Semantic Conflict Intelligence
Conflicts Detected
37
↑ 12% vs last crawl
Borderline Cases
19
↓ 3% vs last crawl
URLs Processed
1,204
via Kafka stream · 40s
Est. Recovery
+22k
clicks/mo post-consolidation
Top Conflicts
37 active
| URL Pair | Similarity | Type | Action |
|---|---|---|---|
|
/blog/how-to-write-meta-descriptions
/guides/meta-description-best-practices
|
0.94
|
Semantic Dupe | 301 /blog → /guides |
|
/blog/internal-linking-strategy
/seo-tips/internal-links-for-seo
|
0.91
|
Intent Conflict | Merge, redirect weaker |
|
/blog/what-is-keyword-cannibalization
/learn/keyword-cannibalisation-explained
|
0.89
|
Semantic Dupe | Ironic. Fix immediately. |
|
/blog/page-speed-seo-impact
/technical-seo/core-web-vitals-guide
|
0.82
|
Partial Overlap | Differentiate scope |
|
/blog/seo-audit-checklist
/resources/technical-seo-audit-template
|
0.79
|
Partial Overlap | Review intent carefully |
Pipeline
8 stages
1
Sitemap Ingestion via Kafka
sitemap.xml published to a Kafka topic. Exactly-once delivery. Fault-tolerant consumer group.
2
Custom BPE Tokenisation
SEO-domain BPE model trained on 4.2M documents. Handles hyphenated slugs.
3
Cross-Encoder Embedding
Fine-tuned multilingual cross-encoder. Joint attention captures interaction effects bi-encoders miss.
4
Poincaré Hyperbolic Projection
Embeddings projected to hyperbolic space. Preserves hierarchical structure Euclidean geometry distorts.
5
Leiden Community Detection
Graph partitioned using Leiden. Resolution-limit-free improvement over Louvain.
6
Ensemble Conflict Scoring
Three signals fused via XGBoost meta-learner trained on 14k labelled pairs.
7
Counterfactual Impact Estimation
Causal model estimates post-consolidation traffic delta. STL decomposition for seasonal adjustment.
8
Recommendation Synthesis
Results ranked. Redirect map generated. Report exported.
💡 What this actually does
# stages 1–7, condensed
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(your_urls)
scores = cosine_similarity(embeddings)
# flag anything above 0.85. done. go for a walk.
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(your_urls)
scores = cosine_similarity(embeddings)
# flag anything above 0.85. done. go for a walk.