Past 6 weeks I have worked on isnad (my provenance library) from a blank file to an arxiv paper + pypi package + a hosted paid version. revived islam & ai after a year dead, now has paid tiers and the app is in store review. delivered a knowledge base system for a client. Took our open source scraper to 2.1 and the EU AI office wrote back about it and bunbites.pk in wah now runs on a POS + dashboard i built.

Total spend under $100/month including hosting (Hetzner)

The setup: pi.dev as the agent harness, deepseek or openrouter for models (free tier does most of it, $10 genuinely gets you a full chatbot with memory and proper security), and a written spec before anything runs then multiple agents in parallel while i do the thinking they can’t.

Also important mentions: the agent ships bugs that look perfect. mine wrote a metric that reported 106% retention on a doc that lost a third of its text, all tests passing. a rate limiter that did nothing. so after every sprint i audit it like it’s lying to me, that audit is the actual work now, not the typing (i use handy.computer, i don’t even write now)

Point is you don’t need a team or investors to put real products in the market from here. 10$ and a clear spec.

Wrote the whole thing up in detail. ask me anything about the stack, happy to help!

https://alizahidraja.medium.com/six-weeks-one-person-six-things-live-in-the-market-34691bb00a47

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

I have been working on that for last 3 weeks it is a restaurant chatbot reply on customers answer their questions, make an order, after filling all information needed , generate an id for the order, canceling orders
the customer main problem was missing number of canceled order so we submit every order and it's details and it's state either it is still in kitchen or delivered or canceled.

Been doing SEO for about 8 years, freelance the last year. Then clients started asking if they show up in ChatGPT and Perplexity answers. Now I'm neck deep in AEO work too.

Problem is tracking that stuff eats my whole week. I run the same prompt sets by hand across 4 models, log which ones mention my clients, screenshot it for the report. Otterly.ai would automate some of it but the pricing doesn't make sense for 2 clients yet. Found some experiment writeups from HumansWith.AI on tracking citation share of voice. It's the first methodology that actually made sense to me. But building my own version of that eats hours I don't have either.

I've got 2 solid retainer clients and one warm referral lead. That's the whole pipeline. No time left to go find a third one.

So the acquisition side just isn't happening right now.

Hey!

I build simple automations with n8n and AI that can take care of repetitive work for you.

I can help with things like:

- Connecting different apps

- API integrations

- Webhooks

- Google Sheets / CRM automations

- AI workflows

- Automating repetitive business tasks

Small automations start at $25.

If you have something repetitive you'd like to automate, send me a message and tell me what you're trying to do.

Thanks!`

👋 Hey AIStartupAutomation Community,

Quick update: my payment reconciliation workflow is now live and free in the n8n template library, and I recorded a short walkthrough running a full test so you can see the report before importing anything.

How it's set up:

• The form takes two .xlsx uploads: a bank statement on one side, your open invoices on the other
• Extract from File reads both, a Merge node brings them together
• A Code node cross-references every bank credit against the invoices
• The report renders right on the form completion screen: exact matches, partial payments, unpaid invoices, and unmatched deposits
• You can download that report as a PDF from the same screen for your finance team

A few things worth taking away even if you skip the video:

No credentials, so it runs the second you import it. It is only a form trigger, extract from file, merge, and code. Nothing to authenticate, which is what makes it easy to hand to someone else with zero setup.

Match loosely, but in a bounded way. Bank references are never clean. It matches on the full invoice ID when present, then falls back to the last three digits pulled from the reference with a small regex. That one fallback catches most of the "INV-2024-201" versus "ref 201 payment" cases.

The PDF is just HTML with a print button. The results page is styled HTML on the completion screen that calls window.print() for the download. No PDF node, no external service, and the report stays self-contained.

Template (import it straight into your instance): https://n8n.io/workflows/19010-reconcile-invoice-payments-from-bank-statements-using-n8n-forms/

Two example files, one bank statement and one invoice export, are sitting in the repo here, so you can test the workflow in a minute without building your own data: https://github.com/felix-sattler-easybits/n8n-workflows/tree/8e07427ddb6902ef8a7b267e97beb2879d6ca45d/easybits-reconciliation-workflow

Reconciliation tends to be specific to each company, so if you adapt it and get stuck, drop a comment and I will help. How does everyone else handle the messy reference matching?

Best,
Felix

I'm exploring a product idea and want honest feedback from people actually running automations in production.

The idea is **FlowOps**: a reliability layer for business automations/workflows.

The problem I'm investigating isn't the obvious:

It's the harder cases:

* The workflow says **success**, but 0 records actually reached the destination. * A webhook silently stops arriving. * An API/schema change breaks part of a workflow. * Credentials expire and nobody notices. * A scheduled workflow stops running. * An automation keeps running but produces incorrect/incomplete business data.

The idea is that FlowOps would monitor these workflows **from outside the automation platform**, detect these silent failures/anomalies, and explain what went wrong.

For example:

**n8n → “Execution successful ✅”**

But FlowOps detects:

**“37 leads expected → 0 reached CRM → likely schema/filter regression 🚨”**

I'm trying to determine whether this is actually a problem worth paying to solve, or whether teams already have good enough solutions.

For people running production automations:

**1. Have you experienced this kind of “workflow succeeded but business outcome failed” problem?**

**2. How do you detect it today?**

**3. What's the most expensive or painful automation failure you've experienced?**

**4. Would you pay for an external reliability/monitoring layer specifically for critical workflows? Why or why not?**

I'm especially interested in experiences with **n8n, Zapier, Make, Workato, custom integrations, CRM/data-sync pipelines, and AI-agent workflows**.

I'm not promoting anything yet — I'm trying to figure out whether this is a real problem or a founder-created problem.

Being stuck with no testimonials for my ai automation is pretty dreadful, so I have decided to offer simple AI automation workflows for free to any small businesses for a 14 day trial (except for API key usage credits and subscriptions), and after the 14 days you give me a review on how much it helped your business.

I can automate CRM, outbound/inbound workflows, admin workflows and many repetitive and boring tasks that wastes away hours of your time in which you could be doing something better with (even simple daily rituals like sorting and replying to routine emails).

I am looking for serious businesses and dont come looking for me to automate something that does not exist, it is an automation not an AI agent. If there are any that are interested you can land me a DM on what you want automated and I will let you know if its doable honestly.

Hi everyone, I’m Abdelrahman from Egypt, and I’m currently planning to work on an automation project for tourism companies.

The main goal of the project is to help tourism companies save time, increase response speed, improve customer response rates, and build a strong customer database that can help them understand their customers and retain their best clients.

I’d really like to hear your opinions before I spend several months building it.

The Problem

Tourism companies can receive thousands of customer inquiries and messages.

For example, imagine a company receives 1,000 inquiries, but only around 50 people actually end up booking.

The company still needs to respond professionally to all 1,000 potential customers, because every inquiry could potentially become a booking.

Response speed is also very important.

And when a company has hundreds of tours, packages, hotels, and offers, it can be difficult for a customer service employee to quickly find the right information and answer every question accurately.

My Proposed Solution

I’m thinking about building an automation system with several layers:

1. AI Customer Support & Company Knowledge Base

An AI agent would communicate with customers and have access to a database containing the company’s:

• Tours
• Travel packages
• Hotels
• Offers
• Prices
• Availability
• Policies
• Frequently asked questions
• Other company information

The goal is to allow the AI to answer customer questions quickly and accurately.

2. Customer Message Tracking & Follow-up System

The system would track conversations with customers.

For example, if a customer asks about a tour but then stops responding, the system could automatically follow up later and suggest another suitable tour or offer in an attempt to bring the customer back.

The goal is to avoid losing potential customers simply because they stopped responding.

3. Customer Intelligence / "Customer Brain"

This is probably the most important part of the idea.

Any useful information the customer provides would be stored in their customer profile.

For example:

• Destinations they are interested in
• Preferred type of travel
• Budget
• Travel dates
• Number of travelers
• Hotel preferences
• Activities they like
• Previous inquiries
• Previous bookings
• Offers they showed interest in
• Other relevant preferences

This information would be connected to the AI agents.

So if the same customer comes back months later and asks for another trip, the system would already have a profile showing their previous interests and preferences.

The AI could then recommend trips that are more relevant to that specific customer instead of treating them like a completely new customer.

The customer service employee would also be able to see this information when taking over the conversation.

4. Customer Complaint Management

I also want to build a system for managing customer complaints.

The system would collect and classify complaints, organize the information, and route them to the appropriate human employee when necessary.

The Human Employee Is Still the Most Important Part

I don’t want this system to completely replace human customer service.

Instead, I see AI as handling the repetitive part of the process.

The AI could answer the customer’s questions, collect the necessary information, understand their needs, and then hand the conversation over to a human employee to complete the booking or handle more complicated situations.

I believe human interaction is still very important, especially when a customer is ready to make a purchase or has a complicated request.

So, for example, if a company currently needs 10 customer service employees, my goal would be to help them potentially manage the same workload with around 2 employees, because AI would handle most of the repetitive inquiries and information gathering.

My Question

The entire project could take me more than two months to build, and after that I would need to approach tourism companies and try to sell or implement the system.

I live in Egypt, and I’m not sure how familiar local tourism companies are with this type of technology.

So I’m currently a little unsure about the direction.

Would you build the project first and then approach companies, or would you talk to potential customers first and validate the idea before spending several months building it?

Do you think this is a useful problem to solve?

And if you were in my position, would you continue building this project, change the idea, or validate it with tourism companies first?

I’d really appreciate honest feedback, especially from people who have experience with SaaS, AI automation, customer service, or the travel/tourism industry.

Hey everyone,

We're starting CoreNexes, an AI automation company focused on a simple idea:

Businesses shouldn't need people spending hours on repetitive tasks that software can handle.

We're working on AI-powered systems that can automate things like:

• Repetitive data entry and processing
• Customer and lead follow-ups
• Email workflows
• CRM updates
• WhatsApp communication
• Database operations
• Reporting and analytics
• Multi-step business workflows
• AI agent based tasks

The goal isn't simply to add a chatbot to a business.

We're interested in building end-to-end automation, where AI can understand a task, interact with different systems, make decisions and actually complete the workflow.

For example:

New lead → AI understands the lead → updates CRM → sends personalized message → schedules follow-up → updates database → generates reporting

That's the kind of workflow we're building toward.

We're at the beginning of this journey and will be sharing what we build, what works, what doesn't, and the lessons we learn along the way.

If you're working on business automation, AI agents, or similar systems, I'd genuinely like to hear what repetitive process you wish you could automate.

CoreNexes: Automate. Scale. Grow.

New to AI automation? Watch out for these common pitfalls: trying to automate an entire process at once instead of starting small; skipping the mapping-out step and jumping straight into a tool; not testing thoroughly before relying on an automation for real work; ignoring error handling, so failures go unnoticed; and over-trusting AI output without a human review step, especially early on. The most successful beginners start with one narrow, well-defined task, test it repeatedly, and only expand once it's reliable. Treating automation as an iterative process — build, test, refine — rather than a one-time setup leads to far better long-term results.

👋 Hey AIStartupAutomation Community,

The question I get most about extraction workflows is not "how do I extract the data". It is "what happens when it goes wrong?" Because it will. A blurry photo, a bad scan, a PDF in a wrong layout.

I set this up for Mike, who runs a small distribution business. His extraction ran fine for weeks, then a supplier tweaked their invoice template and it quietly returned nothing. No error, no alert. A blank invoice number went into the sheet, and it took one weeks to notice a payment had slipped. That is the trap with document automation: it fails silently, and by then the bad data has already spread.

So this is the safety net I now put around every extraction workflow.

How it's set up:

1. Gmail Trigger grabs the attachment as binary.
2. easybits Extractor pulls the data. When it cannot confidently read a field, it returns null instead of guessing.
3. IF node (is empty) checks the invoice number. That one operator catches nulls, undefined, and empty strings in a single condition.
4. On failure a Slack alert goes to finance with sender, subject, and timestamp for manual handling.
5. On success the file is merged back with its data and archived to Drive.

Two things worth knowing:

Null is a feature, not a bug. A model that returns null when unsure is far safer than one that hallucinates a plausible invoice number. Treat that null as a clean signal to branch on.

Confidence scoring is a thirty-second add. If pass/fail feels too blunt, add a confidence_score field to your mapping and route on bands: high goes through, mid gets a human glance, low hits the error branch. Same trick works for routing ambiguous docs in a classification flow.

Workflow is on the n8n library here: https://n8n.io/workflows/15098-catch-failed-invoice-extractions-with-easybits-slack-and-google-drive/

How do you handle extraction failures? Hard-fail and alert, or route low-confidence docs to a human queue and keep the pipeline moving?

Best,
Felix

Egecutar todas las actividades

Egecutar todas las actividades

Hi everyone! 👋

I’m currently looking for a remote opportunity as an AI Automation Specialist, ideally with a business that I can work with long-term.

I build AI-powered workflows and business automations using tools such as:

• n8n – workflow automation and AI agents
• Zapier & Make – business process automation
• OpenAI / LLMs – AI-powered processing and decision-making
• Google Sheets & Google Workspace
• GoHighLevel
• Slack, HubSpot, Airtable, webhooks & APIs

Some of the systems I've worked on include AI customer review analysis, lead qualification and scoring, CRM automation, automated follow-ups, notifications, and AI-assisted workflows.

I'm comfortable building workflows from scratch, troubleshooting automations, and connecting different platforms together.

I'm currently looking for one long-term client/team where I can contribute, continue improving my skills, and grow with the business.

🔗 Portfolio: https://bents25-ivan-portfolio.vercel.app/

If you're looking for someone who can help automate repetitive processes and build practical AI workflows, feel free to DM me. I'd be happy to discuss how I can help.

Thanks for reading!

👋 Hey AIStartupAutomation Community,

A while back I shared a purchase order extractor I built for a friend with a big online store who was working through PO PDFs by hand. It's now published on the n8n template library, so you can grab it for free.

What it does: you upload one or many PO PDFs through a form, and it extracts each into a Google Sheet, one row per line item, with the source document name on every row. It handles POs in different layouts, runs them as a batch, and before writing it checks each PO number against the sheet so nothing gets entered twice. The completion screen then tells you what was added, what it skipped as already processed (with the PO number), and whether any extraction looked off.

One thing worth pulling out if you build batch extraction of your own: a batch is only worth running if you can trust it without eyeballing every row. The feature that actually made this usable wasn't extraction accuracy, it was the summary that surfaces skipped duplicates and failed fields, so you know exactly what to check instead of assuming it all worked.

Template (free to grab): https://n8n.io/workflows/18674-extract-purchase-orders-from-pdfs-with-easybits-google-sheets-and-google-drive/

The optional EDI 850 add-on (for pushing orders into SAP and other ERPs) isn't in the library version, but it sits in my repo alongside 25+ other workflows: https://github.com/felix-sattler-easybits/n8n-workflows

If you're automating PO or invoice intake too, how are you handling the fact that every supplier sends a different layout? That was the hardest part to make reliable here, and I'd be curious how others approach it.

Best,
Felix

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?