Start with the real problem
Clarify users, jobs, goals and constraints before choosing a visual direction.
ByJTT Design
Design with AI. Validate like it matters.
ByJTT Design helps people and AI agents research, design, build and validate digital products — with evidence, clear intent and production reality in the loop.
The workflow
From vague idea to evidence-backed product.
You do not need design vocabulary to start. Describe what you are trying to make; ByJTT turns intent into structured decisions that can be inspected, tested and reused.
Use evidence, not guesses
Look at user needs, established patterns, platform conventions and existing solutions.
Make intent explicit
Capture requirements and measurable success criteria so decisions have something to answer to.
Create deliberate directions
Generate and direct a design while preserving rationale, semantics and constraints.
Challenge the result
Check product fit, UX, visual quality, accessibility, responsiveness, content and engineering reality.
Ship what survives
Test the implementation in real conditions, record what happened and feed useful outcomes back into the system.
Why ByJTT
Generation is not the finish line.
A beautiful screenshot can hide weak information architecture, inaccessible interactions, broken responsive behaviour, generic visual decisions or a product that never solved the original need.
Others generate design. ByJTT should know what deserves to ship — and help build it correctly.
Models and generators are execution backends, not the moat. The durable layer is research, requirements, design intent, evaluation, provenance, production validation and learning.
Product surfaces
One system, different ways to use it.
The public site explains the system. The Studio lets you use it. The supporting surfaces make its knowledge and evidence reusable.
Design Studio
A focused workspace to describe a product, direct the design, inspect decisions and evidence, then publish a reusable result.
Open Studio →Design Contracts
Portable, versioned intent covering goals, brand, components, interactions, responsive behaviour, accessibility, rationale and provenance.
Understand the contracts →Resource Library
Reusable work recorded with more than a screenshot: decisions, evidence and provenance travel with the resource.
Explore the library →Research & Benchmarks
Evidence about design patterns, AI failure modes and controlled comparisons, kept separate from marketing claims.
Read the research →Design Gauntlet
Independent specialist review across product fit, UX, visual design, accessibility, responsive behaviour, engineering and provenance.
See the review model →Production Quality
Browser-rendered validation, real-content stress testing, state coverage, accessibility, performance and regression checks.
See quality guidance →Human + agent ready
Simple for people. Structured for machines.
A beginner can work in plain English. A designer or developer can inspect the reasoning. An AI agent can consume durable, machine-readable intent instead of scraping screenshots and guessing.
Human agency with bounded autonomy.
Important autonomous decisions should expose rationale, evidence, uncertainty and changed scope. ByJTT is designed to help people and agents collaborate without turning the product into an opaque “AI says so” box.
Questions
Start here if this is new.
What is ByJTT Design?
ByJTT Design is a research, design, orchestration and validation layer for digital products. It helps people and AI agents move from product intent through design decisions and production checks.
Is it another AI website builder?
No. Generation is one part of the workflow. ByJTT focuses on deciding what should be built, why, how to evaluate alternatives, and whether the implemented result survives real-world checks.
Do I need to be a designer?
No. The Studio accepts plain-language instructions. You can start with the problem you are trying to solve rather than knowing component or design-system terminology.
Can developers and designers use it?
Yes. The same workflow exposes structured intent, decisions, evidence, provenance and reusable resources that can be inspected by technical and design teams.
Can AI agents use it?
Yes. The product is intentionally tool-independent and is being built around machine-readable contracts, research, discovery, evaluation, testing and provenance so agents can work with explicit product intent.
Does the Studio use a production AI model yet?
The current vertical slice deliberately uses deterministic transformations to prove the product and evidence contracts before coupling the workflow to a model provider. Production model adapters remain replaceable.
Ready to try it?
Bring a product idea. Plain English is enough.
Open the Studio, direct the design, inspect the evidence, and see how the pieces fit together.