SPS Design DNA

SPS Design DNA is a self-reported system for landscape design projects that claims to structure scattered project intake into an immutable, traceable "Design DNA" using AI and evidence-based workflows. The author states it was built as a Laravel backend with PHP and MySQL, incorporating OpenAI Codex for development assistance. It includes features like evidence intake, classification, immutable snapshot sealing, and AI-generated Design DNA claims tied to verified sources.

The system is described as provider-neutral, with strict validation rules and atomic generation lifecycle management. The author notes that the project was submitted to the OpenAI 2026 hackathon and has not yet reached production or customer traction.

The description states that SPS Design DNA is an "evidence-first system for landscape design projects." It collects, reviews, classifies, and seals project evidence into immutable snapshots. These snapshots are then used to generate a structured "Design DNA" containing:

• Site conditions
• Requested changes
• Items to preserve or remove
• Material and style direction
• Planting direction
• Functional requirements
• Assumptions, unknowns, and conflicts
• Adaptive follow-up questions
• Evidence citations

The system is described as preventing unsupported AI statements from becoming project facts by requiring claims to cite verified evidence or be marked as assumptions.

Inference The system appears to be a backend workflow tool for organizing design project intake and generating structured outputs using AI, with emphasis on traceability and integrity of information.

The author states that SPS Design DNA was created to turn "scattered intake" into a "structured, traceable source of truth." It positions itself as an evidence-first approach to landscape design project management.

It claims to be:

• An immutable system for managing design project information
• Provider-neutral in its AI integration
• Designed to prevent unsupported AI statements from becoming facts
• A tool that links AI-generated claims to verified evidence

Inference The positioning is evolving from a hackathon prototype into a potential production-grade workflow tool, but the description does not indicate any prior market traction or adoption.

The author states that SPS Design DNA targets landscape designers who receive project information through "scattered messages, reference images, sketches, files, site notes, and verbal instructions."

It is implied that these users are looking for a way to:

• Organize fragmented intake
• Ensure clarity in design decisions
• Prevent miscommunication or loss of requirements

Inference The primary customer appears to be individual or small teams of landscape designers working on projects with unclear or scattered inputs. No specific ICP segmentation is described.

The description does not contain any information about pricing, monetization, or business model.

Not evidenced.

The system was built using:

• Laravel backend
• PHP and MySQL-compatible persistence
• Eloquent models
• Canonical JSON
• SHA-256 integrity verification
• Database transactions
• Extensive automated testing (more than 600 tests)

OpenAI Codex was used for architecture exploration, implementation planning, writing services, test generation, Git workflows, and validation.

Services include:

• Evidence intake
• Evidence review
• Immutable snapshot sealing
• Snapshot verification
• Generation-attempt queuing
• Trusted identity assembly
• Atomic generation completion
• Lifecycle transitions

Inference The system is built with a focus on integrity, traceability, and safety. It uses backend technologies and AI integration for development rather than direct end-user interfaces.

The project was submitted to the OpenAI 2026 hackathon, indicating it is in an early stage of development.

It includes:

• More than 600 automated tests
• A backend workflow with multiple service components
• Immutable snapshot and integrity features

However, there is no evidence of revenue, customers, or adoption beyond the author's own account.

Not evidenced.

The description does not mention any competitors or how SPS Design DNA compares to existing tools in landscape design project management or AI-assisted workflows.

Not evidenced.

• No evidence of traction or adoption: The system is described as a hackathon submission with no customer base or revenue.
• Unproven market fit: There is no indication that landscape designers are currently seeking this type of solution.
• Limited scope: The project appears to be a backend tool with no visible UI or end-user interface.
• Self-reported maturity: All claims about functionality and testing are from the author, not independent verification.

1. What specific problems in landscape design project intake are you solving, and how do you know?
2. Have you validated your approach with actual landscape designers or firms?
3. How does this system integrate into existing design workflows or tools?
4. What is the path from prototype to production, and what resources are needed?
5. Are there any existing tools in the market that perform similar functions?
6. What are the technical limitations of the current implementation?