AI/ML architecture · applied AI · delivery
Turn a business problem into an AI capability your team can evaluate, integrate, and operate. I design the path from data and model decisions to a working product, with scope agreed before implementation.
What an AI/ML architect helps you decide
An AI/ML architect connects artificial intelligence and machine learning to the product around them: the data available, the decision or task to support, the quality required, and the people responsible for operating it. I help teams assess whether AI is appropriate, choose an approach, define measurable acceptance criteria, and plan the application and infrastructure changes needed to deliver it. The starting point can be a new feature, an existing prototype, or an AI integration that needs a clearer operating model.
In depth
We identify the user, the workflow, the cost of mistakes, and the current non-AI baseline. Then we review sample data, permissions, coverage, and quality. For predictive machine-learning work, the assessment includes whether useful labels and a defensible evaluation dataset exist. The recommendation may be a simpler rules-based workflow when a model would add complexity without a clear benefit.
In depth
The architecture assessment compares hosted APIs, self-hosted models, and conventional machine-learning approaches against the agreed task. For language-model features, the scope may include prompt design, retrieval from approved business content, structured outputs, and bounded tool access. Retrieval, training, and fine-tuning are evaluated as options rather than assumed requirements. The design makes access controls, tenant boundaries, data flows, and provider dependencies explicit.
In depth
Before expanding a prototype, we agree representative examples and acceptance criteria. Depending on the task, evaluation can consider correctness, unsupported answers, classification errors, human review effort, latency, and cost per completed task. The deliverable identifies failure cases and the circumstances requiring human review. No accuracy, savings, or autonomous decision-making guarantees are made before evidence is available.
In depth
Implementation is scoped around the existing application: APIs, background jobs, user permissions, approval steps, retries, and fallback behavior. Business-critical actions need explicit authorization and traceable execution. The goal is a feature that fits the operational workflow, with clear ownership when the model or a dependent service fails.
In depth
The production plan covers infrastructure choices, logging boundaries, configuration and model versioning, monitoring, rollback, and ongoing responsibility. A hosted-versus-self-hosted comparison includes infrastructure and maintenance costs as well as usage charges. Data location and access requirements are assessed for the actual deployment; self-hosting alone is not a compliance guarantee.
In depth
Begin with an assessment of the problem, available data, current system, and constraints. The next phase may be an architecture blueprint, a bounded proof of concept, an implementation project, or retained technical leadership. Each phase has agreed deliverables and acceptance criteria. Production implementation, custom model training, specialist research, security certification, and ongoing support require explicit scope; they are not automatically included in an architecture assessment.
Fit
Scope
Deliverables
Evidence
Self-hosted, OpenAI-compatible LLM service for production captions, review responses, job content, and estimating workflows.
Automotive marketing intelligence platform using historical Canadian dealership data, advanced data mining, and LLM-assisted strategy to automate targeted customer invitations.
Modular Laravel platform combining multichannel publishing, AI-assisted content, public forms, hiring workflows, websites, and tenant storefronts.
A clear starting point
Choose the decision you need to make. Scope and commercial terms are agreed before work starts.
Decision before implementation
For businesses evaluating AI features or private models and needing evidence before committing to a build.
A go/no-go recommendation and a practical AI implementation plan.
Next step
Let's discuss the constraints, the current system, and what a first engagement would look like.
Questions and answers
An AI/ML architect designs how data, models, application features, infrastructure, and human oversight work together. The service connects a business use case to a testable implementation and an operating plan.
Yes. An assessment can review the prototype, its data, evaluation approach, integration points, and failure behavior, then identify the changes needed before a production rollout.
No. Hosted APIs, self-hosted models, and conventional ML approaches are assessed against the use case, data constraints, cost, and operating capacity. Self-hosted deployment remains a separate service when infrastructure delivery is the main need.
Not automatically. We first assess whether existing models, retrieval, configuration, or a simpler approach can meet the need. Custom training or fine-tuning requires suitable data, clear evaluation criteria, and a separately agreed scope.
Relevant portfolio work includes Peaches for self-hosted LLM application workflows, ProfileDriver for customer-data marketing intelligence and an LLM strategy engine, and Pulse/MyOmniHub for AI workflows within a modular business platform. These demonstrate specific experience rather than a claim to every ML specialty.
Share the business problem, current product, available data, and constraints through the contact form. We can scope a remote international engagement and agree communication, milestones, commercial terms, and any location-specific delivery requirements.
