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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.

  1. Research
  2. Prototyping
  3. Production
  4. Licensing

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.

  • 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.

  • 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?
A written technical report covering: the research question (framed so it can be falsified), the architectural choices and why they were chosen, the evaluation methodology, held-out results against the methodology, and a runbook for re-training. The report is the artefact your team — or an inheriting team — uses to rebuild the model from scratch.
What does "peer-reviewable methodology" mean in practice?
The evaluation harness is delivered as code a third party can re-run. The training configuration is reproducible from the report. Held-out splits, metrics, baselines and ablations are documented to a standard a journal reviewer would expect. If the methodology depends on a benchmark we built, we publish the benchmark; if it depends on a benchmark you own, your team can re-run it whenever.
How is research different from a prototyping engagement?
Prototyping answers "is this tractable on our data, in our timeframe, on our budget" — output is a go / no-go memo and a baseline notebook. Research answers "what is the right method for this class of problem" — output is a methodology written to a standard another group could replicate. The two pillars often overlap: a research engagement may include feasibility work, and a prototyping engagement may surface a research question worth investigating in depth.
How do you choose the model architecture?
Architecture follows the task, the data and the deployment constraint. We start with the smallest baseline that could plausibly work, evaluate against a domain-relevant benchmark, and only escalate complexity when the data justifies it. Where parameter-efficient fine-tuning over a strong pretrained backbone clears the bar, we use it. Where the problem requires a custom architecture, we design one — and write up why.
Do you publish your research?
Methods that generalise are written up at a peer-reviewable standard — preprints, technical notes, position papers on evaluation methodology. Results that depend on a client's confidential data, domain or deployment stay with the client. We share the methodology; the client owns the trained weights and the commercial application.

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