PragaWeb

Integrations, APIs and automation

Applied AI

In short

Applied AI puts AI models to work on concrete business tasks, such as assistants for your documents, data extraction from invoices and delivery notes or email sorting, with human oversight and GDPR compliance.

Talk about your project

Applied AI is the use of artificial intelligence models to carry out specific tasks within your company’s processes: answering questions about internal documents, extracting data from invoices and delivery notes, sorting emails, helping website visitors find information. It is for businesses that want measurable results on real work, not experiments for their own sake.

PragaWeb builds AI into the software and workflows you already use, with a careful approach: we start from a concrete problem, check that AI solves it well on real data, keep a person in charge of the important steps and respect European privacy and AI rules.

When applied AI is genuinely useful

AI works best on repetitive tasks that involve reading or understanding text, where someone currently spends time searching, copying or classifying. Some signs:

  • your team spends hours looking for information in manuals, procedures, contracts or old emails;
  • invoices, delivery notes and orders arrive as PDFs and someone retypes the data into your management system;
  • a shared inbox receives many different requests that need sorting;
  • customers keep asking the same questions by phone or through the website;
  • searching your archive only works if you guess the exact word.

If the task involves sensitive decisions or demands absolute accuracy with no checks, AI is not the right choice, and we will tell you so.

What we build

  • Assistants for company documents: an assistant that answers your team’s questions based on manuals, procedures and technical sheets, always showing the source of its answer.
  • Data extraction from invoices and delivery notes: automatic reading of PDFs or scans to pull out supplier, line items, quantities and amounts, with a review screen before anything enters your management system.
  • Email classification: incoming messages are recognised (order, complaint, quote request, admin) and assigned to the right person, with a draft reply to review.
  • Website chatbots: an assistant that answers visitors using only content you approve, states that it is an automated system and hands over to a person when needed.
  • Semantic search: a search engine that understands the meaning of a question, not just its words, for document archives, catalogues and knowledge bases.

How we work

  1. Choosing the use case: we identify one specific task with a measurable outcome (time saved, fewer errors, requests handled).
  2. Data review: we check which documents and data are needed, where they live, who can access them and whether they contain personal data.
  3. Prototype: we build a first version and test it on real examples, measuring how often the AI gets it right.
  4. Integration: we connect the solution to your management system, email or website, with human review steps where needed.
  5. Monitoring and improvement: we collect users’ corrections and refine instructions and data over time.

Privacy, GDPR and the AI Act

Using AI with business data needs clear rules. That is why we:

  • choose model providers that contractually do not use your data to train their models and, where possible, process data within the European Union;
  • consider open-source models running on dedicated servers when the data is particularly sensitive;
  • keep the data sent to a minimum and help you prepare the required privacy documentation together with your adviser;
  • take into account the AI Act, the EU regulation on artificial intelligence, which includes transparency duties: people talking to a chatbot must know they are not talking to a human;
  • always keep human oversight on decisions that affect customers, suppliers or staff.

Technologies

We work with large language models (LLMs, the models that understand and generate text) from different providers as well as open-source models, choosing on quality, cost and privacy requirements. For document assistants we use RAG (Retrieval-Augmented Generation): the system first finds the relevant passages in your documents and then answers on that basis only, which reduces made-up answers. For semantic search we use vector databases, which store the meaning of texts.

Timing and costs: what they depend on

  • how complex the use case is and how many documents are involved;
  • the quality of the source data (readable scans, up-to-date documents);
  • the integrations needed with your management system, email or website;
  • whether you use pay-per-use cloud services or models on dedicated servers;
  • usage volume, which affects the recurring cost of the models.

A prototype for a well-defined use case usually takes a few weeks; from there you decide whether and how to extend it.

Why choose PragaWeb

  • A practical mindset: we start from your processes, not from the latest trend, and we measure results.
  • One point of contact for the project, the integration and ongoing support.
  • Your data and code stay yours, with no lock-in to a single AI provider.
  • End-to-end integration: AI works best inside well-designed workflows, so we combine it with process automation and API integration with your software.

Want to know whether AI can help with a specific task? Describe your use case and we’ll assess its feasibility together.

Frequently asked questions

Will my data be used to train the AI?
Not if you choose the right services. We use providers that contractually do not use customer data for training and, for the most sensitive data, we can run open-source models on dedicated servers.

Can AI make mistakes?
Yes, like any system. That is why we measure accuracy on real cases, make assistants cite their sources and keep human review on important steps, such as entering accounting data.

What does the AI Act mean for a business using a chatbot?
One of the main duties is transparency: users must know they are talking to an automated system. We design solutions with these requirements in mind; for specific legal questions we suggest also checking with your adviser.

How much does an AI project cost?
It depends on the use case, the integrations and the usage volume. We usually start with a focused prototype at a defined cost, then assess whether to extend it. Recurring model costs are quoted separately.

Do I need lots of data to get started?
Not necessarily. For a document assistant the documents you already have are enough; for invoice extraction we need a few real examples for testing. We will tell you what to prepare.