SchemaSafe: LLM Data Extraction & Validation for No-Code Workflows

Promising. This addresses a high-frequency technical friction point in a rapidly growing market (AI automation). It is a pure software solution with high repeatability.

In the source discussions, users emphasize "structured outputs" and "actual workflow-embedded AI logic." The biggest pain in embedding AI into workflows is unreliability. If an LLM returns a list when you expected an object, or wraps its JSON in markdown code blocks, the entire automation stops. Developers are currently building custom retry logic and parsing scripts to handle this, which is time-consuming and fragile.

• Automation Agencies: Building custom bots for clients.
• SME Ops Managers: Using tools like n8n or Make to automate back-office tasks.
• Solo Developers: Building AI-integrated apps who don't want to maintain their own validation boilerplate.

Source discussions on the n8n community forum show users specifically looking for partners who can handle "LLM integration for structured outputs" and "classification pipelines." This highlights that getting valid data out of the AI is the actual engineering challenge, not just sending a prompt.

A "Schema-as-a-Service" API.

1. User defines a JSON schema in the dashboard (e.g., for lead extraction).
2. User calls the SchemaSafe API from their automation tool instead of calling OpenAI directly.
3. SchemaSafe handles the prompt, forces JSON mode, validates the schema, and automatically retries with an error-correction prompt if the LLM fails.
4. Returns valid JSON to the workflow.

Reliability is the core value proposition. If a business process (like lead processing or trading bots mentioned in the source) stops working because of a formatting error, it costs human time and potential revenue. A $20/month fee for "peace of mind" and "set-and-forget" reliability is an easy sell for businesses.

Low to Moderate. The core logic involves wrapping existing libraries (like Pydantic or Instructor-style logic) into a multi-tenant API. The developer needs to handle API key management and usage tracking.

• Manual Workaround: Writing custom JavaScript/Python code blocks inside n8n to parse and fix strings.
• Library Alternatives: Python devs use Instructor, but this doesn't help the massive market of low-code users.
• Direct API: OpenAI's "JSON Mode" is better than it was, but still fails validation against complex schemas frequently.

The best way to distribute this is to become the "best friend" of n8n and Make users. Create "How-to" guides for these platforms titled "How to prevent your AI agents from breaking with invalid JSON."

Reaching 100 subscribers at $20/month is highly realistic. The n8n and Make ecosystems have tens of thousands of active users currently struggling with AI reliability. A utility-style SaaS that solves a specific technical headache for $20-$50/month is standard in the B2B automation space.