Services

Automation and artificial intelligence

AI agents and document recognition take over the repetitive work in a process: they read letters, answer questions from internal rules and put reports together. After launch we measure quality and maintain the models.

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Scopes

Scopes of this service

  1. Artificial intelligence implementations

    An AI agent takes over the stage in your system that consumes the most manual work and prepares a decision with the documents it relied on; one of your employees approves it.

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  2. Natural language processing

    A program that reads your company's scans, e-mails and internal rules, turns them into data and answers that cite their source, and hands uncertain cases to a person.

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  3. Management analytics and reporting

    Board reports calculated from the company's systems according to definitions agreed with you, with an alert when something deviates and a named owner for every figure.

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  4. Data clean-up and data flow

    One agreed dataset on customers, products and sales, fed from your systems by a data flow that shows whether the data has arrived, ready for reports and AI models.

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  5. Looking after AI after launch

    Your agent or model looked after once it is live: answer quality checked on current samples, usage costs under control, and a decision log ready for an audit or a complaint.

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Situations

What clients come to us with

Documents retyped into the system by hand

Invoices, contracts and letters arrive as scans, and someone retypes their content into the system before the case can move. The cost of this work grows with the number of documents.

Answers looked up by hand in rules and procedures

Staff look for answers in internal rules and procedures, and the response time depends on whether someone knows the document. When the rules change, every department starts searching again, at the cost of people's time.

A board report put together by hand

Customer and campaign data sits in several systems, and one person assembles the report for the management board. The decision waits for the report, and the report waits for that person.

Scope

What we do

  1. An AI agent in your process

    We build an AI agent, a program that takes over the stage with the largest share of repetitive work: it reads a document, finds the answer in internal rules or contracts, and prepares a decision for approval. It works in the system your staff already use, and every answer points to its source documents.

  2. Document recognition

    We turn scans of invoices, contracts and letters into data without retyping. The result is a queue in which a person checks only the uncertain cases.

  3. Management analytics and reporting

    We combine customer and campaign data into a single source and build reports for the management board on it. The result is a set of reports refreshed automatically from that source.

  4. Model maintenance after launch

    After launch we measure the quality of the answers and improve the model when the data or the regulations change. The result is a maintenance plan with review dates.

Technologies and standards: Language models, AI agents, OCR, ETL, real-time telemetry, MLOps.

Outcome

What you get

  • A recommendation with a scope: which stage we automate first and what changes in it
  • An automation working every day in your system, with documentation
  • A record of the agent's work: the source documents behind each answer, open to the auditor
  • A maintenance plan: who is responsible for the automation and when we check its quality

Process

How the work proceeds

  1. We analyse the process

    In the analysis we break the process down into stages and show where people do repetitive work. Result: a list of stages to automate, ranked by the share of that work.

  2. We test on a sample of data

    We run the model on a sample of documents or queries from your process and show the answers to the staff who do this work today. Result: a decision on implementation based on your data, before the full project starts.

  3. We implement and maintain

    We build the automation into your system, with answers recorded and approved by a person; after launch we measure quality and cost. Result: the automation in daily use and a person on our side responsible for it.

Regulations

Regulations and scope limits

If the model reads personal data or supports a decision about a customer, data protection and artificial intelligence law applies; when the model is a medical device, FDA, CE and MDR requirements are added. We write these requirements into the design from the start: a record of the source documents behind each answer, approval by a person, data in the European Union or on your servers.

What we don't do

  • Promises of savings before the process has been analysed.
  • An agent whose answers do not point to a source document.
  • Decisions made by a program: the model prepares the decision, and a member of your staff approves it.

Work

Team experience in this area

Situation

A financial-sector company received letters and documents in debt collection that had to be read and assigned to cases by hand.

What the team did

Our team implemented document recognition: the system reads letters, identifies their type and passes the data to the ERP and CRM systems.

Outcome

Letters from debtors reach the right cases without manual re-keying.

How we start

  1. A conversation about the problem

    You describe the process or system that no longer keeps up and what has to change. The conversation costs nothing.

  2. Analysis under an NDA

    We examine the process and the systems and hand over a recommendation with a scope of work. You know the scope, deadline and price of the analysis before it starts.

  3. Offer

    You receive the scope, team, schedule and price of the implementation. You decide whether we go ahead.

The cost of the analysis is credited towards the implementation if you order it.

Let's talk about your project

Phone: +48 455 406 404 E-mail: contact@aliaxit.com

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