each platform bills in its own way. Statements arrive as PDF invoices, CSV transaction reports, and ZIP archives full of both, in several billing cycles a month. The finance team pulled all of it down by hand, the exact grind intelligent document processing exists to end.

Where it broke
  1. 01Every billing cycle meant another round of logins, one platform and one account at a time.
  2. 02Every invoice was opened, read, and retyped, and every keystroke was a chance to be wrong.
  3. 03Every restaurant needed its own analysis spreadsheet, and dozens of restaurants meant dozens of spreadsheets.
  4. 04Nobody could say 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, and digital[1].

Finance clerk sorting printed delivery platform invoices by hand before invoice reconciliation was automated
Before the build, each statement was downloaded, opened, and retyped by hand, one restaurant at a time.
04 · The alternatives

What gets tried before automation?

Four routes get tried before anyone builds, and each one is reasonable.

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 and exports Official data, always current One silo per platform Single-platform operators
Off-the-shelf invoice OCR Strong on standard supplier invoices Cannot log in and fetch Invoices that arrive by email
Platform-aware pipeline, our route Collection through reconciliation, one run Needs upkeep when portals change Multi-brand groups at volume
SAME PILE OF INVOICES — HOW FAR DOES EACH ROUTE GET YOU? Do it by hand Still takes days, every month Platform reports Only one app at a time Scanner apps Can’t log in and fetch the files Our automation One clean monthly total
Every route starts with the same delivery invoices. Three stop partway; only the automation reaches a finished monthly total.

Researchers describe this class of software as tools that drive another system’s interface the way a person would[2].

Tried this and hit the same wall? Tell us where it stopped.

Tell us where it stopped
05 · The solution

How we designed the reconciliation pipeline

Brainy Neurals built the invoice reconciliation automation as one straight line from portal to spreadsheet. A scripted browser collects the files, an extractor turns invoices into rows, and a builder fills the sheets. The whole thing runs 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 rejected waiting on exports or data feeds, because the portals are the only place every invoice reliably appears. So the pipeline drives each portal itself, holding 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 and 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.

CLIENT WORKSPACE THE PIPELINE THREE PORTALS SESSION STORE SCHEDULE DASHBOARD START · WATCH · RETRY MONTHLY SCRIPTED BROWSER VIA PROXIES SAVED LOGINS OBJECT STORAGE SHARED DRIVE MIRRORED INVOICE EXTRACTOR SHEET BUILDER ACCOUNT SHEET STORE SHEETS
Everything between the platform portals and 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 and cycles took most of the engineering.

06 · The stack

The technology stack we used

Every layer replaces a step the team ran by hand. We kept the client inside familiar tools.

The spreadsheet is older than the web, and month end still lives in one.

01 · Collection

Portal automation

A scripted browser on every platform

The only place every invoice reliably shows up

Instead of waiting on data feeds

Login sessions

Saved sessions held behind proxies

Fresh logins kept getting blocked

Instead of signing in from scratch each run

02 · Storage & extraction

File storage

Cloud storage plus the shared drive

Nothing ever sits on a laptop

Instead of a local downloads folder

Invoice reading

Layout rules tuned per statement

Each platform layout stays fixed

Instead of general-purpose OCR

Duplicate control

Keys per restaurant and period

A repeat invoice has nowhere to land

Instead of cleaning up after the import

03 · Reporting & operations

Reconciliation sheets

The team’s own spreadsheets

Finance already works there every day

Instead of a brand-new reporting tool

Analysis sheets

One formula template per store

One clean sheet for every invoice

Instead of pivots rebuilt by hand

Dashboard

A web app on a Python API

Every failure shows up live

Instead of log files nobody reads
07 · One run

How does one monthly run work?

One monthly run, start to finish, in the order the pipeline works.

  1. 1The run starts from the dashboard or the monthly schedule, with an account, a platform set, and a date range.
  2. 2A scripted browser signs in to each platform with the saved session and opens the reporting section.
  3. 3It collects every invoice, transaction report, and archive for every restaurant inside the billing range.
  4. 4Each file lands in object storage and the shared drive, filed by platform, account, restaurant, and period.
  5. 5The extractor reads each invoice and writes commissions, payouts, taxes, and fees into the account sheet.
  6. 6A builder fills one analysis sheet per store from the transaction data, and the dashboard logs the run.
DASHBOARD PIPELINE START THE RUN 1 SIGN IN 2 DOWNLOAD FILES 3 STORE AND FILE 4 EXTRACT FIGURES 5 FILL THE SHEETS 6 RUN LOGGED PER RESTAURANT RETRY LOOP
Each restaurant is its own unit of work, which is what lets a failed one retry alone.

Six steps, and the only human touch is the click that starts them.

Want this walked through for your statements? Book 30 minutes with Mitesh Patel.

No pitch. If it is not a fit, you will know in five minutes.

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08 · The challenges

The four problems that nearly stopped us

Four things went wrong, and the platforms came first, treating the automation as the intruder an unknown browser is. Sessions expired mid-run, and 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 and lose the lot.

Billing cycles overlap, and two cycles can bill the same restaurant twice. Early sheets carried duplicate rows and silent gaps where a cycle went missing.

And for weeks, failure itself was invisible. A run could stop at restaurant sixty with nobody knowing until a sheet came up short.

Records room shelves packed with paper statements, the volume automated invoice processing had to absorb
Hundreds of files arrive across overlapping billing cycles, and one missing file breaks the month’s reconciliation.

Extraction breaking when a source changes is old, studied ground. One study that watched 27 web wrappers for a year logged 37 format changes among them[3]. These are the weeks a client brings in specialist engineers rather than learning it slowly.

09 · The fix

How we fixed each of them

01 · Sessions

We moved every login onto persistent authentication states, carried behind a managed proxy network. The browser now behaves like the same returning user it claims to be, and the walls stopped appearing.

02 · Volume

We made each restaurant its own unit of work, wrapped in retries. A run now finishes what it can and records what remains for the retry pass.

03 · Duplicates and gaps

Every row is keyed by restaurant and billing period, so a second copy simply overwrites the first. A separate check counts expected cycles against received files and flags every gap.

04 · Visibility

We built the monitoring dashboard, a web app on a Python API with role-based login. It shows live progress and full history. Failed accounts re-run on their own, without repeating the whole batch.

A proof of concept against one account surfaced the first two of these before anything was promised.

DONE — STAYS FINISHED FAILED — GOES BACK IN ONE RUN · 8 RESTAURANTS Failed Failed ALL RECONCILED RETRY · FAILED ONLY
Each box is one restaurant. The six that finished stay done; only the two that failed are sent back in and retried, until every restaurant is reconciled.
10 · The results

What changed after go-live?

What Before After
Collecting invoices Log in and download by hand The pipeline fetches every file
Getting the figures Retyped from PDFs into sheets Extracted straight from the files
Duplicates and 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 have not published an hours-saved figure, though the client reports the monthly close got faster. A number we have not measured is a number we will not print.

So the change from invoice reconciliation automation, day to day, is what the finance team no longer touches. Nobody signs in to a portal anymore. And nobody retypes a single commission line.

The files arrive filed and extracted, with the reconciliation sheets filling themselves.

Tell us about your project

What does your finance team keep retyping?

No sales sequence. One reply, from the person who would architect it.







    No sales sequence. One reply, from the person who would architect it.

    11 · In production

    What is running today

    The invoice reconciliation automation runs in production across the client’s restaurant brands, on a monthly schedule and on demand. Each run signs in, collects, extracts, and writes the sheets while the dashboard watches it work.

    Since handover, the client has pointed the same runs at more accounts, the way an e-commerce seller adds marketplaces. The sheet structure has not changed once.

    The finance team’s month moved from producing the numbers to checking that they hold.

    Hands filing a single slim folder after automated financial reconciliation replaced manual invoice handling
    Month end still happens, and the filing that used to take days now fits in one folder.
    12 · Lessons

    What would we do differently?

    01Assume the portals fight back

    We treated login automation as plumbing, and it fought us longer than the extraction did. Session handling belongs in week one, and we scheduled it late.

    02Design for partial runs

    A pipeline that only knows success loses everything when file two hundred fails. We now build the retry and the resume before the happy path.

    03Lock the sheet format first

    The keyed sheet structure ended the duplicate problem, found in week four rather than week one. The format is the contract, and the code exists to fill it.

    04Show the run history early

    For weeks, only our own team knew when a run had failed. But the client’s trust grew the day it could watch one. The dashboard should have shipped day one.

    The format is the contract, and the code exists to fill it.

    13 · The pattern

    Where else does this pattern fit?

    Invoice reconciliation automation collects platform statements, extracts every figure, checks for gaps, and rebuilds one set of books. It belongs wherever payouts arrive faster than a finance team can check them.

    Industry The equivalent problem What changes in the build
    E-commerce marketplaces Sellers reconciling marketplace payout statements New portal scripts, same keyed sheet
    Hotels and travel Booking-site commissions against nightly ledgers Nightly cycles instead of monthly ones
    Logistics Carrier invoices audited 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
    Marketplace sellers Hotels & travel Logistics carriers Franchise groups The same automation COLLECT · EXTRACT · CHECK One set of books
    The same automation runs for any business billed through platforms it does not control, all landing in one set of books.

    Porting takes new portal scripts, retuned extraction rules, and one pilot month run in parallel. The pattern holds whether the statements come from logistics carriers or insurance partners.

    14 · FAQ

    Questions buyers usually ask

    How it works

    How does automated invoice processing work?

    Software captures the invoice, extracts the fields that matter, validates them, and writes them into your reporting format. Here, a scripted browser also collects, because the invoices sit behind platform logins. Your team checks the output instead of producing it.

    Can software download invoices from delivery platforms automatically?

    Yes, when it can hold a login like a person does. A scripted browser signs in with the account’s credentials, opens the reports section, and downloads every file. Saved sessions and managed proxies keep those logins stable at volume.

    What happens when a platform changes its reports?

    The script for that platform needs an update, and the other platforms keep running. The dashboard shows the failure the day it happens, leaving time to fix it before month end.

    Time, cost, and rollout

    How long does invoice reconciliation automation take to build?

    A working pipeline for one platform and one account takes a few weeks. Each added platform brings its own portal script and extraction rules. Plan one month of running in parallel with the manual numbers before switching over.

    How much does invoice processing automation cost?

    The cost of invoice reconciliation automation depends on how many platforms you pull from and how varied the statements are. Brainy Neurals scopes it from a short call and 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, exactly what rule-based extraction handles well. Every figure lands in a keyed sheet the team can audit line by line. The team reviews the output, and every number traces to its file.

    If statements arrive faster than your team can check them, tell us what you reconcile.

    Tell us what you reconcile Book 30 minutes
    15 · Services

    The services this was built from

    Document AI services

    Extraction pipelines that turn platform statements and invoices into rows a finance team can audit.

    AI agents and workflow automation

    Multi-step pipelines like this one, running a browser, an extractor, and a builder unattended.

    Generative AI applications

    Language-model workflows for documents too varied for fixed extraction rules to hold.

    RAG development

    Ask questions across thousands of stored invoices instead of opening them one at a time.

    Hire AI developers

    Automation engineers who extend your team for the weeks when the portals fight back.

    AI in retail and e-commerce

    Reconciliation and monitoring builds for anyone selling through someone else’s storefront.

    A proof of concept against one live account shows within weeks whether this fits your statements. AI consulting helps pick what to automate. An AI readiness assessment checks whether your data is consistent enough, and the industries hub shows where this already runs.

    16 · More proof

    Other builds with this shape

    Three more Brainy Neurals builds, each shipped into a working environment rather than a demo.

    R / 01
    Overhead Line Geometry Measurement

    Stereo cameras on a moving train measuring wire geometry, with inference on the train.

    R / 02
    AI Diet Assistant for Gastroenterology

    Clinical dietary guidance generated under review gates, live in a healthcare setting.

    R / 03
    Personalised AI Meal Planning for Chronic Care

    Structured meal plans built from messy personal health data, grounded and reviewable.

    Cite this case study

    Barot, Nandni, and Rushabh Shah. Restaurant invoice reconciliation automation across delivery platforms. Brainy Neurals, September 2026. https://brainyneurals.com/case-studies/invoice-reconciliation-automation/