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Use Cases for Every Industry

From semantic search to anomaly detection, see how leading teams use Tensorus to power their AI applications.

Use Case #1

Semantic Search

Build Google-quality search for documents, images, or any content. Understand meaning, not just keywords.

Search 10M+ documents in < 50ms
Multi-language support
Hybrid keyword + semantic
❌ Problem

Traditional keyword search fails on synonyms, context, and intent. Users type "affordable housing" but your content says "low-cost apartments"—no match.

✅ Tensorus Solution

Embed documents and queries into semantic space. Search understands meaning, returning relevant results even with different wording.

# Embed & index documents
client.insert(id, embedding, metadata)
# Search semantically
results = client.search(query, k=10)
📈 Results
40%
Higher relevance
2.5x
More engagement
< 50ms
Query latency
Use Case #2

Recommendation Engines

Personalized recommendations that drive revenue. Netflix-style content discovery.

Real-time personalization
Collaborative + content filtering
Cold-start handling
❌ Problem

Generic recommendations don't convert. "Customers who bought X also bought Y" ignores individual preferences and context.

✅ Tensorus Solution

Multi-dimensional embeddings capture user preferences + item features + context. Find items similar to user's taste vector.

# User preference vector
user_vec = compute_preferences(history)
# Find similar items
recs = client.search(user_vec, filters={
"in_stock": True,
"price_max": budget
})
📈 Results
+28%
Click-through
+15%
Revenue
94%
User satisfaction
Use Case #3

Anomaly Detection

Catch fraud, security threats, and system failures before they escalate.

Real-time monitoring
Multi-variate analysis
Low false-positive rate
❌ Problem

Rule-based systems generate thousands of false positives. Security teams waste 80% of time investigating noise.

✅ Tensorus Solution

Learn normal patterns from historical data. Flag behavior that deviates significantly from baseline—even novel attack types.

# Index normal patterns
client.insert(normal_embeddings)
# Check new events
distance = client.nearest(event)
if distance > threshold:
alert("Anomaly detected!")
📈 Results
92%
Detection rate
-70%
False positives
< 100ms
Response time
Use Case #4

Knowledge Graphs

Connect concepts, entities, and relationships. Power intelligent question-answering.

Entity resolution
Relationship inference
Multi-hop reasoning
❌ Problem

Graph databases don't understand semantics. "Apple" (fruit) vs "Apple" (company) requires manual disambiguation.

✅ Tensorus Solution

Embed entities in semantic space. Similar entities cluster naturally. Find related concepts even without explicit edges.

# Index entities
client.insert("Apple_company", emb)
client.insert("iPhone", emb)
# Find related
related = client.search("Steve Jobs")
# → Apple, iPhone, Pixar...
📈 Results
85%
QA accuracy
3-hop
Reasoning depth
1M+
Entities

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