brand

A composable pipeline for brand name generation, evaluation, and availability checking.

Not just domain checking — a full brand naming workbench. Generate candidates, score them on phonetics, linguistics, and sound symbolism, filter by availability across platforms, and persist every intermediate result for inspection and branching.

To install:

pip install brand
pip install brand[phonetics]   # adds BLICK, epitran, panphon
pip install brand[all]         # everything including AI generation

Quick Start

Evaluate a single name:

import brand

result = brand.evaluate_name("figiri")
print(result["scores"])
# {'syllables': 3, 'stress_pattern': 'unknown', 'spelling_transparency': 0.88,
#  'sound_symbolism': {'profile': 'modern/sharp', ...}, 'novelty': 1.0, ...}

Pipelines

The core abstraction is a pipeline — a sequence of Generate, Score, and Filter stages that progressively enriches, scores, and narrows a set of candidate names.

Run a pre-configured template

results = brand.run_pipeline("tech_startup", names=["figiri", "lumex", "voxen"])

for candidate in results["candidates"]:
    print(f"{candidate['name']}: {candidate['scores']}")

Available templates:

Template	Use case
quick_screen	Fast local-only checks. No network. Screen thousands in seconds.
tech_startup	.com + GitHub + full phonolinguistic battery
python_package	PyPI + GitHub + short name + keyboard distance
consumer_global	Cross-linguistic safety + trademark + .com mandatory
developer_tool	Short (4-6 chars), easy to type, PyPI + npm + GitHub
ai_ml_product	.ai TLD preferred, modern sound profile, high distinctiveness
consultancy	Professional tone, .com mandatory, company registration
open_source	GitHub + PyPI + npm, fun/memorable, less trademark concern
youtube_channel	YouTube availability, memorable, pronounceable
full_audit	Every scorer, every check. Thorough but expensive.

Build a custom pipeline

from brand import Generate, Score, Filter, run_pipeline

results = run_pipeline(
    [
        Generate("cvcvcv_filtered"),
        Score(["syllables", "spelling_transparency", "sound_symbolism", "novelty"]),
        Filter(top_n=500, by="spelling_transparency"),
        Score(["dns_com", "dns_io"]),
        Filter(rules={"dns_com": True}),
        Score(["whois_com"]),
        Filter(rules={"whois_com": True}),
        Score(["github_org"]),
    ]
)

print(f"{len(results['candidates'])} names survived the pipeline")
print(f"Artifacts saved to: {results['project_dir']}")

Every run persists intermediate artifacts to disk. You can resume from any stage, branch from any checkpoint, and inspect what happened at each step.

Registry

All components are discoverable:

import brand

# See what's available
list(brand.scorers)  # ['syllables', 'phonotactic', 'dns_com', ...]
list(brand.generators)  # ['cvcvcv', 'ai_suggest', 'morpheme_combiner', ...]
brand.list_templates()  # ['tech_startup', 'python_package', ...]

# Inspect a scorer
brand.scorers["dns_com"].cost  # 'cheap'
brand.scorers["dns_com"].requires_network  # True
brand.scorers["whois_com"].latency  # 'slow'

Register a custom scorer

@brand.scorers.register("my_vowel_ratio")
def my_vowel_ratio(name: str) -> float:
    vowels = sum(1 for c in name.lower() if c in "aeiouy")
    return vowels / len(name)


# Now use it in a pipeline
results = brand.run_pipeline(
    [
        Generate("from_list", params={"names": ["alpha", "beta", "omega"]}),
        Score(["my_vowel_ratio", "syllables"]),
    ]
)

Generators

# CVCVCV combinatoric (371k+ candidates)
names = list(brand.generators["cvcvcv"]())

# CVCVCV with few-uniques filter
names = list(brand.generators["cvcvcv_filtered"]())

# Custom CV pattern
names = list(brand.generators["pattern"](pattern="CVCCV"))

# Morpheme combiner (portmanteau-style)
names = list(
    brand.generators["morpheme_combiner"](
        prefixes=["lum", "vox", "syn"],
        suffixes=["ify", "io", "ar"],
    )
)

# AI-assisted (requires 'oa' package)
names = list(brand.generators["ai_suggest"](context="AI data tools"))

# From a file or list
names = list(brand.generators["from_list"](names=["alpha", "beta"]))

Scorers

Phonetic (local, fast)

• syllables — syllable count via CMU dict or vowel heuristic
• stress_pattern — stress digits from ARPAbet (trochaic "10" is ideal)
• phonotactic — BLICK well-formedness (0 = perfect English phonotactics) [requires brand[phonetics]]
• articulatory_complexity — place-of-articulation transitions [requires brand[phonetics]]
• sound_symbolism — front/back vowel and voiceless/voiced ratios mapped to brand archetypes

Linguistic (local, fast)

• novelty — word frequency inverse (1.0 = novel, 0.0 = very common)
• existing_word — collision with known English words
• spelling_transparency — grapheme-to-phoneme ambiguity score (flat lookup; see pronunciation_entropy for a richer alternative)
• pronunciation_entropy — positional, context-aware pronunciation ambiguity measured as Shannon entropy in bits. Models how initial Y before a consonant is far more ambiguous than terminal Y, terminal E (silent? schwa?) vs. medial E, open vs. closed syllables, and intervocalic consonant voicing. Supports cross-linguistic mode: pronunciation_entropy('levole', languages=('en', 'fr')) merges English and French phoneme distributions, capturing real-world ambiguity for names that must work across language communities. Lower = more unambiguous. See also pronunciation_entropy_detail() for per-grapheme breakdowns.
• substring_hazards — profanity substring scan

Linguistic (network)

• cross_linguistic — word frequency in 11 languages
• phonetic_neighbors — similar-sounding words via Datamuse API

This is the beginning of the README. Read the rest at the original. Read the full README ↗