AI/ML architecture · applied AI · delivery

AI/ML Architecture & Development

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.

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Engagement: Assessment, project or retained
Recommended start: From $5,900
Areas in scope: 7
Deliverables: 7

The core decision

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

Start with the task and the evidence

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.

Related work Peaches

Self-hosted, OpenAI-compatible LLM service for production captions, review responses, job content, and estimating workflows.

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In depth

Choose the model and integration approach

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.

Related work ProfileDriver

Automotive marketing intelligence platform using historical Canadian dealership data, advanced data mining, and LLM-assisted strategy to automate targ...

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In depth

Define how quality will be measured

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.

Related work Pulse / MyOmniHub

Modular Laravel platform combining multichannel publishing, AI-assisted content, public forms, hiring workflows, websites, and tenant storefronts.

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In depth

Connect the AI capability to the product

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

Plan deployment, monitoring, and ownership

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

Agree the engagement and its boundaries

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

Who this service helps

SaaS founders adding AI to a product with existing users, permissions, and workflows.
Businesses evaluating private AI for internal content and operational tasks.
Teams with an AI prototype that needs evaluation, integration, and production planning.
Engineering leaders comparing hosted models, self-hosting, and other approaches.

Scope

Scope agreed around your use case

Use-case prioritization and feasibility assessment.
Data readiness, access boundaries, and evaluation planning.
Model and provider selection, including hosted and self-hosted options.
LLM workflow design, prompt engineering, and retrieval assessment.
Predictive ML feasibility and evaluation design where suitable data exists.
Application APIs, workflow integration, and human-review controls.
Production architecture, monitoring, fallback, and handover planning.

Deliverables

What an architecture engagement can deliver

A written problem statement, use-case priorities, and feasibility recommendation.
A data-readiness assessment with gaps, access requirements, and preparation tasks.
An architecture blueprint covering data flows, application boundaries, models, and integrations.
A model/provider comparison with assumptions and estimated operating costs clearly labeled.
An evaluation plan with representative test cases and agreed acceptance criteria.
A phased implementation backlog with dependencies, risks, and ownership.
A deployment and handover plan; implementation or a proof of concept when separately included in scope.

Recommended start

AI Feasibility & Architecture Assessment

Decision before implementation

A go/no-go recommendation and a practical AI implementation plan.

How we work: Define the tasks and baselines, assess data and deployment options, then deliver the report and walkthrough.
What you provide: The workflows, representative data you are authorised to share, expected usage, and privacy or infrastructure constraints.
Engagement boundaries: Prototypes, model training, production deployment and legal or compliance certification are not included. Findings are technical evidence for your own compliance review.

Fees and timing

From $5,900

2–3 weeks

From $5,900, confirmed in writing after a free scoping call. 50% on booking, 50% after the walkthrough. Credited in full against a retainer or implementation booked within 45 days.

Request an assessment me@mmuzammilq.com

Evidence