AI-Logic Bridge: Legacy SaaS to AI-Native Converter
0.52已归档17 次浏览0 次认可5/29/2026
B2B SaaSSolo SaaS foundersEfficiency GapAI ToolCode Migration
来源平台: idea-spark
A developer tool that analyzes the business logic of an existing code-based SaaS (e.g., a fixed-form booking system, rule-based CRM) and generates an AI-native, workflow-driven version. It helps solo developers future-proof their products against being replaced by custom AI agents without rewriting their entire core from scratch.
目标用户
Solo SaaS founders or small teams (1-3 people) who have a live, revenue-generating web application built on a fixed business logic (e.g., appointment scheduling, simple e-commerce rule engine, document processing) and are worried about the 'AI replacement' trend for their specific niche.
核心差异点
It doesn't generate a whole new app from a prompt. It specifically *refactors an existing, working codebase* to an AI-native architecture, preserving the original user interface and data models while upgrading the decision engine. This lowers the risk and effort for a solo dev to 'AI-upgrade' their existing business.
解决方案
A web app where the user connects their code repository (e.g., via GitHub) or uploads key files. The tool uses LLMs to perform a static analysis of the application's core rules, data models, and user flows. It then generates: 1) A structured 'Logic Manifest' JSON describing the app's core functions, 2) A new codebase scaffold (using Next.js + TypeScript) where fixed logic is replaced with an AI agent (using LangChain.js or similar) that has the original rules as system prompts/tools, and 3) A visual comparison of the old vs. new execution flow. The MVP focuses on one common app type: appointment scheduling or rule-based form processing.
关联痛点
Struggle with marketing and distribution as a technical founderBalancing passion projects with income-generating workConcerns about AI impact on small SaaS businesses
MVP 范围
Core feature 1: Support analysis of a single
common app archetype (e.g.
a meeting scheduler with time-slot rules).
Core feature 2: Generate the 'Logic Manifest' and a new AI-agent backend scaffold (with placeholder tools).
Core feature 3: Provide a side-by-side view of the original logic flow vs. the proposed AI-driven flow.