Product implementation

Agent-enabled product engineering for teams shipping real AI features

If you're building AI into a product, you need more than prompts. You need workflow design, context assembly, bounded behavior, fallback paths, and an implementation that respects the rest of the stack.

See an AI product case study →

Starting at €10,000 for scoped implementation work.

Product patterns I can help implement

In-product copilot

A bounded assistant inside the product that retrieves account or workflow context and helps users complete a specific task faster.

Structured drafting workflow

Generate drafts, summaries, or recommendations in a defined schema so the output fits product logic instead of landing as freeform text.

Recommendation system with human confirmation

Use AI to rank or suggest the next action while the user or application rules still control the final step.

Bounded action workflow

Let the system prepare or queue actions, but only trigger them when the trust boundary, access rules, and fallback path are explicit.

Good fit

Founders adding AI features into an existing product

Teams building a first agent-enabled workflow inside a SaaS product

Laravel-heavy products that need someone comfortable with both product code and AI integration realities

What this work actually includes

User action and product workflow design
Context assembly from product state, docs, or internal systems
Tooling, business rules, and bounded actions
Review UX, confidence cues, and fallback behavior
Queues, persistence, logging, and evaluation

Questions to answer before shipping AI in a product

What user action gets better if AI is added here?

What output needs to be reliable enough for customers to trust?

What fallback should exist when the model, retrieval, or tool step is weak?

What must remain deterministic in the app even if the AI output varies?

How will the team review quality and improve the feature over time?

Relevant background

My product work already spans Laravel applications, operational software, and AI-enabled products.

That combination is useful when AI features need to connect to queues, billing, user roles, auditability, evaluation, and the rest of the platform reality.

If you need both architecture thinking and hands-on shipping, this offer is built for that.

Building an AI feature right now?

Send me the product context, the user action you want to improve, and the output you need the system to produce. I'll tell you whether it sounds implementation-ready.