Software development,
augmented by AI.

1. Executive Summary

Saze AI Studio is a proposed AI-powered software development platform designed to help small and mid-sized engineering teams move from business requirements to reviewed, testable software deliverables. The platform envisions a human-in-the-loop workflow in which AI agents assist with requirement analysis, development planning, code generation, code review, and quality assurance. Claude API is planned as a central language-model capability, subject to technical validation and commercial availability.

2. Problem & Opportunity

Software delivery often involves disconnected handoffs among product owners, business analysts, developers, and QA engineers. Requirements may be incomplete, implementation decisions may be poorly documented, and regression testing may lag behind development. Saze AI Studio aims to reduce repetitive work and improve traceability without replacing engineering judgment or release approvals.

3. Target Users & Use Cases

Primary users: startup engineering teams, software agencies, internal IT departments, product owners, business analysts, and QA teams. Initial use cases include turning feature requests into structured user stories, proposing API specifications, generating starter code, reviewing pull requests, and drafting automated test scenarios.

4. Proposed Product Modules

AI Business Analyst — converts stakeholder notes into functional requirements, acceptance criteria, user stories, and clarification questions.

AI Development Planner — proposes implementation tasks, dependencies, interfaces, and effort assumptions.

AI Code Assistant — generates or refactors code with repository context and developer approval.

AI Code Reviewer — flags possible defects, maintainability concerns, and security risks; findings require human validation.

AI QA Engineer — drafts test cases, test data, and Playwright test scripts; execution occurs in controlled environments.

Workflow & Team Visibility — connects artifacts, approval checkpoints, run history, and status across the delivery pipeline.

5. End-to-End Workflow

1. A user submits a requirement or imports an approved ticket.

2. The AI BA module extracts requirements, ambiguities, and acceptance criteria.

3. A human reviewer approves the specification.

4. The development planner proposes tasks and architecture notes.

5. The code assistant prepares changes for review in a branch or sandbox.

6. The QA module generates tests and collects execution results.

7. Developers and QA approve changes before deployment. No autonomous production deployment is assumed for the MVP.

6. Planned Claude API Integration

Claude API is intended for natural-language understanding, structured requirement extraction, coding assistance, code review summaries, and test design. Proposed integration uses server-side API calls, prompt templates, JSON-schema validation where supported, usage metering, and audit logging. Sensitive information should be minimized or redacted before model requests. The application should support model configuration and provider fallback to manage cost and reliability.

7. Proposed Technical Architecture

Frontend: Next.js / React for dashboards and workflow review.

Backend: Laravel (PHP 8.x) REST API for authentication, orchestration, and integrations.

Database: MySQL for users, projects, requirements, artifacts, approvals, and run logs.

AI layer: Claude API through a backend adapter with rate limits, retries, budget caps, and model selection.

Automation: Playwright in isolated workers for browser testing.

Integrations (planned): Git repositories and Jira-compatible issue tracking.

Deployment: Docker-based development environment; production hosting to be selected after security and load testing.

8. Security, Privacy & Human Oversight

Apply role-based access control, secret management, encryption in transit, audit logs, tenant separation, least-privilege repository access, and approval gates. Do not send credentials or production customer data to models by default. Generated code must undergo review, automated checks, dependency scanning, and controlled testing. AI outputs may be inaccurate or insecure; the platform should make this limitation visible.

9. Proposed MVP Scope

Phase 1 — Project workspace, requirement input, AI BA draft, editable acceptance criteria, and human approval.

Phase 2 — Development task breakdown, code-assistance prototype, and repository integration in a sandbox.

Phase 3 — QA case generation, Playwright test draft/export, run logs, and quality feedback.

Phase 4 — Pilot feedback, usage analytics, cost controls, and security hardening.

Dates and delivery commitments are not yet established.

10. Success Metrics

Potential pilot metrics: time to produce a reviewed specification; time to prepare an implementation plan; percentage of generated test cases accepted after review; defects found before release; AI cost per workflow; and user satisfaction. Baselines and targets will be defined during MVP testing.

11. Business Model (Hypothesis)

A potential SaaS subscription with tiered monthly usage allowances, project seats, and usage-based AI credits. Enterprise options may include private deployment and integration support. Pricing and commercial terms remain under evaluation.

12. Current Status & Next Steps

Status: project concept and proposed MVP. This document does not claim a launched product, active paying customers, completed integrations, or approved Anthropic partnership. Next steps are to validate user needs, build a demonstrable workflow, test Claude API feasibility, and publish a transparent MVP roadmap.

13. Office & Contact

Project: Saze AI Studio

Office address: No. 19/402, My Dinh, Tu Liem, Hanoi, Vietnam.

Country: Vietnam

Website and contact email: to be confirmed by the project owner before publication.

Note: the office address is not represented as a registered business address.