AI process automation · Microsoft Azure · Data in the EU

Put AI to workon your calls and paperwork.Inside your own Azure.

Your team answers the same questions on the phone all day and types the same fields out of the same documents. Models on Azure can now do most of that reading and sorting, and the data can stay in the EU.

Binitex picks the model, connects it to the systems you already use and builds the steps around it. A person sees only the cases that need one.

Show us one processSee three examples

What runs on Azure

A model alone does not automate anything.

The model reads and decides. The work around it, such as looking things up, filling in records and asking a person, needs its own tool.

  1. 01 Read emails, sort requests, pull out fields, decide the next step

    A model deployed through Microsoft Foundry

  2. 02 An assistant that looks things up and acts in your systems

    Foundry Agent Service, or Copilot Studio

  3. 03 Documents that go to a person for review and approval

    Power Automate with AI Builder

  4. 04 Phone calls: write down what was said and summarise it

    Azure AI Speech, with a Foundry model

Models in the Foundry catalogue

  • GPT-6.1
  • GPT-6
  • GPT-5.x
  • GPT-4.1
  • Claude
  • Llama
  • Mistral
  • DeepSeek
  • Microsoft

What you can use depends on the Azure region and the deployment type. We check it for your region before we plan anything.

Foundry with a .NET or Python backend

Our default. The model connects to your databases, accounts and phone system, and we control every step: what it may read, what it may change, and when it must ask a person.

Copilot Studio

For when your own team wants to change the steps themselves, inside Microsoft 365, without calling a developer. Fewer models to choose from, and simpler to look after.

GDPR

GDPR applies to the whole system around the model.

Microsoft gives you contract terms and technical protections. Your company still decides how personal data is used, and answers for it. What matters most is the deployment type, because it decides where your prompts and the model’s answers are processed.

EU Data Zone Standard / Provisioned

Inside the EU data zone

Standard / Regional Provisioned in an EU region

Inside the Azure geography you choose

Global Standard / Provisioned

Possibly outside the EU, even when the resource is created in Europe

A European resource location on its own does not keep processing in the EU. Not every model supports every deployment type.

What we set up

  • A GPT model on EU Data Zone Standard, under Microsoft’s Data Protection Addendum
  • Your database, document storage and logs in the EU as well
  • Claude and other partner models checked one by one. A listing in Foundry does not mean the same privacy terms
  • Access rights and how long each kind of data is kept, written down
  • The technical part of a data protection impact assessment, where the law asks for one

GDPR does not always require hosting in the EU. Keeping processing in the EU makes residency questions from your clients and lawyers easier to answer.

Examples

Three processes, and how they run on Azure.

Each one starts the same way: one process that eats hours every day, with a clear rule for when a person has to step in.

Staffing · Latvia and Scandinavia

VLAVI · call centre

Before
Candidates and workers abroad call about vacancies, documents, shifts and pay, in several languages. Recruiters took every call, wrote it up and passed it on by hand.
What we built
Each call is written down and summarised while it happens. The model sorts it by reason, updates the candidate’s record and answers routine questions from the company’s own rules. Calls about pay or disputes go straight to a person, with the summary attached.
What changed for the business
Recruiters spend their day with candidates and clients instead of the call log.
  • ~60%of routine calls closed without a recruiter
  • 6 min → 0of note-taking after each call

Azure AI Speech · GPT in the EU Data Zone · call records kept in the EU

How the call system works

Example · Insurance

Insurance claims that arrive by email

Before
Claims came in as emails with photos and scanned forms. Two people retyped them into the claims system and chased customers for missing details.
What we built
A model reads the email and its attachments, fills in the claim and checks the policy number. Power Automate sends anything unclear to an adjuster for approval, and asks the customer for what is missing.
What changed for the business
A claim is registered the same morning it arrives, and adjusters only see the ones that need judgement.
  • 4 h → 15 minto register a claim
  • 70%of claims entered with no retyping

Foundry · Power Automate with AI Builder · documents stored in the EU

Example · Car rental

Booking changes at a car rental company

Before
Customers wrote in to change dates, add a child seat or ask where their deposit was. Each request waited in a shared inbox until the next working day.
What we built
An assistant reads each request, looks up the booking and makes the change itself when the rules allow it. Refunds above a set amount and anything unusual go to a person, already filled in.
What changed for the business
Customers get an answer within minutes, at night and at weekends too, and the support team keeps the same size through the summer peak.
  • Next day → 2 minfor a first answer
  • 55%of requests closed with no one touching them

Foundry Agent Service · .NET backend · booking system unchanged

Indicative figures. The insurance and car rental cases are examples built from our past projects, not named clients.

Want agents that change data in your business system on their own, each limited to its own actions and data? How we roll out AI agents

How we start

One process, measured before and after

  1. 01

    Pick one process

    We sit with the people who do the work, count the hours it takes and agree when a person must stay in charge.

  2. 02

    Build a pilot on your Azure

    In your own Azure subscription, on EU Data Zone deployment, with real but limited traffic. Usually four to six weeks.

  3. 03

    Measure

    Hours, errors and waiting times against the numbers from step one. If the pilot does not pay back, we tell you.

  4. 04

    Scale and look after it

    We switch it on for everyone, watch the answers the model gives, and update the model when a better one becomes available in your region.

Why Binitex

We have built systems that cannot stop for a day since 2007.

2007Year we started
200+Systems delivered
EURiga and Daugavpils, Latvia

Our developers work with .NET and Python every day and use AI on their own work too. The hard part of these projects is connecting the model safely to a booking system, a call centre or a claims database that is already running. We have done that many times.

What’s next

Tell us which process eats the most hours.

We will tell you whether AI on Azure can take it over, which model and deployment type fit, and what the pilot would cost.

Describe one process

Sending your message…Thanks. We will come back to you within one business day.We couldn't send your message. Please try again, or email us directly.

A person reads it and answers within one business day.

Prefer to talk?

Book 30 minutes with our CEO. Bring the process, we will bring the questions.

Book a 30-minute call

Riga · Daugavpils, Latvia