AI automation that removes the busywork.
The inbox nobody answers until Thursday. The quote that went cold because nobody chased it. The admin hire you can't afford and can't do without. WebBox is a Dubai-based AI automation agency that builds the systems behind those jobs for businesses wherever they operate: workflows and AI agents that reply, chase, route, file and report, around the tools your team already uses.
What happens in the 3.1 seconds nobody sees.
This is a real system we run for a publishing network. Scrub through a single inbound email and watch each stage fire.
From: m.haddad@fintechco.io Subject: Sponsored article pricing? Hi, what would a sponsored feature on Dubai Week cost, and how fast can it go live? We have a funding announcement next week.
A lightweight listener on each inbox does one job: catch the mail and hand it to the core. No logic lives here, so adding a 64th site takes minutes.
{ site: "Dubai Week", tier: 2, price: 295, currency: "USD", voice: "warm, editorial, no exclamation marks", upsell: ["Gulf Business News", "ABC Money"], history: 0 }The agent is one brain wearing 63 faces. Before a word is written it knows which publication it is, what it charges, and how it speaks.
stage → first_contact intent → pricing + timeline signals → urgency (funding announcement, "next week") risk → low (business domain, no prior disputes)
Every conversation sits in a named stage with its own module. The router decides which one, so the agent always knows what it is trying to do next.
Hi Maya, Thanks for getting in touch. A sponsored feature on Dubai Week is $295, and with your copy approved we can usually publish within 48 hours, so a launch next week is comfortable. If it helps, I can include a complimentary placement on Gulf Business News with the same article. Shall I send the invoice?
Pricing is fixed by tier so margin is never negotiated away. Objections are met with added value, not discounts.
✓ reply sent from sales@dubaiweek.ae ✓ client record created (4,301 total) ✓ invoice PO-202610-1108 drafted, $295 ✓ chase sequence armed: 24h → 72h → 7d ✓ payment webhook listening
From here the system runs the deal alone: it chases, reinstates the placement when payment lands, and offers the upsell. Total cost: about $105 a month for all 63 sites.
The questions worth asking.
The three things people ask us before they buy, answered without the sales gloss.
What in my business can actually be automated?
Most of the work that follows a pattern: logging enquiries, first replies, follow-ups, invoice chasing, moving data between tools, weekly reports, reading documents and filing them. AI now covers the steps that used to need a person to read something and decide, like classifying an email or drafting a reply. What stays human is the judgement: pricing exceptions, the difficult client, the decision that needs context only you have.
What happens if an automation breaks?
You hear about it from an alert, not from a customer. Every workflow we build has error handling, a notification when a step fails, and a log of what ran. Anything risky, like sending money or deleting data, sits behind a human approval step. You get documentation and a named owner for every system.
Automation, or hire someone?
Often both. Automation takes the volume, people take the judgement. One case study below shows a system running for around $105 a month that replaced $9,000 to $9,500 a month in staff cost. Not every build returns that much, which is why we cost each one before we build it.
Your team is doing a machine's job.
Busywork scales your costs, not your output
Every new client means more emails to answer, more data to re-key and more things to chase. So the business grows by adding people to do admin, and the people you hired for their judgement spend half their week copying and pasting.
Systems that run the routine
The routine work runs on its own, the same way every time, day and night. Your team gets its hours back for the work that needs a person, and growth stops requiring a new hire for every new batch of clients.
Built around how your business runs.
Off-the-shelf tools are built for the average business. Yours has its own rules: who gets which lead, when a quote needs sign-off, which supplier is always late. We design the system around those rules, then build it, so the automation fits the way your team already works instead of forcing a new process on it.
Memory and judgement where it counts. Airtable or your own database holds what the system needs to remember: clients, orders, threads, rules. AI models handle the reading and writing. In the sales agent case study, that's how one system keeps full recall across thousands of conversations.
- Bespoke AI agents that work through multi-step tasks: read the email, check the CRM, draft the reply, book the meeting.
- Custom integrations for tools with no ready-made connector, written against their API.
- Business logic beyond drag-and-drop: pricing rules, approvals, routing and exceptions, written exactly as you run them.
- One connected system, not a pile of separate automations, documented so your team knows what runs where.
What it looks like on a Tuesday.
Say you run a 20-person clinic group with three locations. Here is an ordinary Tuesday once the systems are in.
- 07:40. Eleven enquiries arrived overnight from the website, Instagram and WhatsApp. Each one is already in the CRM, tagged by treatment and location, and has had a reply offering the next three available slots.
- 09:15. Two patients booked from those replies. Their intake forms went out automatically, and the clinic manager gets one message listing who is coming in this week with forms still missing.
- 11:00. A supplier invoice lands in the accounts inbox. The AI step reads it, matches it to the purchase order, and files it. The one that doesn't match is flagged for a person.
- 14:30. Last week's quotes that haven't been answered get a polite second message. The ones that have now been ignored twice are flagged to a human to call.
- 17:00. The owner's daily summary arrives: enquiries, bookings, no-shows, revenue by location. Nobody built a spreadsheet to make it.
Nothing in that list needs a new hire. It needs the tools you already have, connected and given a few rules.
Where the hours usually go.
The same handful of jobs eat most teams' weeks. These are the ones we automate first, because the return is quick and the risk is low.
Lead intake and first replies
New enquiries from every channel logged in the CRM and answered within minutes, with an AI step drafting the reply in your voice. Nothing waits in an inbox overnight.
Follow-ups and chasing
Unanswered quotes, unpaid invoices and stalled threads chased on a schedule you set. The system remembers so nobody has to.
Routing and triage
Requests sent to the right person or queue based on what they say, not on who happened to open the inbox first.
Data sync between tools
Records kept consistent across CRM, sheets, inboxes and databases. One change, updated everywhere, with no re-keying.
Reporting
The weekly numbers compiled from the systems that hold them and sent before anyone asks.
Documents and content
Invoices, orders and forms read, extracted and filed. Content drafted from your sources in your house style, ready for review or published on a schedule.
Systems we've built, with the numbers.
These are real builds for a digital publishing business. The shape of each one transfers to almost any business with an inbox, a team and a backlog.
AI sales agent: one system behind 63 inboxes
Every inbound sales conversation across 63 properties, from first enquiry to pricing, objections, invoicing and upsell, handled by one agent with no human in the loop for the standard deal. It manages 4,300+ client records, runs for around $105 a month, and replaced $9,000 to $9,500 a month in staff cost.
AI email sales agent case study →Self-diagnosing operations: 60+ sites, one approval gate
An agent system watches 60+ live sites, works out what's wrong in read-only mode, and proposes the fix in chat. Nothing destructive runs without a human saying yes. It took over the job of a full-time technical operations hire.
Self-diagnosing operations case study →A factory that builds automations
Around 15 specialist agents take a plain-language brief and produce a tested workflow for n8n, Zapier or Make. Reviewers check the work before it ships, and lessons are written back to memory so the next build is better.
Automation factory case study →AI chief of staff
A founder's calendar, inbox, meeting notes and document chasing run as one assistant. When a transcript mentions a follow-up meeting, the system offers to book it.
AI chief of staff case study →Content engine: news to published article
A scheduled pipeline pulls current news, ranks the strongest story, writes it in a controlled editorial voice, generates an image and publishes it, with zero human steps per article.
Content engine case study →Public data into publishable articles
A poller pushes public filings through 10 quality filters, routes 26 filing types down 7 research paths, and writes each story to its own template.
Public data journalism case study →Appointment setter in the operator's own voice
An outbound agent replies to leads with voice notes in the operator's cloned voice, tracks follow-up state per lead and books the meeting.
Voice appointment setter case study →The right tool for each job.
We pick the tool by what the workflow needs, not by habit. Most businesses end up with a mix, and all of it is documented so your team knows what runs where.
n8n: the backbone. For flows where data moves between several systems with logic in the middle. n8n wires your CRM, inboxes, sheets, databases and APIs into one system, with AI steps that classify, draft, summarise and decide. Every workflow has error handling and an alert, so a failed step shows up as a message, not as a missing report.
Custom code: when the logic gets real. When the job needs more than drag-and-drop, such as agents that reason through a multi-step task, integrations for tools without a connector, or rules too specific for a visual builder, we write it. Tested against real data before it goes live, and the code is yours.
Zapier and Make: the quick wins
For a few steps between everyday apps, Zapier or Make gets it running in days. When the logic grows, the flow moves to n8n or code.
Specialists in the tools that build faster.
Our engineers work every day in AI coding tools such as Claude Code and OpenAI Codex, alongside n8n, Make and Zapier. Used well, these tools let a small team write, test and ship production-grade automation in days rather than months. Used carelessly, they produce code nobody can maintain, which is why every build is reviewed, tested against real data and documented before it goes live.
- AI coding tools: Claude Code, OpenAI Codex and similar, for custom agents, integrations and scripts.
- Workflow platforms: n8n, Make and Zapier, chosen by what the job needs.
- Production standards: version control, testing, error handling and monitoring on every build.
- Code you own: handed over with documentation, never locked inside our accounts.
Built so it doesn't fail quietly.
An automation you can't trust is worse than none, because people stop checking and then miss the day it breaks. We build every system with the same safeguards.
What you get, and when.
Scope agreed
We confirm the first workflow, agree what done looks like, and get access to the tools involved. If you've had an AI Consultation, we start from the roadmap.
First system live on real data
You see it running on your actual enquiries, invoices or records, with a human approval step switched on while you watch it work.
Handed over
Edge cases tested, alerts in place, documentation written, your team trained. The approval step comes off where you're comfortable.
The next build
With the first system paying back, we move to the next item on the list, or you take it from there. Timings are typical for a single, well-defined workflow. Larger agent systems take longer and are scoped individually.
How a build runs.
Scope
We pick the workflows with the highest, safest return and agree what success looks like for each, in hours, response time or cost.
Build
We build around your existing tools and show it working on real data early, so you can correct it before it's finished.
Harden
Edge cases, error handling, alerts and approval steps go in before go-live, not after the first failure.
Hand over
We train your team and document everything. You own the systems; we're available to extend them.
Why WebBox.
We automated our own operation first
Our team came out of running media and placement operations at scale, 9,000+ placements across 1,800+ publisher domains. Running that taught us exactly where hours leak, so we automated our own sales inbox, incident handling and content production before we built for anyone else.
Around your stack
We work with the tools you already use. When something has no connector, we write the integration.
Measured in hours and cost
We cost each workflow before we build it. If it won't pay back, we tell you and suggest something that will.
What clients say.
The quote chaser was the one I was sceptical about, because I assumed clients would find it annoying. They didn't. Two of them replied to the second reminder saying thanks for following up. Our office manager now spends Fridays on supplier accounts instead of chasing.
Questions, answered.
Something else? Book a call and ask us directly.
Related services.
Read before you decide.
Tell us the job your team hates most.
We'll tell you whether it can be automated, how we'd build it, what it would cost to run, and what it would give back.