rAIflow — AI Colour Intelligence

Professional colour intelligence for real spaces, currently supporting Benjamin Moore and Graimondi paint colours.

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rAIflow — AI Colour Intelligence

Project history

Development of rAIflow began at the end of May 2026. The core colour-intelligence platform, Shopify interface, Cloudflare Worker architecture and early recommendation systems were already under development before OpenAI Build Week.

During Build Week, the existing project was substantially extended and refined using ChatGPT with GPT-5.6, Codex and Claude. The Build Week work included code review, architecture decisions, interface improvements, debugging, mobile and 3D interaction refinements, colour-data validation, spectrophotometer corrections, server-side engine work and technical documentation.

This submission presents the working rAIflow platform as it exists now. It does not claim that GPT-5.6 runs inside the customer-facing application. GPT-5.6 and Codex were used as development tools to help build, review and improve the project during Build Week.

Inspiration

Choosing paint colours is difficult because colours cannot be judged in isolation. Flooring, cabinetry, furniture, fixed finishes, undertones, depth and surrounding colours all affect how a paint colour will appear.

Traditional colour selection often relies on small chips, inspiration photos or isolated swatches. Those approaches can overlook the relationships between the colours already present in a real space and the paint colours being considered.

What it does

rAIflow is a browser-based colour consultation and palette-building platform that combines real-world colour input, a proprietary server-side colour intelligence engine and an interactive 3D colour environment.

Users can begin by:

  • Uploading a room or material photo
  • Entering colours manually
  • Exploring the 3D Colour Space directly

rAIflow helps users identify relevant colours, build coordinated palettes, compare relationships and understand how colour families, undertones, contrast and depth work together.

The current production catalogue supports Benjamin Moore and Graimondi paint colours. The architecture is designed so additional manufacturer collections can be incorporated in the future through structured colour data, metadata and catalogue-specific relationships.

How it works

The user-facing Shopify theme handles photo input, colour sampling, palette interaction, reports and the interactive 3D environment.

A Cloudflare Worker hosts the proprietary colour intelligence engine and catalogue data. It processes structured requests from the storefront and returns recommendation results without exposing the underlying matrices, weighting rules or catalogue relationships to the browser.

The system includes:

  • Neutral and colourful recommendation paths
  • Colour-family, undertone and depth relationships
  • Complementary, split-complementary, triadic and tetradic schemes
  • Monochromatic and directional colour movement
  • Floor, cabinetry, trim and accent handling
  • LRV, descriptions, aliases and catalogue metadata
  • Searchable interactive 3D colour visualization
  • Printable and savable palette reports

Build Week work

During OpenAI Build Week, ChatGPT with GPT-5.6 and Codex were used to accelerate substantial work on the existing platform, including:

  • Reviewing and navigating a large Shopify theme and Cloudflare Worker codebase
  • Mapping the frontend-to-Worker architecture
  • Planning and implementing interface changes
  • Refining desktop and mobile controls in the 3D Colour Space
  • Improving Stellar Mode behaviour and axis presentation
  • Debugging colour-selection and add-colour interactions
  • Moving and protecting proprietary recommendation logic server-side
  • Reviewing recommendation behaviour and preserving existing algorithm rules
  • Validating colour catalogues, aliases, names, LRV values and descriptions
  • Applying spectrophotometer-based corrections to colour data
  • Documenting version changes and technical decisions

Codex was particularly valuable for working across large files, tracing interactions between frontend and server code, proposing targeted changes and helping preserve the existing proprietary engine while new features and corrections were added.

Claude was also used during Build Week for code review, implementation support, debugging and comparison of technical approaches. This submission documents that mixed-tool workflow honestly while highlighting the meaningful role of GPT-5.6 and Codex.

Current catalogue support

rAIflow currently generates recommendations from:

  • Benjamin Moore paint colours
  • Graimondi paint colours

These are real, purchasable paint catalogues rather than generic RGB swatches. The system can be expanded to support additional paint collections in the future without changing the core consultation workflow.

Input accuracy and current limitation

rAIflow can only reason from the colour information it receives. When a user works from a phone photo, accuracy can be affected by camera processing, lighting, glare, white balance, viewing angle and whether the user selects the correct representative points in the image.

The recommendation engine can be highly precise, but the quality of the final result is limited by the quality of the input measurement. At present, the main constraint on precise colour selection is not the palette engine itself; it is the variability of mobile-camera capture and manual sample-point selection.

For users who require measurement-grade input, the architecture can be expanded to accept readings from devices such as Nix sensors, spectrophotometers and other colour-scanning tools. These devices can provide more controlled colour measurements than a phone photograph, allowing rAIflow to begin from a more precise representation of the actual surface colour.

Architecture

Room photo, manual colours or 3D selections
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                    v
         Browser colour-processing tools
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                    v
       Cloudflare Worker colour engine
     Proprietary matrices and recommendation logic
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                    v
       Colour Results and 3D Colour Space
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                    v
          Saved or printed report

What makes it different

rAIflow is not a generic AI paint picker. It combines domain-specific colour logic, real paint catalogues, structured palette relationships and a visual colour-discovery environment.

The platform was created from practical colour-consultation experience and is designed to help users understand not only which colours are recommended, but why those colours relate to the materials already present in their space.

Privacy and intellectual property

The original uploaded photo is processed in the browser as part of the colour-selection workflow. The proprietary recommendation logic runs server-side on Cloudflare Workers.

The public-facing project documentation describes the system architecture and Build Week work without publishing confidential matrices, weighting logic, spectrophotometer-derived catalogue data or recommendation rules.

Future expansion

The same architecture can be expanded to support:

  • Additional paint manufacturers and colour catalogues
  • Direct input from Nix sensors and other colour-scanning tools
  • Spectrophotometer and measurement-device integrations
  • More advanced palette comparison and explanation
  • Manufacturer-specific product recommendations
  • Professional designer and retailer workflows
  • Saved project workspaces and multi-room palette planning

As mobile-camera colour capture improves, photo-based input should also become more consistent. Until then, scanner integration offers a practical path to more precise surface-colour measurements for users who need higher confidence.

rAIflow demonstrates how AI-assisted development can accelerate the creation of a complex, domain-specific colour platform while keeping the customer-facing recommendation engine grounded in proprietary colour logic and real paint data.

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