Over the past few years, I’ve dedicated myself to building SCCS, the Standard Cannabis Classification System, and today I’m proud to introduce it to the public.

# What is SCCS and why I built SCCS: 

Over more than a decade of trying thousands of strains from a wide range of cultivators, one issue became clear. Much of the industry still classifies cannabis using indica and sativa labels based on genetic history or how cultivators and testers say a strain feels. The problem is that neither approach is reliable.

What truly determines how a product feels is its chemical profile, especially how its terpene composition interacts with cannabinoids. Relying on lineage alone, such as labeling every GMO as indica dominant, overlooks a critical reality. Cannabis chemistry is shaped as much by environment and handling as by genetics. The same strain can test differently across cultivators and even across batches from the same grower. One batch may express as heavily indica dominant, while another is balanced.

Yet classifications often remain unchanged, even when the chemistry does not. Terpene content varies significantly based on grow environment, phenotype, harvest timing, curing, and storage. I have personally experienced buying a strain labeled indica that got me very sedated, only for the next batch under the same name to feel like a balanced hybrid. When classification is rooted in assumption instead of data, consistency becomes impossible.

Classifying a strain based on how the cultivator or a cannabis tester reported feeling of  the strain is also unreliable since individual experience can vary widely depending on tolerance, dosage, method of consumption, mixing strains, mood, environment, and even expectations.

SCCS was built to solve this.

Its algorithm is grounded in thousands of published scientific and medical studies on terpenes, cannabinoids, and their pharmacological interactions. Instead of relying on tradition or personal experience, SCCS analyzes verified lab Certificates of Analysis, normalizes terpene and cannabinoid values, and applies an evidence based model to generate a precise chemotype score.

The result is a clear, scientific classification rooted in measurable chemistry.

# Why SCCS outperforms AI:

AI cannot extract numerical terpene and cannabinoid values, normalize them, and weigh them against evidence from published biochemical research. SCCS does precisely that: it ingests verified lab data, standardizes values, applies a purpose built algorithm rooted in phytochemical science, and produces a repeatable chemotype score tied to actual chemistry.

# What SCCS will do for the cannabis industry:

Brands can use SCCS in order to present science backed classifications while at the same time customers can use this for clearer expectations helping reduce confusion and returns while strengthening brand trust. 

**How it works:**

1. Upload a cropped photo of a COA that shows terpenes and compounds for best results. SCCS extracts the data. If a value is missing you can add it manually. (Photo 1)

2. Tap Run SCCS. (Photo 2)

3. SCCS processes the input and displays the strain classification and expected effects. (Photo 3)

**SCCS is currently protected by password to prevent server overflow. To request access:**

1. Go to [neehaw.us](https://neehaw.us) and enter your email in the newsletter window and subscribe. (Photo 4)

2. You will receive an email shortly with a password.

3. At the bottom of [neehaw.us](https://neehaw.us) tap Login and sign in with the password to access SCCS. (Photo 5)

**How to add SCCS to your home screen for quick access:**

1. Tap the arrow in a box button on the bottom of the screen. (Photo 6)

2. Tap “Add to Home Screen”. (Photo 7)

3. Tap “Add” on the top right of the screen. (Photo 8)

Introducing SCCS: A New Standard for Cannabis Classification 🧬