Applied research for problems without an off-the-shelf answer.
Custom-model research and development for enterprises with novel computer vision, perception, classification or detection problems. Methodology written to a peer-reviewable standard; weights and IP transfer to the client.
Dynamis Labs — Research is the applied-research pillar of Dynamis Group. Engagements deliver custom computer vision and neural-network models for enterprise problems that don’t have an off-the-shelf answer. Output: a trained model, an evaluation harness a third party can re-run, and a written technical report covering architecture, training configuration, results and a re-training runbook. Where the methodology generalises, it’s published; the client owns the weights and the commercial application.
How an engagement runs
From a falsifiable question to a defensible model.
Three workstreams, sequenced. None of them produce slides — they produce a written report, a reproducible evaluation, and a model your team can defend.
Problem framing
Framing the question.
Before any model: a written problem statement. What is the task, what does success mean, what would falsify the approach. Sketches the evaluation methodology before the first dataset is touched.
Methodology
Choosing the method.
Architecture, loss, training regime — chosen for the data and the constraint, not the latest paper. Documented choices, documented trade-offs, written up so an inheriting team can defend them.
Benchmarking
Honest evaluation.
Held-out splits, regression suites, ablations against the smallest credible baseline. Evaluation methodology written to a standard a peer reviewer would recognise — and delivered as code your team can re-run.
Engagements run under NDA by default. Both the methodology documents and the trained model can be marked client-confidential if the work depends on commercially sensitive data or domain.
IP & confidentiality
Methodology we publish; weights clients own.
Where a result generalises across data and domain, we write it up. Where a result depends on the client’s confidential data or operating context, the trained model, the evaluation harness and the technical report stay with the client under Licensing terms.
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Generalisable methodology
Architectural patterns, evaluation protocols and benchmark methodologies that aren’t specific to one client are eligible for publication as preprints or technical notes — credited and reviewable by the wider community.
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Client-confidential outputs
Trained weights, the dataset, the held-out evaluation and the technical report stay with the client by default — covered under Lease or Own outright commercial terms.
Common questions
FAQs
Here are some of our most frequently asked questions. Can't find what you're looking for? Reach out to our support team.
What does an applied-research engagement actually deliver?
What does "peer-reviewable methodology" mean in practice?
How is research different from a prototyping engagement?
How do you choose the model architecture?
Do you publish your research?
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