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AI that works for you

We turn documents and data into useful answers. We integrate AI agents and automations that reduce repetitive tasks and connect your applications.

What You Get

We cut down on repetitive work for a specific process in your company. This might mean document processing, searching through an archive, preparing data, or controlled operation of an application.

We start with a clear problem, measure the time, volume, and error rate, then build a pilot using real data. We only expand the solution if the results justify the investment.

We offer solutions we’ve used and validated in our own workflows. We have experience with document processing assisted by human verification, semantic search, and secure, controlled AI agent access to applications.

Where It Helps in Practice

  • Document Processing

    We convert PDFs, scans, and forms into structured data. The system identifies page structure and extracts text and images, with results validated by an operator in a review interface.

  • Searching your data

    We enable natural language search across archives, documentation, and catalogs, showing which files the answers came from. Semantic search can find information even if it's phrased differently from the question.

  • Agents working in your systems

    We connect your applications to AI assistants via Model Context Protocol, with explicitly allowed operations and clear access limits. The agent can only read or change authorized information, and all actions are recorded in a log.

  • Automations with human validation

    We automate classification, completion, and content prep, while keeping a human verification step before publishing or sending. The system processes the bulk, and people validate the decisions.

Our Approach

People stay in the loop. We don’t let a model make decisions on its own when a mistake could have serious consequences. We automate mechanical steps, keeping decisions and validation with people via interfaces that make checks quick and easy.

Your data can stay on your own infrastructure. When your information can’t be sent to an external provider, we can run models locally—on our infrastructure or yours. Otherwise, we use external services if they provide better results and lower costs.

We start with a measurable pilot. We pick a single workflow and set upfront success criteria: time saved, errors avoided, or volume processed. We only expand the solution after validating results.

We verify the results. Outputs undergo automated quality checks and are kept in a versioned log, so you can see what changed and who approved it.

What We Work With

We use locally run models via Ollama, external AI services when appropriate, RAG systems with multilingual semantic encoders, models for analyzing document structure, and Model Context Protocol for controlled agent access to applications.

Our technical infrastructure can include Python and FastAPI, Node.js and TypeScript, PostgreSQL, asynchronous workflows with processing queues, containers, and infrastructure managed by us.

We choose technology according to the problem. If a workflow can be handled more safely and cheaply with clear rules, without an AI model, we’ll recommend that option.

Who it’s for

For companies that process significant volumes of documents or data and can identify where time is being spent. For organizations with archives or documentation where information is hard to find. For teams that want to connect their apps to AI assistants, with controlled access and auditable actions.

Who it's not for

If the process isn't clearly defined yet, we clarify it before automation. Technology on its own doesn’t fix a flawed workflow—in fact, it can amplify the problems.

We don’t recommend implementing a chatbot or an AI function just for image purposes. The solution needs to solve a measurable problem. We also don’t build systems that make major decisions autonomously, without control rules and human validation.

How much does it cost

It depends on the workflow and how clean your starting data is. We can’t give you a number until we see what you’re processing and at what volume.

Here’s how we start: a conversation where you show us the workflow, then a pilot proposal with measurable objectives, deliverables, estimated hours, and price. If the pilot doesn’t meet the target we set together, we’ll tell you and won’t continue just for the sake of it.

On top of the development cost, there may be running costs: external AI services or resources for local models. We show these separately, so you’ll know the monthly cost after launch.

Billing is done in RON; amounts in EUR are converted at the exchange rate of the BNR on the invoicing date.

How we work

Pilot first, expansion after.

  1. 1. Pick the workflow

    You show us where time is being spent. We pick a single process together—important enough to achieve a relevant result, and well-defined enough to implement.

  2. 2. Define how we measure

    Before we write any code, we define what success means: time saved, volume processed, and error rate. We use indicators you can check after the pilot.

  3. 3. Build the pilot

    You run the pilot on real data, with human verification active. We analyze the results and identify where the system needs improvement.

  4. 4. Decide together

    We compare the result to the agreed objective. If the investment is justified, we expand the solution and launch it in production. If not, we stop and document the reasons.

Show us a workflow