We’ve been building Adisyonist AI, a platform designed for restaurants, cafés, and other food-service businesses.

When we first started, most of our focus was on features: orders, checks, QR menus, inventory, staff, shifts, reporting, and so on.

But as the product grew, we realized that simply having all of these features isn’t enough.

The real challenge is making them work together in a way that actually makes daily operations easier.

Instead of switching between different tools, the idea behind Adisyonist AI is to let a business manage its day-to-day operations from one place.

Orders and checks can be handled from the same system, QR menus can connect customers directly with the business, inventory can be followed alongside daily operations, and staff and shifts can be managed without relying on separate tools.

Real-time reporting also gives managers a clearer view of what is happening in the business without having to collect information manually from different places.

The platform can be used on mobile, tablet, and desktop, so employees and managers can access the parts they need depending on where and how they are working.

We also support multiple languages, which has become especially important for businesses serving international customers or working with multilingual teams.

While building all of this, one thing became very clear to us:

A restaurant employee doesn’t care how many modules a system has. They just want to take an order, manage the check, see what needs attention, and continue working without unnecessary steps.

That’s why a big part of our development process now focuses on simplifying workflows rather than simply adding more features.

If you’re curious about what we’re building, you can follow the project at Adisyonist.com or take a look at the App Store version.

We’re still actively developing the platform, so feedback from people outside the project is genuinely useful to us.

If you were using something like this in a restaurant or café, what would you change, simplify, or add?

[ Removed by Reddit on account of violating the content policy. ]

👋 Hey AIStartupAutomation Community,

A few days ago I shared a reconciliation workflow that matches bank deposits to open invoices. A few people asked about the tricky parts, so here are the five things I took away from building it. Most of them are less about n8n and more about how messy real financial data is.

1. The reference field is where reconciliation dies.
Bank references almost never match your invoice IDs cleanly. You get "INV-2024-201", "ref 201", "payment 0201 thanks", all for the same invoice. So do not match on the exact string. Match on the full invoice ID when it is there, then fall back to a looser signal like the last three digits pulled out with a small regex. Fuzzy but bounded beats exact every time.

2. "Matched vs unmatched" is too coarse.
The moment I split results into four states instead of two, the report got genuinely useful: exact matches, partial payments, unpaid invoices, and deposits with no invoice at all. The two middle buckets are where the money actually leaks, and a simple matched/unmatched view hides them completely.

3. A deposit with no invoice is a signal, not noise.
Money landing in the bank that matches nothing is exactly what manual reviewers skip because they are focused on ticking off invoices. But that column is where the interest payments, refunds, bank fees, and typo'd customer references all show up. Give it its own table so nothing silently disappears.

4. Let the code do the arithmetic, keep the human for judgment.
The old manual process was someone staring at two spreadsheets for hours, which is exactly how 525.52 becomes 252.25. A Code node compares amounts to the cent and never gets tired. The person only reviews the handful of flagged exceptions, not the 90 percent that reconcile cleanly.

5. Every result needs a way back to the source.
Reconciliation you cannot audit is just guessing. So each row in the report keeps the original bank reference, the value date, and the invoice ID side by side. When a number looks off, you can trace it back in seconds instead of reopening the raw statement.

The workflow itself, plus two example files (an invoice export and a bank statement) so you can run it right away, is here: https://github.com/felix-sattler-easybits/n8n-workflows/tree/8e07427ddb6902ef8a7b267e97beb2879d6ca45d/easybits-reconciliation-workflow

It sits alongside 25+ other free n8n workflows in my repo, including plenty of finance ones. A star helps me out a lot if any of them save you time: https://github.com/felix-sattler-easybits/n8n-workflows

What is your reconciliation matching logic? I am especially curious how people handle one payment covering several invoices, because that was the case I found hardest to generalise.

Best,
Felix

👋 Hey AIStartupAutomation Community,

A friend of mine runs a small e-commerce shop, and at the end of every month his finance colleague Sarah has the same grim job: open the bank statement, open the list of open invoices, and manually tick off who actually paid. Full payments, short payments, deposits that match no invoice at all. Hours of eyeballing two spreadsheets side by side.

So I built her something that does the matching in one click. She uploads the two files through an n8n form, and a clean reconciliation report comes back in the browser, ready to download as a PDF.

How it's set up:

• The form takes two .xlsx uploads: the open invoices export and the bank statement
• Each file is read into rows, then a Code node cross-references every bank credit against the invoices
• Everything is sorted into four buckets: exact matches, partial payments (short or over), unpaid invoices, and unmatched deposits
• The result renders as a styled summary page with a match rate, totals, and a table per bucket

A couple of things worth stealing:

Fuzzy reference matching. Bank references are never clean. Mine matches on the full invoice ID when it is there, and falls back to the last three digits pulled out of the reference with a small regex. That alone catches most of the "INV-2024-201" versus "ref 201 payment" mismatches.

No PDF library needed. The final report is just HTML rendered on the form completion screen, with a button that calls window.print(). The browser does the PDF export for free, so there is no extra node or service to host, and the whole report is self-contained.

Four buckets, not two. Splitting partial payments and unmatched deposits out from a plain matched or unmatched view is what makes it actually useful. The unmatched deposits table is where you catch refunds and payments with a typo in the reference.

I dropped two example files in the repo (one invoice export, one bank statement) so you can run it end to end in a minute without building test data.

Workflow JSON and the example files: https://github.com/felix-sattler-easybits/n8n-workflows/tree/8e07427ddb6902ef8a7b267e97beb2879d6ca45d/easybits-reconciliation-workflow

Find 25+ more free n8n workflows in my repo, including plenty of finance ones. A star helps me out a lot if any of them save you time: https://github.com/felix-sattler-easybits/n8n-workflows

How do you handle reconciliation right now? Curious whether people match on amount, reference, or something smarter, because the messy references were by far the trickiest part.

Best,
Felix

so I was drowning in customer support emails, like seriously overwhelmed. I decided to give AI a shot to see if it could help me manage the chaos. I set up a simple AI system to categorize emails and suggest responses.

I used GPT 4 for generating draft replies based on previous responses. It wasn't perfect, but it cut down the time I spent on each email by at least 50%! The AI would draft something, and I'd just tweak it a bit before sending it out.

It was a game changer for my small business. Not only did it save me hours each week, but it also helped me respond faster, which made my customers super happy. Anyone else tried something similar? How did it work out for you?

I’ve been thinking about how much time businesses lose on small tasks that happen every single day.

Not the complicated work.

The repetitive stuff:

• Copying data between systems
• Sending the same follow-up emails
• Creating routine reports
• Processing documents
• Updating spreadsheets
• Entering customer information
• Checking and validating data

Individually, these tasks don’t seem like a big deal.

But when they happen hundreds of times every month, the hours add up.

The interesting part is that not every task needs AI. Some can be handled with simple automation. Others need AI because they involve reading, understanding, or making decisions.

If you could automate ONE repetitive task in your business tomorrow, what would it be?

I’m curious what people are actually dealing with—not what AI demos claim businesses need.

👋 Hey AIStartupAutomation Community,

As you might already know, my friend Mike runs a small business and has one folder where everything lands: invoices, receipts, the odd contract, a scanned delivery note someone photographed on their phone. He wanted to auto-sort it, but he was nervous about handing that to a model. His words were roughly: "what happens when it's wrong and I don't even know it was wrong?"

That's the real problem with plain classification. You get a label back, but no idea whether the model was confident or just guessing. So I built a flow that returns the class and a confidence score in the same call, and routes anything shaky to a human before it goes anywhere.

How it's set up:

• Form upload: drop a PDF, PNG, or JPEG through a hosted n8n form.
• Classify + score in one call: the easybits extractor returns two fields at once – document_class (or null if it can't decide) and confidence_score (0.0 to 1.0). No second model call, no extra latency, no extra cost.
• IF-node routing: one plain IF node checks "class is empty OR confidence below 0.5". Either condition sends the document to Slack for manual review. Everything else flows straight through.
• Continue however you like: from the success branch, route to Drive folders, log to a Sheet, or fire type-specific extraction. The template leaves that open.

A few things worth knowing if you build something similar:

1. Ask for the confidence score in the same extraction call as the class. It's just a second field in your mapping. You get a routing signal for free instead of paying for a whole separate model pass to second-guess the first one.
2. Treat that number as a routing signal, not ground truth. It's self-reported, so it won't be perfect – but it's a reliable trigger for "have a human glance at this one" and that's all you need it to be.
3. Route on two conditions, not one. Empty class catches the "I have no idea" cases; the threshold catches the "I have an idea but I'm not sure" cases. Both need a human, for different reasons.
4. Tune the threshold to your review capacity, not to a nice round number. Default is 0.5. Raise it to 0.7 if a misroute is expensive downstream, lower it if your review queue is already full. It's a dial, not a constant.

Template on the n8n library here: https://n8n.io/workflows/15229-classify-documents-and-score-confidence-with-easybits-extractor-and-slack/

How are the rest of you handling low-confidence classifications right now? Manual review queue, a second model to check the first, or just letting it route and fixing mistakes after the fact?

Best,
Felix

One problem we noticed in sales workflows is that reps often have to reopen their CRM after every call, search for the lead, and then update remarks or follow-up details.

We worked on a workflow where the system automatically checks the called number once the call ends.

If it’s a new number, the Add Lead screen opens automatically.

If it’s an existing lead, the relevant lead/follow-up screen opens automatically.

The goal is to reduce manual searching and make sure lead updates happen immediately after the call.

How does your sales team handle post-call CRM updates — manually or through automation?”

Hello everyone!

We’re excited to announce that ParcelAI is coming soon!

ParcelAI monitors selected supplier emails, detects important stock changes, and updates your database and website in real time.

This means:

• Fewer manual updates
• More accurate inventory information
• A better customer experience

When an item is unavailable, customers can see when it’s expected to be back in stock, instead of searching elsewhere.

Final testing is underway

We’re currently running final tests with selected companies, and the early feedback has been incredibly encouraging.

We’re putting the finishing touches on ParcelAI, and we can’t wait to share it with you.

We’d love to hear your thoughts!

What do you think of ParcelAI, and which features would be most useful for your business? Let us know in the comments 👇

Check out MeridianAI on Whop to see more!

Stay tuned, launching soon!

A parent sees a childcare center on Instagram.

They send a message.

And then… nothing.

Maybe the owner is busy.
Maybe the receptionist is handling another parent.
Maybe the message gets buried under 20 other DMs.

A few hours later, they finally respond.

By then, that parent may have already messaged 3 other childcare centers.

That was the problem I wanted to solve.

I recently built an AI-powered Instagram appointment setter for a childcare business using n8n, Gemini, Instagram and Google Sheets.

But I didn't want to build another “AI chatbot that answers FAQs.”

The actual challenge was:

How do you automate the repetitive parts of an enrollment conversation without making the interaction feel automated?

So I designed the workflow around what an actual enrollment advisor would do.

What happens when a parent sends a DM?

The Instagram message comes into the workflow through a webhook.

The system extracts the sender and message, checks whether the parent already exists in the CRM, and then passes the conversation to the AI appointment setter.

The AI has access to the business's approved information, such as:

• Programs
• Age ranges
• Appointment types
• Business hours
• Tour information
• Existing customer context

It then handles the conversation naturally.

For example:

Parent:
“Hi, my daughter is 2 and we're looking for childcare. What do you guys offer?”

Instead of dumping an entire program list on them, the AI can understand that the child is 2 and respond based on the relevant program.

If the parent then says:

Parent:
“Can we come see the place?”

The system shifts from information mode → appointment-setting mode.

It can collect the required information, determine the appointment type, ask for a preferred day/time, and keep the conversation moving.

The part I found more interesting: memory

If the same parent comes back later, the system doesn't treat them like a brand-new lead.

It checks the CRM and conversation memory.

So instead of:

“Hi! What's your name?”

It can recognize that this is a returning parent and continue from the existing context.

That's important because real sales conversations don't restart from zero every time someone sends a message.

Then there's the CRM side

Once an appointment action is actually finalized, the workflow structures the information and updates the CRM.

It stores things like:

• Parent name
• Phone
• Email
• Child's age
• Program of interest
• Appointment type
• Preferred day/time
• Lead score
• Appointment status
• Conversation summary
• Escalation status

It also handles existing records so the same lead doesn't just get duplicated every time they send another message.

And I deliberately added human escalation

This was one of the most important parts.

The AI isn't supposed to answer everything.

If a parent asks something outside the approved business information, has a complaint, explicitly wants a human, or the situation requires human judgment, the workflow flags the conversation for a team member.

The goal isn't:

“Replace the receptionist.”

It's:

“Stop making the receptionist spend their day answering the same repetitive questions.”

That distinction matters.

A childcare team should be spending their time talking to parents, giving tours, handling sensitive situations and actually enrolling families — not manually copying Instagram information into a spreadsheet all day.

The architecture

The workflow ended up looking roughly like this:

Instagram DM
↓
Webhook intake
↓
Message extraction
↓
Check existing customer
↓
Load business + program context
↓
AI appointment setter
↓
Conversation memory
↓
Structured appointment data
↓
Send Instagram response
↓
Determine whether the lead needs logging/human intervention
↓
Update CRM

The AI is essentially sitting between the Instagram inbox and the enrollment workflow.

And that's the part I'm most interested in with AI automation right now.

Not:

“Look, I connected ChatGPT to an app.”

But:

“What repetitive business process is costing someone time or losing them money, and can I redesign that process with AI + automation?”

This project was a good example of that.

There's still a lot I'd improve before putting something like this into a real childcare business — especially around production-grade appointment availability, authentication, error handling, observability and integrations with a proper CRM/calendar.

But building it made one thing very clear to me:

The valuable part of AI automation isn't the AI itself.

It's designing the workflow around the business problem.

I'd love to hear from people building AI automations for businesses:

What repetitive workflow have you automated that actually made a measurable difference?

#AIAutomation #BusinessAutomation #WorkflowAutomation #AppointmentSetting #LeadGeneration #AIForBusiness #CustomerExperience #n8n #AgenticAI #InstagramAutomation #Childcare

Hi, I am Lakshya from India, Nagpur.
I am just curious about whats working for people and what isn't.

So I am looking for people who are doing AI Automations be it the starting phase or if they've been doing it for a long time.

I would like to have a quick chat on google meet, zoom or a call whatever and whenever you're comfortable.

Comment down below or dm me anythnig works.

Building a tool to auto-import work schedules — would anyone share an anonymized screenshot of their schedule to help me test?

👋 Hey AIStartupAutomation Community,

A while back I posted a couple of finance workflows I built for my friend Mike. Around the same time he came to me with a new one: he'd just been hit with almost €2,000 in bills because two contracts auto-renewed after the first period. Nobody in his team had caught the cancellation window early enough.

Classic small-business pattern. Contracts scattered across Drive folders and inboxes, nobody tracking anything. The only time anyone notices a contract exists is when the invoice for the next term lands.

So I built him a contract intake workflow to fix it. I cleaned it up over the last few weeks and just published it on the n8n template library.

How it's set up:

The workflow watches a Google Drive folder. Mike's finance colleague Sarah just drops any contract in there (PDF, JPG, PNG).

The file goes to the easybits extractor, which does two jobs in a single call: it classifies the contract (SaaS, Lease, Service, Insurance, Other) and pulls every renewal-relevant field at the same time – client, provider, start date, term length, notice period, auto-renew flag, contract value, signatories.

A Set node calculates end date and cancellation deadline in n8n. Cancellation deadline is end_date – notice_period_days, which for 60–90 day notice clauses lands the alert weeks before the renewal itself. That's the whole point.

A Switch on contract_class routes the row into the matching tab of one Google Sheet – one source of truth for every renewal in the company.

Two things I learned:

• Classify and extract in one call. I almost built five type-specific pipelines. Turns out you can define the classification as just another field in the same pipeline – half the complexity, half the cost.
• Never extract dates you can calculate. Contracts almost never print the end date directly. Ask a model to do date math and you get silent off-by-one bugs. Extract the raw values, do the arithmetic in a Set node.

Workflow on the n8n library: https://n8n.io/workflows/15230-classify-contracts-and-track-renewals-with-easybits-google-drive-and-sheets/

Also on my GitHub alongside 30+ other community workflows (a star helps other builders find it): github.com/felix-sattler-easybits/n8n-workflows

How are you tracking contract renewals today? Spreadsheet someone updates manually? Tool like Spendflo/Vendr? Or hoping for the best, like Mike was?

Best,
Felix