What does Neovara do?

Neovara is an AI & data partner for F&B, hospitality and other ambitious SMEs. We build and run AI agents that do the work — Agentic as a Service — on top of a clean, structured data foundation you can actually trust. Where it's useful, we run the back-office too.

What is GaaS, or Agentic as a Service?

GaaS stands for Agentic as a Service. With software — SaaS — you buy a tool and your team does the work inside it. With GaaS you get working AI agents that do the job itself: researching, processing, reporting, updating your systems. They're built around how your business already runs, and we manage them. You're buying the outcome, not the licence.

Do you specialise in F&B and hospitality?

Yes. F&B and hospitality is our home turf: we built and proved the model with fine-wine merchant Cru Wine — back-office costs down 65% and a record profit year. We also work with SMEs beyond F&B.

How do we start, and how do you price?

Start with the AI & Data Health Check: a fixed-price, two-week diagnostic that scores your data, maps your back-office costs and ranks your top five AI opportunities in AED. Or book a free 20-minute discovery call first. Everything after that is scoped to your business, not to a package. No long-term lock-in.

Do we need to replace our existing systems?

Usually not. Most of the value comes from connecting and cleaning what you already have, then putting agents on top. If something genuinely does need replacing we'll tell you — but that's rarely the first move, and it's never the starting point.

Where is your team based?

We deliver from four hubs: the UK, Philippines, India and Dubai.

What is the difference between AI enablement, data architecture and back-office support?

AI enablement gets your team using AI effectively; data cleaning and architecture builds a single source of truth you can trust; back-office support is our team running finance, IT, customer service, marketing and admin for you.

Do you only work with F&B and hospitality?

No — F&B and hospitality is where we go deepest, but we help any SME growing fast on messy data and manual back-office.

Is your restaurant's data ready for AI?

Is your restaurant's data ready for AI? Five checks you can run this week

Every AI pitch starts with the clever part: the agent, the dashboard, the forecast. Almost nobody asks to see your data first. That's the wrong way round. An AI tool built on a POS export that doesn't match Xero will give you wrong answers, faster and with more confidence.

So before you sit through another demo, spend a few hours with your own numbers. Here are five checks any restaurant, café or hotel F&B team can run this week, with nothing more than system exports and a spreadsheet.

The short answer

our restaurant's data is ready for AI when your systems agree with each other, things are named the same way everywhere, you know where your back-office hours go, you can trace a dish from invoice to plate, and someone owns each data source. Fail two or more of the five checks below and the first job is fixing the data, not buying a tool.

1. Do your systems agree on yesterday's sales?

Pick one normal trading day. Pull net sales for one outlet from three places: the POS, each delivery app's report (Talabat, Deliveroo, Careem) and Xero or whatever you close the books in.

The numbers won't match to the dirham, and that's fine. Commissions, refunds and timing all get in the way. The test is whether someone on your team can explain every gap within 15 minutes. If the answer is "we sort it out at month-end", you've found your first problem.

2. Is everything called the same thing everywhere?

Open your supplier list and search for one ingredient. Count how many ways it's been entered. Do the same for outlet names across the POS, the inventory system and the chart of accounts.

AI matches things by name and code. If one outlet is "JBR", "JBR Walk" and "Outlet 3" depending on the system, every report that joins them up needs a person to translate first.

3. Where do your back-office hours actually go?

For one normal week, ask your finance and admin team to note time spent on four jobs: keying invoices, reconciling delivery payouts, building reports and chasing missing information.

Most teams have never added this up. Once you have, you know which job is worth automating first, and roughly what it costs you every month to leave it as it is.

4. Can you trace one dish from invoice to plate?

Choose your best-selling dish. Can you go from the supplier invoice price, to the recipe cost in your inventory system, to the menu price and sales in the POS, without retyping anything?

If you can, you can compare what your food cost should have been with what it actually was, and find out where the difference went. If you can't, food cost stays a number you see once a month, too late to act on.

5. Who owns each data source?

List every system that holds business data: POS, delivery apps, inventory, accounting, reservations, HR and the spreadsheets that fill the gaps. Next to each, write who owns it and who can export from it.

Watch for the single spreadsheet only one person understands, and for logins that sit with an old employee or an agency. This also matters for compliance. UAE businesses with revenue under AED 50M must appoint an e-invoicing Accredited Service Provider by 31 March 2027 and go live on 1 July 2027 (KPMG). Clean, owned invoice data makes that a much smaller project.What your score means

Where AI fits once the data is right

The checks above are also a map of where AI pays off. Payout reconciliation, invoice matching, leakage alerts on voids and refunds, plain-English answers about your numbers: each one gets easier once the data underneath is consistent. Fix the foundations and the useful AI work becomes the easy part.

If you'd rather not run it yourself

These five checks are a short version of what we look at in our AI & Data Health Check. It's a two-week review in which we go through your systems, partly using AI to test the data itself, and hand back four things: a data health score, a map of where your back-office cost goes, the five AI or automation opportunities most worth doing, and a 90-day plan.

Run the checks yourself first. If you get stuck on one, or the results raise more questions than answers, we're happy to talk them through.

Talk through your results with us

Check →

FAQ
How do I know if my restaurant's data is ready for AI?
Check whether your POS, delivery apps and accounts agree on sales, whether outlets, items and suppliers are named consistently, and whether you can trace a dish's cost from invoice to sale. If two or more of those fail, fix the data before buying a tool.
What does it take to fix restaurant data?
It depends on how many systems you run and how far they've drifted apart. Matching names and codes across systems and setting up a daily reconciliation are the usual starting points.
Do I need new systems to get my data ready?
Not necessarily. Check how your existing systems are set up and connected before you replace any of them.