Five industries worked hands on, so we arrive knowing where the gaps usually are.
All industries →Request a demoFour ERP platforms in house, and fifteen capability modules. The recommendation is never tied to a licence quota.
All solutions →Request a demoOne team from platform selection through to the reports on your desk.
All services →Request a demoGuides, how-tos, comparisons and a plain-English glossary, written by the consultants who deliver the projects. No form in front of any of it.
Nobody in UAE ERP publishes their tools or their pricing. We do both.
Free tools →Request a demoAn ERP and IT consultancy in Business Bay, Dubai, implementing Oracle, Microsoft, Odoo and SAP across the UAE and GCC.
Consultants in the UAE, technical team in South Asia. Cost stays sharp without cutting corners.
About QZ Infomatics →Request a demoAI that solves a real problem in your business,
not just something that looks good in a demo.
QZ Infomatics builds AI into systems UAE businesses already run. Not a separate dashboard that gets opened twice and forgotten, but automation and prediction wired into the ERP, CRM and workflow tools your team uses every day — an invoice that processes itself into the ledger, a reorder suggestion that appears in the purchase screen, or a churn score that reaches the salesperson before the renewal call rather than after it. The work spans process automation, demand and financial forecasting, document processing, customer analytics and custom model development.
What we build
Most AI projects fail for the same reason most software projects fail: nobody defined the problem before choosing the technology. Five areas cover almost every AI request we receive from UAE businesses, and each one starts from a task somebody currently does by hand.
We identify the rule-based, repetitive tasks that consume your team's time and automate them, so people focus on work that actually requires judgement.
We build machine learning models on your historical data that forecast demand, flag supply chain risk, predict customer churn and surface revenue opportunities before they show up in your standard reports.
Some of this is configuring proven tools. Some of it is building something specific to your data. We will tell you which one your problem needs, including when the honest answer is that you do not need AI at all. Vision-based products, where a camera is the sensor, are a separate line of work with a page of their own. Vision-based products →
03 — Read
We use AI to extract and process information from unstructured documents — invoices, purchase orders, contracts, delivery notes and compliance certificates — directly into your ERP. Less manual entry, fewer errors, and a business case you can calculate before anyone builds anything: count the supplier invoices your finance team keys in each month and multiply by the minutes each one takes.
Renewal dates, notice periods, penalty clauses and obligations pulled out and tracked, rather than sitting in a PDF nobody reopens until it is too late.
Purchase order, goods receipt note and invoice reconciled automatically, with only the mismatches routed to a person.
Certificates, licences and statutory documents classified, filed and archived against the record they belong to.
Optical character recognition checked against what the value ought to be, so a misread total is caught rather than posted.
Structured data pulled from documents that arrive in a different layout from every supplier, without a template per supplier.
Modern extraction does not need a template per supplier, and confidence scoring routes only the uncertain fields to a person for checking — so accuracy improves as the system sees more of each supplier's format. Invoice and document processing →
04 — Understand
Tools that help your sales and customer service teams understand, predict and serve customers more effectively, using voice analytics, behavioural pattern recognition and intelligent communication profiling.
This is where the UAE context matters. Call floors here handle Arabic, English, Hindi, Urdu and Tagalog on the same shift, and most sentiment tools depend on transcription and therefore on language. Acoustic voice analytics reads how someone speaks rather than what they say, so one model covers every language on the floor.
A signal is only worth generating if somebody acts on it. Scores are written back into the CRM record the salesperson already has open, and coaching points attach to the call they came from rather than arriving in a monthly summary.
05 — Build
Where no existing product fits, we build and train models on your own data. This is the smallest part of the work by volume and the largest by commitment, so it starts with a proof of concept on a single process rather than a full build. Data availability and quality determine the timeline far more than the modelling itself does.
Your transaction history, your product catalogue, your customers. The advantage of a custom model is not that it is cleverer, it is that it has seen the thing you actually sell.
That last item matters more than it looks. A model that was accurate at launch drifts as your business changes, and a forecasting tool nobody retrains quietly becomes a forecasting tool nobody trusts.
Our approach
They are the reason we turn work down, and the reason the projects we take on tend to survive their first year.
We define the business problem before selecting the AI approach. Technology follows the challenge, not the other way around. If the process is low volume, unpredictable, or has no clear right answer, we say so — automating it would make the inconsistency faster rather than fixing it.
AI insights sitting in a standalone dashboard do not get acted upon. We integrate AI outputs into the ERP and workflow tools your team already uses every day, so a prediction appears at the moment of the decision it is meant to inform.
Every AI engagement is defined with clear success metrics before deployment, so you can measure exactly what value the solution is generating. Agreed at scoping, reviewed after go-live.
Governance
Anything touching customer, employee or financial data needs a clear position on three questions. UAE data-protection obligations and any sector-specific rules apply to an AI deployment exactly as they do to your ERP.
Which service handles the data, in which region, and whether anything leaves the country. Named in the scoping document rather than discovered in a subprocessor list later.
What is stored, for how long, and whether your data is used to train anything beyond your own model. The answer should be written down before a proof of concept starts.
A prediction about a customer or an employee is itself sensitive. Access to the output needs the same controls as access to the data that produced it.
We agree all three at scoping, before a proof of concept begins rather than after, including any data-residency constraint you have to work within.
AI FAQs
AI solutions apply machine learning and language models to specific business problems: extracting data from documents, forecasting demand, classifying and routing requests, answering customer questions, and flagging anomalies in transactions. The useful ones automate a task that currently consumes staff hours. Vision-based products are covered separately.
The consistently successful ones are document processing for invoices and delivery notes, demand and inventory forecasting, customer support automation in Arabic and English, sales lead scoring, and anomaly detection across expenses and procurement. Each has a measurable before-and-after, which is what makes them fundable internally.
Look for volume, repetition and a clear right answer. A task performed two hundred times a month against a consistent rule set is a strong candidate; a judgement call made twice a quarter is not. Volume multiplied by time saved, weighed against build and running cost, gives the answer.
Yes, and that is usually where the value sits. ERP data is structured, historical and already owned by you — good input for forecasting, classification and anomaly detection. Integration reads from the ERP and writes results back into it, so output appears where staff already work rather than in a separate tool.
A product solves a defined problem the same way for every customer and costs less. Custom development fits your data, workflow and systems, and earns its cost when the process is specific to your business. Many projects combine both: a product for the general task, custom work for the integration.
Cost depends on whether the work is configuring an existing model, building an integration around one, or training something on your own data. A focused document-processing automation is modest; a bespoke model with data pipelines and monitoring is a larger commitment. Starting with a proof of concept is usually cheaper.
A proof of concept on a single process takes three to six weeks. Moving it into production, with error handling, monitoring and user access controls, typically adds another six to twelve weeks. Data availability and quality determine the schedule far more than the modelling work does.
Anything touching customer, employee or financial data needs a clear position on where processing happens, what is retained and who can see the output. UAE data-protection obligations and any sector rules apply exactly as they do to your ERP. Agree this before the proof of concept, not afterwards.
Talk to a consultant
Book a free 30 minute call with QZ Infomatics in Dubai. You will leave it with a platform recommendation, the reasoning behind it, a realistic timeline and an indicative budget band — before you commit to anything.
