IndependentAIR&DCenter, Kyiv

DRL is a fit when a product depends on solving a difficult technical problem: improving a learning system, making an agent reliable, or integrating AI into a working product. Our labs combine applied research with engineering across models, data, software and hardware. The scope can be a focused investigation, a critical subsystem or an ongoing R&D programme.

Yes. We can take on a defined research or engineering workstream alongside your team and other partners. We agree a technical lead, interfaces and acceptance criteria. Your team retains product priorities and architecture decisions; DRL coordinates the lab expertise needed within our scope.

Yes. We start with a technical discussion involving the researchers and engineers proposed for the work. Together, we review the objective, operating constraints, available data and existing systems. The first phase can establish a baseline, test feasibility and determine whether existing technology is sufficient or further research is needed.

The result is evidence for a technical decision, supported by the agreed research artefacts. These may include reproducible experiments, an evaluation suite, datasets, model artefacts or a prototype. We document the findings, limitations and recommended next step so your team can assess what to build, change or investigate further.

An experiment can rule an approach out. We agree the criteria for continuing, changing direction or stopping before committing to larger experiments. If a hypothesis fails, the result should explain what was tested, where the approach reached its limits and what the evidence suggests doing next.

We test against agreed operating conditions, including representative data, failure cases, latency and resource constraints. For a deployed system, validation also covers integration, failure recovery and operation in the target environment. Your team and ours define the evaluation and release criteria before a deployment decision.

We agree the scope, budget and timeframe for each phase, including engineering effort, compute and data costs. Research milestones are tied to specific experiments and decisions. Changes to scope or assumptions trigger a review of the plan and budget before further work is committed.

Before work starts, we agree approved environments, access permissions, model endpoints and data-handling requirements. Partner-confidential material is shared only with appropriate permission. The engagement also defines what may be reused or disclosed, including any use of project results in publications or case studies.

We distinguish project-specific results from existing know-how, reusable components and third-party technology. Ownership, licences and permitted reuse are agreed before work starts. The agreement specifies what you own, what you may use and any restrictions attached to the technologies involved.

We plan handover around your team reproducing the results and running the agreed system in its own environment. The scope covers the necessary code, permitted model artefacts, experiment records, tests and documentation, together with knowledge transfer. Continued research or operational support can be arranged separately.