AI workflow automation examples, from real work

Each example below says what kind of example it is. Client story means work we did for a real client, with results they reported or agreed. What we build means a system from our service list, described as it usually works, without invented numbers. Prototype means a working demo you can click through on our work page; it is not a client deployment.

Sales and leads

1. Leads from ads and WhatsApp, in one pipeline

The full write-up is on our WhatsApp lead management page.

2. Lead scoring and follow-up that doesn't depend on memory

What we build. The system scores each new lead against the patterns of customers who actually bought, puts the best ones at the top of the list, and keeps routine follow-ups running so no warm lead goes cold because someone forgot. See AI lead generation.

3. Every inbound call answered and booked

What we build. An AI voice agent picks up every call, answers the common questions, works out whether the caller is a real lead, and books them into the calendar or hands off to a person. Each call is transcribed and logged to the CRM. See AI voice agents.

Prototype. Our clinic prototype on the work page shows a voice assistant answering a call and booking it into a doctor's calendar.

Operations and back office

4. A ticketing platform shipped in three days

Not every automation project is a small script. Sometimes the fastest way to remove manual work is a purpose-built tool, which is what custom AI software covers.

5. Invoices read and entered automatically

What we build. The AI reads invoices from email or uploads, pulls out the vendor, amounts, dates and line items, checks its own work, flags anything it is unsure about for a quick human look, and posts the rest to the accounting system. We go through the steps in how to automate invoice data entry.

6. Client onboarding without the copy-paste

What we build. A signed contract triggers the rest: the client record is created in the CRM and billing system, the welcome pack goes out, documents are requested and chased, and the account manager sees a checklist of what is still missing. See AI workflow automation.

7. The Monday report that builds itself

What we build. Instead of someone pulling numbers from five tools into a spreadsheet every Friday, a live dashboard shows the handful of numbers that matter and keeps them current. See BI dashboards.

8. Shop-floor tracking instead of paper job sheets

Prototype. Our manufacturing prototype gives every worker a daily job sheet, tracks production as it happens, and compares planned with actual material use. You can click through it on the work page. Read more on AI for manufacturing.

Customers and knowledge

9. Support questions answered from your own docs

What we build. An assistant trained on your help docs, past tickets and FAQs answers the repeat questions day and night, and passes the full conversation to a person when it needs one. See AI customer support.

10. A nightly list of customers at risk

What we build. The system reads CRM, support and usage data every night and hands the team a short list: who might leave, which deals went quiet, who is ready to buy more, each with a reason. See churn prediction.

11. Company knowledge you can ask a question

What we build. A private assistant that has read your handbooks, SOPs and past projects and answers your team's questions in plain language, with a link to the source. See AI knowledge assistant.

What the good examples have in common

• They start from a problem with a cost. Lost leads, hours of retyping, calls nobody answers. Not "we should use AI somewhere".
• They fit around existing tools. Kamai kept selling the same way; the pipeline replaced the WhatsApp group, not the business.
• A person stays in charge of the risky part. Uncertain invoices go to a review queue. Calls that need a human get handed over.
• Success is a number agreed up front. That is how the Kamai result can be stated as 70% against a 30% target.

How to pick your first one

Write down the jobs your team repeats every week. For each one, note the hours it takes, what goes wrong, and how many tools it touches. The best first project is usually frequent, a bit painful, and touches only two or three systems. If you want a sense of budget, read how much AI automation costs. If the job needs judgment at several steps, read AI chatbot vs AI agent first.