Short answer: use a hybrid pipeline — start with a curated list of brand variants + deterministic checks (exact/regex, domain/handle matching), add fuzzy matching for misspellings, and add a semantic layer (embeddings or a classifier) to catch paraphrases and implied mentions. Monitor performance with a labeled sample and iterate with human-in-the-loop.

Detailed, practical plan

1. Build your brand vocabulary

• Canonical names, legal forms, abbreviations, common misspellings, punctuation/diacritics variants.
• Domains, social handles, hashtags, common product names, acronyms.
• Example for "Acme Co.": ["acme", "acme co", "acme.co", "acme-corp", "@acme", "#acme"].

2. Preprocess text

• Normalize unicode, lowercase, remove zero-width chars, optionally strip punctuation except in handles/URLs.
• Extract URLs, @handles, and hashtags separately (they’re high-precision signals).

3. Fast deterministic checks (high precision)

• Exact token match with word-boundary regex (catches canonical forms).
• URL/handle/domain match (domain acme.co or @acme is definitive).
• Use word-boundary regex to avoid partial matches (e.g., "\bacme\b" so "macme" doesn’t match).

Example (Python): import re pattern = re.compile(r'\b(acme|acme\s+co|acme.co|acmecorp)\b', re.I) bool(pattern.search(text))

4. Fuzzy matching (catch typos)

• Use RapidFuzz or Levenshtein-based scoring for short strings.
• Compare tokens/ngrams from the prompt against aliases. Tune threshold (typical start: 80–90 for short names).
• Be conservative (higher threshold) for short names — low thresholds produce false positives.

5. Semantic matching / paraphrase detection (catch implied mentions)

• Use sentence embeddings (e.g., sentence-transformers) and cosine similarity between prompt embeddings and one or more "brand description" embeddings (a short canonical description, product names, slogans).
• For scale, index embeddings with FAISS, Milvus, Pinecone.
• Typical cosine thresholds: 0.6–0.8 (must be tuned on your labeled data).

Example (sentence-transformers): from sentence_transformers import SentenceTransformer, util model = SentenceTransformer('all-MiniLM-L6-v2') brand_vec = model.encode("Acme Company - maker of widgets", convert_to_tensor=True) prompt_vec = model.encode(prompt, convert_to_tensor=True) sim = util.cos_sim(prompt_vec, brand_vec) match = sim >= 0.65 # tune threshold

6. Supervised classifier (if you have labeled examples)

• Train a binary text classifier (transformer fine-tune or classical TF-IDF + logistic regression) to label prompts as "mentions brand" / "doesn't".
• Good when you need to capture tone, context, or disambiguate homonyms (e.g., “apple” the fruit vs Apple Inc.).
• Evaluate with precision/recall/F1 and use calibration/thresholding to meet business goals (e.g., favor precision if false positives are costly).

7. Disambiguation & context

• Use NER and dependency parsing (spaCy) when a token could be multiple things.
• If brand name matches a common word, require co-occurring signals (product name, domain, context words) to confirm.

8. Human-in-the-loop + active learning

• Sample false positives/negatives regularly and add them to training/alias lists.
• Use an active learning loop: surface low-confidence detections for human review to improve classifier and alias list.

9. Scale & infrastructure

• Small volumes: regex + RapidFuzz on incoming text.
• Medium/Large: precompute embeddings, index with FAISS/Milvus/Pinecone for fast nearest-neighbor queries, run real-time inference on new prompts.
• Add streaming ingestion, metrics, and autoscaling as needed.

10. Quality metrics and thresholds

• Create a labeled test set and measure precision, recall, F1. Track these over time.
• Choose threshold based on whether you care more about false positives (precision) or false negatives (recall).
• Monitor rate of “unknown / low-confidence” detections as a signal for updating aliases or retraining.

11. Privacy, logging, and compliance

• Avoid storing PII unnecessarily. Anonymize or hash content if required.
• Follow retention and consent rules for user-submitted prompts.

Quick recommended starter stack

• Extraction & rules: Python re, URL parsing
• Fuzzy matching: RapidFuzz
• NER/Parsing: spaCy
• Semantic detection: sentence-transformers (all-MiniLM-*), embeddings + FAISS or Pinecone for scale
• Classifier: scikit-learn for TF-IDF models; Hugging Face transformers for fine-tuning
• Monitoring: Kibana/Looker/Grafana, small labeled-review app for human validation

Practical rollout (minimal effort → full solution)

1. Implement exact/regex + handle/domain matching + alias list (fast, high precision).
2. Add RapidFuzz for common misspellings.
3. If you still miss cases or need semantic coverage, add embeddings + similarity search.
4. If you have labels or need contextual accuracy, train a classifier and run human-in-the-loop for low-confidence items.

If you want, I can:

• Draft the initial alias/regex set given your brand name.
• Provide ready-to-run Python snippets tailored to your tech stack.
• Outline an evaluation dataset sampling plan and thresholds to start with.

Which would you like next?