Develop intelligent features and models when the available data, evaluation method, operating workflow, and business decision justify the investment.
Luminous builds machine learning into products where a prediction changes a decision: scoring, forecasting, classification and retrieval. Work uses PyTorch, scikit-learn and AWS SageMaker, and starts by checking there is enough data to justify it. Fixed price from $999 to $15,000, delivered in 2 to 24 weeks depending on scope.
| If you need | Use |
|---|---|
| You have historical data and a repeating decision | AI and machine learning |
| You want language understanding rather than prediction | AI agent development on Claude or GPT |
| You have under a few thousand labelled examples | A rules-based system will beat a model |
| The data lives in several disconnected systems | AWS ML pipeline first |
The engagement starts with the operating problem, users, constraints, and evidence required to make a sound technical decision.
Clarify the workflow, users, constraints, and outcome before selecting technology.
Decide what should be built, integrated, automated, or deliberately left unchanged.
Treat security, accessibility, failure states, maintainability, and operating cost as delivery requirements.
Document decisions, demonstrate working increments, and make post-launch responsibilities explicit.
The process stays visible so business context and technical implementation do not drift apart.
Map the current workflow, user needs, constraints, existing systems, and success criteria.
Define the experience, architecture, scope, sequence, and decisions that need validation.
Deliver working increments and test the important paths before expanding scope.
Deploy with ownership, monitoring, documentation, and a clear next-iteration plan.
Post-launch work can cover monitoring, maintenance, optimization, training, or continued product development where it creates clear value.
We select tools based on the workflow, existing environment, security needs, team capability, operating cost, and expected lifespan.
Use these guides and services to evaluate the surrounding website, search, advertising, intake, and measurement work.
It is a good fit when a defined product, workflow, customer, or operating problem needs senior technical judgment and production delivery. Discovery is used to confirm whether this capability is necessary and where its boundary should be.
Yes. We can improve, integrate, or replace only the parts creating material risk or friction. A full rebuild is recommended only when the evidence supports it.
Scope follows the users, workflows, integrations, risks, and verification required. After discovery, we provide a phased plan with assumptions, decisions, responsibilities, and a realistic delivery range.
We evaluate the existing environment, security and privacy needs, team capability, operating cost, maintainability, performance, and expected lifespan. Technology follows those requirements.
We establish ownership, documentation, monitoring, and the next review point. Ongoing support can include maintenance, optimization, training, or continued development where it creates clear value.
Yes. Many useful systems combine product design, software engineering, AI, automation, infrastructure, and growth work. We organize those capabilities as one coherent system rather than separate vendor workstreams.
We will help define the sensible next step, including when a smaller intervention is better than a full build.
