AI-Driven Flavor Evaluation System: The Three Core Models
Guiding Belief
We believe flavor is a process.
Not a final score, but a journey — one that can be understood, learned, and trusted.
FCA’s AI system is built around three core models that work across different dimensions of sensory evaluation: from professional cupping logic to individual preferences to global data benchmarks. These models are not designed to replace human judgment — they’re here to help professionals see more clearly, speak more logically, and connect more precisely.
#1 CertiScore | Standard Cupping Model
A professional anchor for “natural scores,” aligned with Q Grader training systems.
- Mirrors CQI Q Grader standards, focusing on structure, defects, and balance.
- Useful for baseline scoring, processing comparisons, and reference calibration.
- Supports green bean screening, cupping training, and sample consistency tracking.
- Ideal for experienced cuppers, training institutions, and origin labs.
Technical Highlights:
Standardized cupping form × Expert sensory input × Reverse natural score modeling
#2 BeanSeeker | Emotional Scoring Model
A collaborative tool to express individual flavor preferences.
- Combines emotional intensity with sensory attributes in a two-dimensional framework.
- Supports personal scoring logic, flavor storytelling, and consumer training.
- Designed for brand R&D teams, taster communities, and education programs.
- Core to FCA’s flexible and expressive flavor language system.
Technical Highlights:
Sensory vocabulary dataset × Weighted distribution model × Emotional coordinate mapping
#3 Omni | Flavor Fingerprint Hybrid Model
A fast-screening engine combining chemistry and market-scale data.
- Integrates NIR spectroscopy, global sensory databases, and behavioral preferences.
- Designed for pre-screening origin samples, blend matching, and batch management.
- Suitable for roaster sourcing, exporter inventory systems, and smart recommendation engines.
- A key pillar for FCA’s automation and scalable quality evaluation efforts.
Technical Highlights:
NIR data × Historical sample matching × Multi-model hybrid inference engine
Which Model Fits Your Needs?
| Your Goal | Recommended Model | Ideal User Role |
|---|---|---|
| Align with the Q Grading system | CertiScore | Q Graders, Origin Organizations |
| Develop a personal scoring style | BeanSeeker | Tasters, Brand R&D Teams |
| Quickly screen large volumes | Omni | Exporters, Processing Facilities |
Expansion Modules: Six Add-On Systems
Designed for future applications and specific use cases:
- TIM — Terroir Identification Model (for origin organizations)
- PIM — Processing Impact Model (for farm training)
- RAM — Roast Alignment Model (for roasters)
- CSC — Consumer Segmentation Clusters (for brand R&D)
- SES — Sustainability and Ethics Scoring (for ESG programs)
- CBM — Championship Benchmark Model (for competition prep)
These modules extend the real-world impact of the three core models.
Who Is It For?
This system is for those who want to:
- Build stronger flavor judgment
- Connect with the language of the market
- Make flavor more consistent, reliable, and expressive
FCA’s AI isn’t here to make decisions for you — it’s here to help you train, refine, and communicate your judgment more clearly.