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Production-ready models, APIs, SDKs and the deployment artefacts behind them.

Six-to-sixteen-week engagements that take a feasible prototype to a production-grade computer-vision system: trained model, held-out evaluation, inference service, hand-off documentation.

Dynamis Labs — Production is the production-engineering pillar of Dynamis Group. Six-to-sixteen-week engagements deliver four artefacts: trained model + weights, evaluation harness (golden datasets, regression suites, drift detectors), inference service (HTTP / gRPC, FastAPI, Tonic) with Python / TypeScript / Go SDKs, and deployment for AWS GPU, GCP, Cloudflare Workers AI, NVIDIA Jetson, Google Coral, Apple Silicon or browser WASM.

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

Workstreams

What ships when prototyping clears its gate.

Four artefacts, delivered together. The model is one of them — the harness, the service and the deployment story are the other three. Without those three, the model is a demo.

Trained model & weights

Task-specific dataset, the model architecture and training regime chosen to fit the data and the constraint (parameter-efficient fine-tuning where it gets to the answer; custom architectures where the data justifies it), and a production-grade trained model. Versioned, reproducible from the training configuration, and held-out-evaluated against a domain-relevant benchmark.

Evaluation harness

Golden datasets, regression suites and the evaluation methodology written to a peer-reviewable standard. The artefact your team uses to keep retrained versions honest — and the document an auditor reads to understand what "the model works" means.

APIs & SDKs

A production inference service with a stable HTTP / gRPC interface, language SDKs for the stacks your team actually runs (Python, TypeScript, Go), and the operational telemetry to know when something is degrading before a user notices.

Edge + cloud deployment

Deployment artefacts for the topology that matches the workload: hyperscaler GPU inference, on-premise CUDA, or edge inference (Jetson, Coral, Apple Silicon, browser WASM). Pick the topology; we ship the deployment.

The architecture decisions behind a production deployment — pipelines, deployment topology, cloud-vs-edge trade-offs — live with Dynamis Advisory — Architecture. Commercial terms (lease vs own outright) are at Licensing.

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.

How long is a Production engagement?
Six to sixteen weeks for the first production release, depending on data complexity and integration scope. Variables that drive duration: dataset readiness from Prototyping, evaluation methodology depth, integration into existing systems (CRMs, ERPs, content pipelines), and deployment topology (cloud GPU is faster to ship than edge inference). Weekly checkpoints with named deliverables.
What does the evaluation harness include?
Golden dataset with held-out splits, regression suite that runs on every model update, drift detectors (input, output, performance), calibration checks, and a benchmark comparison against either prior internal models or domain-relevant public baselines. The harness is delivered as code the client team can re-run.
Where do the architectural decisions come from?
From the Prototyping engagement that preceded Production, plus Dynamis Advisory — Architecture for the upstream data and deployment-topology decisions. Production is where decisions become a built and operated system; the decisions themselves live with Advisory. The single solution architect coordinates, so Production never starts work the brief does not support.
What inference hardware do you target?
For cloud: AWS GPU (g5, g6 families), GCP A100 / H100, Cloudflare Workers AI for smaller models. For edge: NVIDIA Jetson (Orin, Nano), Google Coral TPU, Apple Silicon (M-series Mac mini for low-cost edge inference), and browser WASM (ONNX Runtime Web) for client-side perception. Hardware choice is workload-driven, not vendor-driven.
What APIs and SDKs do you ship?
A production inference service exposed via HTTP / gRPC (FastAPI, Tonic, or whatever fits the client stack), language SDKs in Python, TypeScript and Go for the stacks teams actually run, and operational telemetry (Prometheus metrics, OpenTelemetry traces, request-level audit logs). Generated client libraries via OpenAPI / Protocol Buffers where applicable.

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