You have 3 years of customer support tickets, team meeting notes, and internal documentation.
Someone asks "What are the most common problems customers complain about?"
You spend a week reading through documents, making notes, and trying to find patterns manually.
The answer was in there. You just needed a way to see across all of it at once.
POWERFUL for any organization with accumulated text. Turns unstructured history into structured insight.
Corpus analysis looks at a collection of documents as a whole rather than one at a time. Instead of reading 500 support tickets individually, you ask: "What themes appear across all of these? What topics keep coming up? What patterns emerge?"
The AI processes your entire corpus, clusters similar content together, identifies recurring themes, and surfaces the patterns you would never spot reading document by document. Three years of customer complaints become "47% are about billing confusion, 28% are about onboarding, 15% are about improvement suggestions."
Reading documents one by one tells you what happened. Corpus analysis tells you what keeps happening.
Corpus analysis solves a universal problem: how do you understand what a large collection of information is really about? Any organization with accumulated documents, communications, or records benefits from seeing patterns across the whole.
Take a collection of documents. Group similar ones together. Identify what themes emerge most frequently. Surface the patterns that would be invisible reading one at a time. Turn "we have a lot of data" into "here is what our data says."
This corpus contains 20 support tickets. Click "Analyze Corpus" to discover what themes emerge.
Click "Analyze Corpus" to see results
Tickets will appear here after analysis
Discover latent themes
Algorithms like LDA (Latent Dirichlet Allocation) analyze word co-occurrence patterns to discover topics that exist across your documents. No predefined categories needed. The themes emerge from the data itself.
Group similar documents
Convert documents to vectors (embeddings) and group similar ones together. A support ticket about "payment failed" clusters with other payment-related tickets. You see natural groupings emerge.
Count what matters
Extract entities, keywords, or phrases and count their frequency across the corpus. Simple but powerful. "Refund" appears in 847 tickets. "API error" appears in 312. "Login" appears in 289.
Your operations lead needs to prioritize support improvements. You have 3,000 tickets from the past 2 years. Reading them would take weeks. Corpus analysis processes all of them and returns: "1. Billing confusion (47%), 2. Onboarding friction (28%), 3. Improvement suggestions (15%), 4. Integration issues (7%), 5. Account access (3%)."
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You run analysis on raw documents. The top "theme" is email signatures, meeting boilerplate, and template text. The actual content is buried under noise. Your insights are about formatting, not substance.
Instead: Strip boilerplate, remove signatures, clean formatting artifacts before analysis. Garbage in, garbage out.
Corpus analysis shows "pricing" is your top topic. You assume customers are confused about pricing. Turns out half those mentions are your team discussing pricing internally. The corpus mixed two very different document types.
Instead: Segment your corpus by source, type, or timeframe. Analyze customer-facing vs internal separately.
The algorithm clusters documents into 5 groups. Group 3 contains both "improvement suggestions" and "bug reports" because they use similar language. You treat them the same. They need opposite responses.
Instead: Treat automatic clustering as a starting point. Validate clusters by sampling. Refine with human judgment.
You have learned how to extract patterns from document collections. The next step is understanding how to use these patterns to detect specific topics in new incoming documents.