Validate your idea or existing product, create an architecture-ready product specification, and hand off clear product logic to AI builders, no-code tools, or developers.
Idea or existing product → readiness score, risk signal, missing inputs, next step.
Module 1 context → product specification, user stories, database draft, prompts and handoff.
Start free with Module 1. Continue only when the next layer is useful: architecture-ready specification, architecture workspace, and future implementation audit.
For founders with a new idea, an existing business process, or a live no-code/AI product. The goal is to understand what should happen next before paying for deeper work.
The Architecture-Ready Product Specification translates the founder's business idea into user stories, use cases, requirements, data structure, platform route, AI-builder prompts and developer handoff.
The platform creates a shared product language between business intent, architecture decisions and implementation work.
Translate your idea into a clear product direction before hiring, paying for development, or adding AI/no-code tools.
Review assumptions, role boundaries, data ownership, platform fit, AI governance, launch risks and future module decisions.
Receive a better starting point: user stories, requirements, database draft, flows, constraints and unresolved decisions.
Use structured context, prompts, evidence and policy boundaries instead of asking an AI tool to guess the product logic.
Use Miro to brainstorm, Notion to document and Cursor or Lovable to build. Use AI Product Architecture OS to decide what should be built, why, on which stack, and what must not break.
Use Miro or FigJam to brainstorm. Use this platform to decide what should be built before the board turns into scattered notes.
Use Productboard, Jira or Notion to manage work. Use this platform to define the product logic before it becomes a roadmap.
Use Cursor, Lovable, Bolt or Replit to build. Use this platform to give them architecture context instead of guesses.
The platform asks what should be built, not only which screen, automation or generated component to create.
The architecture-ready specification gives implementers a structured handoff instead of a vague conversation or screenshot list.
Each trigger points to a different route: free Module 1, product specification, product diagnostic, architecture review, or studio support.
Start with Module 1, then create a product specification if the signal is strong enough.
Validate idea →Map the current business flow and decide what should be digitized first.
Check digitization →If the product is vague, each estimate describes a different product.
Clarify scope →Data, permissions, actions, payments, AI, or platform limits may be the real cause.
Run diagnostic →Inputs, outputs, saving, fallback, cost, logs, disclosure and review must be designed.
Map AI risk →Roles, listings, ownership, checkout, payouts, disputes and moderation connect.
Check marketplace →App Store, Google Play, privacy, geo, account deletion, UGC and support can block launch.
Review launch risk →Module 3 is planned as the workspace where architecture decisions, comments and approvals stay with the project.
See module path →A simple path from unclear context to a product system that can be evaluated, built, reviewed and improved.
New idea, business digitization, or existing product diagnostic. The intake adapts to the situation.
The system returns validation, digitization, or diagnostic signals: product stage, missing context, risk zones and recommended next step.
Module 2 turns the Module 1 context into user stories, requirements, user flow, database draft, platform handoff and AI-builder prompts.
Module 3 is planned as the shared environment where founders, architects and developers keep decisions, comments, approvals and version history.
Module 4 is planned for repository/no-code/API audit, technical errors, mismatch with the specification and architect/developer review workflows.
Module 1 shows the first signal. Module 2 turns it into an architecture-ready product specification. Later modules preserve architecture decisions as the product changes.
For founders who need a first validation, digitization, or diagnostic signal before paying for deeper product work.
AI can create screens, code and database suggestions, but it does not automatically know ownership, permissions, payment states, human review points or launch constraints.
Records, profiles, payments, AI outputs and support notes need ownership rules before implementation.
Permissions, roles, approval states and admin overrides must be designed before users enter real data.
Not every operation belongs in the first version. MVP boundaries protect budget and reduce rebuild risk.
Payments, privacy, AI boundaries, app-store requirements and platform limits need explicit decisions.
Simple screens can hide complex product rules. The platform checks the layers founders, AI builders and no-code developers usually underestimate.
Roles, listings, checkout, commissions, payouts, messages and refunds.
Availability, double booking, cancellations, notifications and payment timing.
Input, prompt, output, database saving, fallback, cost and governance.
Students, tutors, progress, reports, parents and sensitive data.
Plans, access, trial state, billing events and user entitlements.
Adalo, Bubble, Webflow, Xano, Airtable, Make, Supabase and backend boundary decisions.
Feature drift, unclear structure, missing tests, prompt handoff and fragile generated logic.
App Store / Google Play, privacy, account deletion, test users and UGC rules.
Start free. Pay only when you need a stronger module, human review, or implementation support.
Free first signal for ideas, business digitization, and existing product checks.
For founders who need to turn validated context into a developer-ready specification.
Coming next: a shared workspace for architecture decisions after the product specification.
Planned technical audit runs against the specification and architecture after the workflow is validated.
Expert review or implementation support when the product needs human judgment.
AI usage is capped to keep pricing predictable and profitable. Large evidence sets, long documents, repeated generations, ongoing workspace updates or heavy audit runs can use additional AI credit packs instead of making the base modules more expensive for everyone.
The platform creates a structured starting point quickly. For complex products, Anastasia can review assumptions, platform limits, MVP boundaries, AI governance, launch risks and the safest next step.
Meet the creator →If the specification and architecture route are clear enough, studio support can help build, fix, stabilize or prepare the product for launch based on the same product record.
Request studio support →Start with screenshots, recordings, preview links, test users, duplicated app copies or read-only access.
Use temporary users or preview links.
Diagnostic can start without editing your app.
The platform analyzes before implementation.
Health, children, finance, identity, payments and AI advice are marked.
Self-service, expert review, audit or implementation.
Quick answers before you choose the right module path for your product.
Module 1 gives a first product signal: what stage the product is in, what is missing, what risks are visible, and whether the next useful step is a product specification, human review, or more validation.
Buy Module 2 when the idea is strong enough to turn into a developer-ready specification: user stories, use cases, requirements, user flow, database draft, platform route and AI-builder prompts.
No. The platform also supports business digitization and existing products that need diagnostic clarity before rebuilding, fixing, launching or hiring a developer.
No. It reduces confusion before people start building. Founders can use it independently, and architects or developers can later use the same record to review decisions and continue work faster.
Yes. The platform is designed for Adalo, Bubble, Webflow, Xano, Airtable, Make, Supabase, Lovable, Cursor and custom-code paths. Recommendations focus on product logic, data, roles, platform fit and implementation risk.
Each paid module should include a clear number of AI runs or credits. This keeps the base price fair while protecting the platform from very large evidence reviews, repeated rewrites or ongoing audit usage.
No. Start with screenshots, screen recordings, preview links, test users or read-only access. Permanent passwords should not be requested.
Get the first signal, then move to product specification, architecture workspace, technical audit or human review only when the product needs it.