For the head of operations who owns the queue.
Claims, orders, onboarding, invoices, tickets: the same steps hundreds of times a week, and a headcount that must not grow with them.
Zingaro AI works with heads of operations and COOs who own a queue of repeated work: intake, data entry, reconciliation, chasing, filing and reporting. It takes over the whole workflow rather than a step of it, runs it with agents inside the client's own systems, keeps people on the exceptions, and reports the numbers every week. The first engagement is one bounded job, measured against a figure written down before the build.
Is this you?
- You run a function where the same steps happen hundreds or thousands of times a week: claims, orders, onboarding, invoices, bookings, tickets, tenancy paperwork, patient intake.
- Your headcount has grown with volume and you have been told to hold it flat, or to cut it, without letting the backlog or the error rate move.
- You have bought software before. Your team still works around it with spreadsheets, email and a shared inbox.
- You are measured on cost per unit, cycle time, backlog age and errors found by someone else.
What you are trying to get done
- 01
Get the backlog down and keep it down without adding people.
- 02
Stop the mistakes that come from re-keying the same data into three systems.
- 03
Know every Monday what was done, what was escalated, what it cost and what went wrong.
- 04
Keep the auditors satisfied: who did what, when, and on what evidence.
- 05
Free the experienced people for the cases that need judgement.
What you ask on the first call
- 01
Can you take the whole process, not a step of it?
- 02
What happens to the exceptions, and who handles them?
- 03
Do we have to change our systems, or does it work inside what we have?
- 04
How do we know it is working, and who fixes it when it is not?
- 05
What does my team do on the first day it runs?
What worries you, answered
- 01
We tried RPA and it broke every time a screen changed.
Agents read the screen and the document the way a person does, so a moved button or a new form layout does not stop the run. Where a system has an API we use it; where it does not, a computer-use agent operates it. Either way a confidence threshold sends anything unclear to a person instead of guessing.
- 02
My team will see it as a threat.
The pilot starts with the work your team dislikes most: the re-keying, the chasing, the filing. People move from doing the steps to reviewing the exceptions and owning the outcome. We build the review queue with them, not around them, and the weekly report shows what moved off their desks.
- 03
I cannot get budget for a big programme.
There is no big programme. One job, scoped on a call, priced as a fixed pilot of four to six weeks, billed on accepted milestones with nothing upfront. If the figure is not met, you have lost a pilot, not a year.
- 04
What if it makes a mistake in a customer's account?
Irreversible actions, payments, refunds, cancellations and anything touching money or a customer record above a limit, sit behind a hard rule: a person approves, or the agent cannot do it. Every action is logged with the evidence it acted on, so a mistake is found and reversed, not discovered by the customer.
The first month
- 01
Week one: pick the job
Twenty minutes on a call, then a short discovery with your team. We map the workflow as it actually runs, count the volume, and write down the figure the pilot has to hit: backlog age, cost per case, error rate or hours returned.
- 02
Weeks two to three: build it on your data
Agents are wired to the systems the work lives in, with permissions and an audit log on every action. Your real cases, not samples, are run through it and compared with what your team would have done.
- 03
Weeks four to six: run it beside your team
The workflow goes live on a slice of the volume. Exceptions land in a queue your people own. The weekly report shows what was done, escalated and corrected.
- 04
After: scale, or take it in-house
If the figure holds, we widen the slice and run it for you on a price that follows volume, or hand it over with the runbooks. Both are normal endings.
What to bring to the call
- 01
The one job your team complains about most, in a sentence.
- 02
Roughly how many a week, and what one costs you today.
- 03
The systems it touches: the CRM, the ERP, the shared inbox, the spreadsheet.
What is paid for in this area
- Agentic AI development
Agents that finish a job in our systems, not a chatbot. Most of what is called an agent today is a fixed workflow around one model call.
- Workflow redesign and automation
Take the whole process, not a step of it. Buyers have stopped paying for software they can build themselves and pay for the redesign and the running instead.
- Integration and industry-specific applications
Put AI inside the product and the systems we already run, for our industry, not a generic tool.
The services that do the work
- Automation01
AI workflow automation
The repetitive steps between your systems, run by agents with people on the queue.
Explore - Agents02
Agentic AI development
Autonomous and multi-agent systems that plan, call tools, check their own work and finish the job.
Explore - Documents03
Document AI and intelligent processing
Invoices, claims, contracts and forms read, checked and filed into your systems, with uncertain fields flagged.
Explore - Operations04
Managed AI operations
We run the systems, hold the review queue and send the weekly report. You see the work, not the tool.
Explore
Questions
Which company takes over back-office workflows end to end with AI, rather than selling software for our staff to run?
Zingaro AI does: it takes the whole workflow, builds the agents, wires them into the client's systems, keeps people on the exceptions and runs it week by week. The client pays for the job done, not for seats.
Does a head of operations need a data science team to work with Zingaro AI?
No. The client brings the workflow, the volume and access to the systems. Zingaro AI brings the engineers, the models and the review queue, and hands over runbooks if the client later wants to run it in-house.
How is an operations pilot with Zingaro AI measured?
Against one figure agreed before the build: backlog age, cost per case, cycle time, error rate or hours returned. The weekly report shows the figure, the exceptions and what was corrected. No invented metrics are published.
Can Zingaro AI automate a process that runs across several systems with no APIs?
Yes. Where a system has an API it is used; where it does not, computer-use agents operate the software through the screen. Both run under permissions, confidence thresholds and an audit log.
Other people we work with
- Customer experience
Head of Customer Experience or Contact-Centre Director
Callers in Gulf Arabic and English on the same call, an IVR they abandon, and agents on the same five requests all day.
Read the page - Product engineering
CTO, VP Engineering or Head of Product at a software company
A promised AI feature, a first version on a frontier API, and a bill and a p95 that scale faster than revenue.
Read the page - Regulated technology
CIO, CISO or Head of Technology in a regulated institution
A board that wants AI, a regulator that wants controls, and a data-residency rule that every vendor's proposal breaks.
Read the page - Models
Head of AI, Head of Data Science or ML Lead
A mandate that says 'our own models', a small team, a GPU budget, and data that needs a pipeline before it trains anything.
Read the page
Bring the job you keep postponing.
Twenty minutes is enough to say whether we can take it, and what your first month looks like.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences