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We turn your documents and data into useful answers. We integrate AI agents and automations that reduce repetitive work and connect your apps.
We reduce repetitive work in a specific process within your company. This could involve document processing, searching through an archive, preparing data, or controlled operation of an application.
We start with a clear problem, measure time, volume, and error rates, then build a pilot using real data. We only expand if the results justify the investment.
We propose solutions we've used and validated in our own workflows. We have experience with document processing assisted by human verification, semantic search, and controlled AI agent access to applications.
We turn 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 verification interface.
We enable natural language search in archives, documentation, and catalogs, showing which documents answers come from. Semantic search can identify information even if it's worded differently than the question.
We connect applications to AI assistants through the Model Context Protocol, with explicitly allowed operations and clear access limits. The agent can only read or modify authorized information, and all actions are logged.
We automate classification, completion, and content preparation, always keeping a human verification step before publishing or sending. The system handles volume, while people validate decisions.
Humans stay in the loop. We don't let a model decide alone when mistakes can have serious consequences. We automate mechanical parts and keep decisions and validation with people, using interfaces that make verification fast.
Your data can stay in your infrastructure. When information can't be sent to an external provider, we can run models locally, on our infrastructure or yours. Otherwise, we can use external services if they offer better results and more efficient costs.
We start with a measurable pilot. We pick a single workflow and define success criteria from the start: time saved, errors avoided, or volume processed. We expand only after validating the results.
We check the results. Outputs go through automated quality checks and are stored in a versioned log, so you can see what changed and who approved it.
We use models run locally with Ollama, external AI services where appropriate, RAG systems with multilingual semantic encoders, document structure analysis models, and Model Context Protocol for controlled agent access to applications.
The technical infrastructure may include Python and FastAPI, Node.js and TypeScript, PostgreSQL, asynchronous workflows with processing queues, containers, and infrastructure managed by us.
We choose technology based on the problem. If a workflow can be solved more safely and economically with well-defined rules, without an AI model, we'll recommend that option.
For companies processing significant volumes of documents or data and able to identify where time is spent. For organizations with archives or documentation where information is hard to find. For teams wanting to connect applications to AI assistants, with controlled access and auditable actions.
If the process isn't well defined yet, we clarify it before automating. Technology won't fix a broken workflow on its own—it might only amplify the problems.
We don't recommend implementing a chatbot or AI feature just for image. The solution needs to solve a measurable problem. Also, we don't build systems that make important decisions on their own, without control rules and human validation.
It depends on the workflow and how clean your starting data is. We can't quote a price before seeing what you process and at what volume.
Here's how we start: a discussion where you show us the workflow, followed by a proposal for a pilot with measurable goals, deliverables, estimated hours, and price. If the pilot doesn't achieve the agreed objective, we tell you and we don't continue just for the sake of it.
Development costs may also include, when relevant, the cost of running: external AI services or resources for local models. We'll show these costs separately, so you know the monthly cost after launch.
Billing is in RON; amounts in EUR are converted at the BNR exchange rate on the billing day. Prices do not include VAT.
Pilot first, expansion after.
You show us where time is spent. Together we pick a single process, important enough to matter, and clearly defined enough to implement.
Before writing any code, we define what success means: time saved, volume processed, and error rate. We use metrics you can check after the pilot.
You run the pilot on real data, with human verification active. We analyze results and identify where the system needs improvement.
We compare the outcomes against the agreed objective. If the investment is justified, we expand the solution and launch it into production. If not, we stop and document the reasons why.
