AI-first products & MVPs
A first useful version with a clear user journey, working software, and an AI layer that earns its place.
BUILD · AI PRODUCT DEVELOPMENT
Build AI products that hold up in the real world.
I design and build AI-powered products, MVPs, and product features—from product decisions and UX to full-stack engineering, model integration, evaluation, deployment, and iteration.
THE POINT Build the product first. Use AI where it makes that product more useful.
01 / THE PRINCIPLE
A model isn't the product.
A useful AI product is the whole system around it. The interface must make sense, the data must be relevant, and the software must know what to do when the model is uncertain or unavailable. Latency, cost, and what happens after launch matter too.
02 / WHAT I BUILD
Products people use, with AI integrated where it helps.
From the first product decision to the working software.
AI features in existing products
Search, summarization, generation, classification, or assistance designed into a product people already use.
Retrieval & knowledge experiences
Search and retrieval-augmented (RAG) experiences that help people work with information when keyword lookup is not enough.
Copilots & controlled actions
Context-aware assistance that can suggest or take bounded product actions, with clear permissions and review where needed.
The full product around the model
UX, frontend, backend, APIs, data, authentication, deployment, and the ordinary engineering that makes the feature usable.
03 / THE DECISION
The best architecture uses the simplest useful tool for each job.
Where AI actually earns its place.
Use deterministic software when the answer should be exact. Use AI when ambiguity is part of the problem.
DETERMINISTIC SOFTWARE
Keep the rules exact.
Validation, permissions, calculations, transactions, fixed business rules, and exact transformations belong in predictable code.
MODEL-DRIVEN SOFTWARE
Handle the ambiguity.
Language understanding, fuzzy retrieval, summarization, generation, interpretation, and semantic matching may benefit from a model.
The boundary depends on the product, its users, and the cost of a wrong answer.
04 / PRODUCT SYSTEM
A representative way to think about the layers—not a required stack for every product.
The model sits inside a product.
A model call is one responsibility among several. The surrounding experience and engineering decide whether its output is useful.
- 01UserA person with a task, a context, and an expectation.
- 02ExperienceThe interface makes the next action and the result understandable.
- 03Product logicRules, permissions, state, and exact operations stay in software.
- 04ModelAI handles the parts that benefit from interpretation or generation.
- 05Retrieval & toolsRelevant information or bounded actions are available when needed.
- 06Evals & safeguardsQuality is tested; guardrails and fallbacks cover uncertain or failed paths.
- 07InfrastructureDeployment and observability make the system operable after launch.
05 / HOW I BUILD
Decisions, proof, engineering, and learning in one continuous process.
Make the first version useful. Then make it better.
- 01
Understand
Find the real problem, the user, and the constraint.
- 02
Scope
Decide what belongs in the first useful version.
- 03
Architect
Separate exact software logic from work that benefits from AI.
- 04
Prove
Test the riskiest product and technical assumptions early.
- 05
Build
Create the experience, application logic, and supporting infrastructure.
- 06
Evaluate
Check behavior, edge cases, quality, latency, and cost where relevant.
- 07
Ship
Deploy, observe real use, and decide what to improve next.
06 / SELECTED EVIDENCE
Product work and technical thinking, described for what they are.
The work behind the approach.
These examples show product engineering, experience design, and evidence-led thinking. They are not presented as AI deployment case studies.
01 / PRODUCT ENGINEERING
ImageCompressorFree
A browser-based image compression product shaped around privacy, client-side processing, and a usable interface.
02 / PRODUCT EXPERIENCE
RAENX
A fashion storefront where brand expression, product discovery, and the shopping journey were designed as one experience.
View case study03 / TECHNICAL THINKING
Research & notes
Build notes and investigations that separate observed evidence from assumptions and open questions.
Explore research07 / CHOOSE THE RIGHT PILLAR
Related engineering disciplines, different things to build.
A product to use, or a process to run?
BUILD / AI PRODUCT DEVELOPMENT
Software people use.
An AI-powered MVP, SaaS application, search experience, product feature, or copilot with a real interface and product logic.
You are hereAUTOMATE / AI AUTOMATION
Workflows businesses run.
Lead routing, data movement, notifications, CRM processes, or multi-step operations that reduce manual work behind the scenes.
Explore AI automation08 / WHO THIS IS FOR
Different starting points, one useful question: what should the product do for someone?
A clear next step for your product.
Founders with an early product idea or a prototype that needs a working first version.
Businesses that need customer-facing or internal software with a useful AI capability.
Teams improving an existing product's UX, architecture, or AI integration.
09 / BEFORE WE START
Questions worth answering before choosing technology.
A few practical questions.
Can we start with an early idea?
Yes. The first step is to define the user, the problem, and the smallest version worth testing before choosing a model or a stack.
Can you add AI to an existing product?
Yes. The new capability should fit the current experience, data, permissions, and operating constraints instead of becoming a disconnected demo.
Do I need to choose the model first?
No. The product task comes first. Model and provider choices follow from the required behavior, quality, latency, cost, and data constraints.
What happens after the first version?
We look at what people actually do, where the experience fails or confuses them, and what the next iteration needs.
THE NEXT PRODUCT
Have an AI product worth making real?
If you have a product idea, prototype, or existing product that needs a stronger AI layer, tell me what you're trying to build.