DataRoot Labs

Research and engineering for artificial intelligence, run from Kyiv, is how this independent centre describes its work. Its slogan about the depth to invent and the engineering to deliver captures a model in which scientists and engineers build a client's AI system from first design to production.

Three laboratories

The centre organises its work as an Agentic Lab, a Deep Learning Lab and an Engineering Lab. The agentic side covers conversational and voice agents, tool integration, orchestration design, retrieval with knowledge graphs and agent memory, evaluation, and document tasks such as rewriting, classification and entity extraction. It talks about harnesses with long tasks, checkpoints and recovery, containment of prompt injection and least-privilege access.

Agents inside business systems

DataRoot Labs says it connects agents to client systems through typed tools whose outputs are checked against a schema and written to databases, with browser and API actions kept inside access boundaries. It describes evidence-grounded memory that retrieves and reconciles private sources and gives traceable answers with stated uncertainty. Validation sets are agreed before work begins, and results are tested across repeated trials and traced in production.

The deep learning side

The deep learning lab addresses policies that turn perception into action, understanding across sensors and modalities, model performance on target hardware, and training data and simulated worlds.

How a project proceeds

Earlier pages set out a staged process. A free consultation on a client's technical challenge leads to a roadmap with stages, timeline and team, then a contract. The first deliverable is a solution architecture document, followed by data work: anonymising sensitive data, labelling datasets and cleaning them. The team trains and tunes models, builds the service around them, deploys to cloud, dedicated servers, mobile or embedded devices, adds monitoring and continuous integration, documents the solution and trains the client's staff. It applies agile methods such as Kanban and Scrum to research cycles, and described itself as research and development as a service for AI startups.