AI-powered semantic clustering

Semantic clustering is automatic grouping of texts by semantic proximity (via embeddings — 894): a large array (thousands of queries, reviews, inquiries, products, pages) is taken and the AI merges semantically similar items into clusters. This helps see the main themes, demand structure, duplicates and gaps — e.g. for a semantic content strategy, inquiry analysis or tidying up the catalog. Honestly about approximateness, this is key: clustering is not an exact science. Boundaries between groups are often blurry, the same text can belong to several themes, and the AI sometimes merges what you would split, or vice versa. The number and composition of clusters depend on settings, an ideal 'single correct' partition does not exist. So the result is a draft map of meanings that must be interpreted. Honestly about the human's role: clusters need to be made sense of and labeled (give names to themes), disputed cases checked — the AI groups, but the human gives meaning and conclusions. It is a tool for exploration and navigation, not an automatic verdict. Honestly about quality: it depends on the quality and homogeneity of texts and the chosen embedding model; on noisy data clusters are less distinct. Honestly about the effect: it saves enormous time on manually parsing large arrays and reveals structure but does not make decisions for you. Honestly about cost: building embeddings and processing are paid resources, usually moderate. Honestly about access: data for clustering is needed. An important boundary: this is clustering; semantic search — 874; review/sentiment analysis — 875; embeddings — 894. Picture this: instead of 'thousands of queries/reviews without structure' — a map of themes that a human makes sense of and uses. The base price starts from 40,000 ₽ (depends on volume and tasks).

• Thousands of queries/reviews/products without clear structure.
• Grouping a large array by themes manually is unrealistic.
• The main demand themes and content gaps are unclear.
• Duplicates and overlaps in the catalog/themes are not visible.

• Semantic clustering of the array via embeddings (894)
• Grouping queries/reviews/products/documents by meaning
• Highlighting main themes, duplicates and gaps
• Help with interpreting and labeling clusters
• Honest boundaries (approximation; blurry boundaries; a human needed)
• Tuning the number/granularity of clusters for the task
• A link with search (874) and review analysis (875)
• Handover of the theme map and review with you

• A clear map of themes from a large text array
• Main themes, duplicates, gaps are visible
• Time savings on manual parsing
• Honest boundaries (a draft map; a human interprets)

• We collect the text array and the clustering goal; choose the model
• We build embeddings and clusters, tune granularity
• We interpret and label with you, honestly set boundaries

• Will clustering give the single correct partition?

  No, and that is honest: it is an approximation. Boundaries between groups are blurry, one text can belong to several themes, and the number and composition of clusters depend on settings. An ideal 'single correct' partition does not exist. The result is a draft map of meanings that a human interprets. We tune granularity for your task, but passing off clustering as absolute truth would be dishonest.

• Will the AI understand and label the themes itself?

  Group — yes, but making sense of and naming themes, checking disputed cases must be done by a human. The AI merges what is similar in meaning, while the human gives conclusions and names. We help with interpretation, but it is joint work: the AI gives structure, you (with us) give meaning. It is a tool for exploration, not an automatic verdict.

• What does cluster quality depend on?

  On the quality and homogeneity of texts and the chosen embedding model. On clean, homogeneous data clusters are more distinct; on noisy and heterogeneous data more blurry. We pick the model and settings for your data and honestly note where the partition is less confident rather than pass off any result as ideal.