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Dynamis Labs. Custom computer vision and neural networks.

Custom AI models, built for your task — leased or owned.

A small applied-research group that builds task-specific computer vision and neural-network models for enterprises whose problem doesn’t have an off-the-shelf answer.

Dynamis Labs is the applied-research arm of Dynamis Group. We build custom computer vision and neural-network models — perception, classification, detection, segmentation, representation learning — for enterprises with a defined business problem and the data to back it. Four pillars: Research, Prototyping, Production, Licensing. Models are leased as a managed service or owned outright on a single build-and-deliver fee.

Where off-the-shelf vision APIs and generic LLMs run out, custom models start. We build them to fit the task — and license them on terms the client can stand on.

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Practices

What we do.

Research

Applied research for problems without an off-the-shelf answer. We frame the question, propose architectures, validate methodology — to a standard a peer reviewer would recognise.

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Prototyping

Two-to-four-week feasibility studies. We establish whether a perception, classification or detection task is tractable on your data — with a written go / no-go either way.

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Production

Production-grade computer vision and neural-network systems with evaluation harnesses, inference services and the deployment artefacts that make a model operate, not just exist.

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Licensing

Two commercial paths for a delivered model: lease it as a managed service, or own the weights outright with the training configuration and technical report.

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How we work

Research, prototype, production, license.

  1. 01

    Research

    Frame the question, choose the methodology, define the evaluation a third party could re-run.

  2. 02

    Prototype

    Scoped feasibility studies, dataset creation, and a written go / no-go either way.

  3. 03

    Production

    Engineered models, evaluation harnesses, inference services, deployment artefacts.

  4. 04

    License

    Lease the model as a managed service, or own the weights and the report outright.

Guardrails

Research ethics, written down.

Computer vision is a dual-use field. We hold ourselves to research ethics equivalent to a university lab, and decline work that does not meet them.

  • A single, falsifiable question

    If we can’t phrase the research question cleanly, we won’t take the work.

  • Documented data provenance

    Training and evaluation data must have clear provenance, consent and licensing.

  • Published methodology

    Methods are written up at a standard a peer reviewer would recognise.

  • No identifiable-person targeting

    We do not build detectors, trackers or biometric systems that target identifiable individuals.

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 kind of models do you build?
Custom computer vision and neural network models for tasks that don’t have an off-the-shelf answer. Common shapes: perception (object detection, segmentation, classification), re-identification, defect or anomaly detection, document and form understanding, video event detection, and bespoke representation learning where the value is in a domain-specific embedding. The pattern that connects them: a single, well-defined task on the client’s data, with an evaluation methodology a third party can re-run.
What is the difference between Lease and Own outright?
Lease — the trained model is hosted and maintained by Dynamis Labs on your chosen cloud, with a predictable monthly fee and quarterly re-training included. Own outright — a single build-and-deliver fee, with the weights, evaluation harness, training configuration and technical report transferred on completion. Lease is convertible to ownership at any time.
Will you take any computer-vision project?
No. Four guardrails: a single falsifiable research question, documented data provenance and consent, methodology written to a peer-reviewable standard, and no detectors or biometric systems targeting identifiable individuals. Computer vision is a dual-use field; we hold ourselves to research ethics equivalent to a university lab and decline work that does not meet them.
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.
How do you decide which architecture to use?
Architecture follows the task and the data. We start with a baseline that fits the constraint (latency, hardware, label budget), evaluate against domain-relevant benchmarks, and only escalate complexity when the data justifies it. Where parameter-efficient fine-tuning over a strong pretrained backbone gets to the answer, we use it. Where a custom architecture is genuinely required, we build one — but only with the methodology written up so an inheriting team can rebuild it.

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One architect, one inbox.

Every Dynamis engagement is led by a client-facing solution architect — your single point of contact. We coordinate Digital, Advisory and Labs internally, so you brief one person and we handle the rest.

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