AI we build for production

What it is

An AI agent is a language model that does not stop at an answer. Given a goal, it plans the steps, calls your systems through their APIs or the Model Context Protocol (MCP), reads what comes back and decides what to do next. Agentic automation puts that loop to work on processes where people today copy, check and re-type data between tools.

Typical use cases

• Triage of incoming requests: read the email or ticket, gather context from your systems, route it and draft the reply.
• Back-office routines that span several systems, such as order entry, invoice matching or data clean-up.
• Research and preparation: a briefing, a quote or a report compiled from internal data and public sources.
• An operations assistant that checks status across your tools and opens the right task when something is off.

How we deliver it

We start with one process and map every step, tool and decision a person makes in it today. The agent gets only the permissions it needs, every action is logged, and anything that sends, pays or deletes waits for a person's approval. We test it on real cases before it touches live data, then widen its scope step by step.

What it is

A knowledge assistant answers questions in plain language from your own material: manuals, contracts, policies, tickets, wikis and databases. It works by retrieval-augmented generation (RAG): for every question it first finds the relevant passages, then writes the answer from them and links to where each statement comes from. When your sources do not cover a question, it says so instead of guessing.

Typical use cases

• An internal help desk that answers staff questions about processes, products and policies.
• Support agents who get a drafted answer with the matching manual pages and past tickets beside it.
• Search across contracts and technical documentation that understands meaning, not just keywords.
• Onboarding for new colleagues, who can ask the assistant what they would otherwise ask a senior teammate.

How we deliver it

We connect the sources you choose and keep them in sync, so answers follow your latest documents. Access rules travel with the data: people only get answers from documents they are allowed to open. Before launch we build a test set of real questions with expected answers, and we measure accuracy and citations on it after every change.

What it is

Document processing turns the paper, scans and PDFs that arrive every day into clean data your systems can use. Current language and vision models read documents in many layouts and languages, find the fields that matter and check them against your rules, without a hand-made template for every supplier or form.

Typical use cases

• Supplier invoices read, matched to orders and posted to the ERP for approval.
• Contracts sorted by type, with parties, dates, notice periods and unusual clauses pulled out for review.
• Application forms and identity documents checked for completeness before a person picks them up.
• Incoming mail and attachments classified and sent to the right team or system.

How we deliver it

Every extracted field carries a confidence score. Clear cases flow straight through, and uncertain ones go to a person, who sees the document and the proposed values side by side and confirms or corrects them. Those corrections become test data, so accuracy is measured, not assumed. Results land in your ERP, CRM or document system through their APIs.

What it is

Chat and voice agents hold a real conversation in your customers' language, Slovak included. They answer from your own knowledge, look up orders or bookings in your systems and complete simple tasks on the spot. When a request needs a person, they pass on the whole conversation, so nobody has to repeat themselves.

Typical use cases

• A website or in-app chat that answers product and order questions at any hour.
• A phone line that handles routine calls, such as bookings, changes and status checks, and passes the rest on.
• Lead qualification that asks the right questions and books a meeting with your sales team.
• A voice assistant that lets field staff log work or check stock without typing.

How we deliver it

We design the conversation around your real requests, starting from transcripts and emails you already have. The agent knows its limits: it says what it can do, never invents prices or commitments, and hands over to a person on request or when it is unsure. Every conversation is logged, so your team can review it and we can improve the agent from what it shows.

What it is

These are AI features built into software your customers or staff already use, not a separate tool beside it. A copilot drafts and summarises in context, search understands what people mean, matching pairs records that belong together, recommendations suggest the next step, and generation writes text or prepares images. Each feature works with your app's own data and respects its permissions.

Typical use cases

• A copilot in a business application that drafts replies, summaries and documents from the record on screen.
• Semantic search across products, tickets or listings that finds results by meaning.
• Matching of demand and supply, such as candidates to jobs or buyers to offers, with the reasoning shown.
• Recommendations for the next action in a CRM or a customer portal.

How we deliver it

We build the feature into your existing product, front end and back end, and keep the model behind an interface so it can be swapped later. Users see why the AI suggests something and decide what is saved or sent. We have used this pattern in a product we developed.

In production AIRAmax, which we developed: matching with a score and its reasoning, and Ask AIRA answering from the agency's own data

What it is

Predictive analytics uses machine learning on your historical data to estimate what is likely to happen next: how much you will sell, which customers may leave, which transactions look unusual and which price fits a deal. These models work on numbers and events rather than text, and they are often small, fast and inexpensive to run. Their value comes from careful data preparation and from putting each prediction where a decision is made.

Typical use cases

• Demand and sales forecasts that feed purchasing, staffing or production planning.
• Churn signals that tell account managers which customers to call first.
• Anomaly detection in transactions, sensor readings or system logs, with alerts that explain what looks wrong.
• Pricing suggestions based on comparable deals and current demand.

How we deliver it

We start with a data audit: what you have, how clean it is and whether it can answer the question at all. We compare a simple baseline with machine learning models and keep the complex one only if it clearly does better. The model runs as a service or a scheduled job, shows its predictions in your tools and is monitored, so a shift in your data is noticed before the predictions drift.

What it is

Computer vision lets software understand images and video: recognise objects, read labels, spot defects and compare what it sees. Generative media works the other way round and creates or edits images, such as enhancing a photo or furnishing an empty room. Both now run on models that can be adapted to your products from a modest set of examples.

Typical use cases

• Visual quality inspection that flags defects on a production line or in incoming goods.
• Recognition of products, documents or damage in photos taken by staff or customers.
• Photo enhancement for listings and catalogues: light, colour, perspective and clean-up.
• Virtual staging and image variants for marketing, created when someone asks for them.

How we deliver it

We collect sample images from your real conditions, label them with your team and measure the model on cases it has not seen. Inspection results reach a person or a system together with the part of the image that triggered them. For generative edits a person decides what changes: enhancement improves what the camera captured, and staging is never applied on its own.

In production AIRAmax, which we developed: photo enhancement in AIRA Studio, and virtual staging that never switches itself on

What it is

Not every company can or wants to send its data to a public AI service. Open-weight models can be downloaded and run where you choose: on your own servers, in your data centre or with an EU cloud provider. For many business tasks a well-chosen open model, adapted to your domain, does the job, and you pay for hardware or fixed hosting instead of paying per request.

Typical use cases

• Assistants and document processing for data that must not leave your network, such as health, legal or financial records.
• An internal AI platform that several teams use through one secured interface.
• A model tuned for one narrow task, such as sorting requests into your own categories.
• A hybrid set-up that keeps sensitive steps in-house and sends only non-sensitive work to a hosted model.

How we deliver it

We size the hardware or cloud capacity to your workload, set up model serving with access control and logging, and compare open models on your own tasks before you commit to one. You get the deployment, the configuration and the documentation, so your team can run it, or we keep running it with you.

What it is

AI coding agents write code fast, but speed alone does not make software you can rely on. So we use them inside a fixed process. Every change starts as a written spec with acceptance criteria, agents implement it test-first, a separate verification step checks every criterion, and review by more than one model catches what a single model misses. People decide what is built and approve what ships.

Typical use cases

• New web and business applications delivered as a series of small, tested and reviewable changes.
• Modernising an older system: documenting what it does, covering it with tests, then changing it safely.
• New features in an existing codebase, with its tests and documentation kept up to date.
• The same spec, test and review pipeline set up for your own development team.

How we deliver it

You see the work as it happens: specs, tests and review findings are files in your repository that anyone on your team can read. This website was built the same way, from a written spec through tests to a verified build, with every design decision recorded next to the code.

What it is

Before building, it pays to know where AI will help and what it will demand of you. We help you find and rank use cases by value and effort, check whether your data and processes are ready, and classify each system under the risk categories of the EU AI Act. For systems already in use, we set up evaluations, guardrails and security testing, prompt injection included, and align data handling with the GDPR.

Typical use cases

• A workshop that turns a long list of AI ideas into a short, ranked roadmap.
• An inventory of the AI tools and systems already in use, each with its risk class and owner.
• Internal rules for using AI tools safely, written for the people who use them.
• Security testing of an AI assistant or agent against prompt injection and data leaks.
• Preparation for the obligations of the AI Act, such as documentation, transparency and human oversight.

How we deliver it

We work in short engagements with a fixed scope, together with your management, IT and legal staff, and leave you with documents you can use: a roadmap, a risk register, internal policies and test reports. We are engineers, not lawyers, so on legal questions we work alongside your legal advisers.