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Data and model architecture for systems you can hand to a regulator.

The pipelines, deployment posture and cloud/edge trade-offs behind anything that ships into production. We write the architecture down at a standard your operators, your auditors, and the next person in the chair can all hold.

Dynamis Advisory — Architecture provides decision-grade counsel on the data and model architecture behind production AI systems: pipeline design (dbt, Airbyte, OpenLineage), table formats (Iceberg, Delta Lake), deployment topology across AWS, Azure, GCP, Cloudflare Workers and on-premise, plus MLOps documentation (MLflow, SageMaker registries, runbooks, manifests). Independent of hyperscaler partner programmes.

  1. Vision
  2. Integration
  3. Architecture
  4. Governance

Workstreams

Where Architecture counsel earns its place.

The hard decisions before a model is trained: where the data lives, where inference runs, how it’s documented, and what survives the team that built it.

Data pipelines & lineage

Where the data comes from, where it goes, and what gets to look at it on the way. Pipeline topology, transformation contracts, lineage tracking, and the schemas a model sees at training and at inference — documented, not implicit.

Deployment topology

Cloud, edge, on-premise, or some combination. We map the inference path: where the model lives, where the data lives, where the latency budget lands, and which choices a future audit will reward.

Cloud vs edge vs hybrid

Trade-offs between hyperscaler convenience and edge sovereignty. AWS, Azure, GCP, Cloudflare Workers, on-prem GPU — written-down rationale per workload, not a vendor’s preferred path.

Hand-off documentation

The artifacts your operators inherit: architecture diagrams, runbooks, evaluation harnesses, deployment manifests, and the failure-mode register. Built so the team that owns it can actually own it.

Where architecture decisions become a built and operated system, the work lives with Dynamis Labs — Production. Advisory is where it’s drawn; Labs is where it’s built and run.

Engagement shape (Briefing, Review, Fractional) lives on the Advisory overview.

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 data architecture cover for an AI workload?
Source-of-truth identification, ingest pipelines, transformation contracts, lineage tracking, schema evolution and the boundary between training data and inference data. Tools commonly recommended: dbt for transformation, Airbyte for ingest, OpenLineage for lineage, Iceberg or Delta Lake for the table format. The recommendation is workload-specific, not pre-baked.
Which cloud providers do you cover?
AWS, Azure, GCP, Cloudflare Workers and on-premise. The recommendation is independent — Dynamis does not have a partner programme with any hyperscaler. Where a workload genuinely runs cheaper or safer on a different provider, we say so. Where the answer is "stay on what you have", we say that too.
Who builds what Architecture decides?
For computer-vision workloads, Dynamis Labs — Production. For LLM and agentic workloads, Dynamis Digital — Integrate. Architecture sits upstream of both: the decisions live here; the build happens in the relevant division. The single solution architect coordinates, so the brief never bounces between teams.
How do you decide cloud versus edge inference?
On four axes: latency budget (edge wins below 50ms), data sovereignty (edge wins for confidential or regulated data), throughput cost (cloud GPU wins for sustained high QPS), and operational maturity (cloud wins for teams without on-call hardware experience). Hybrid is common; the decision is written down per workload, not assumed.
What is MLOps and what artefacts do you produce?
MLOps is the discipline of running machine-learning systems in production: model registries (MLflow, SageMaker), training pipelines, deployment manifests, evaluation harnesses, drift monitoring, and incident runbooks. Architecture engagements produce the documentation set — diagrams, runbooks, manifests, register — that the operating team inherits and can actually use.

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