Your team answers the same question 47 times a day.
"How do we handle X?" "What did we do last time?" "Who knows about Y?"
Each question gets answered. Each answer disappears. Tomorrow, someone asks again.
The pattern is already there. You just keep solving it one-off instead of once.
LAYER 3 - Pattern Extraction transforms scattered observations into actionable intelligence.
Your support tickets, customer communications, team questions, and process exceptions all contain patterns. The same three issues cause 80% of your problems. The same five questions eat up your team's time. The same mistakes get made by every new hire.
Pattern extraction is the process of identifying these recurring structures automatically. Instead of one person noticing 'we get a lot of questions about X,' the system surfaces that you answered 47 variations of the same question last month. Instead of gut feel, you get evidence.
Get it wrong and you're forever solving symptoms. Get it right and you fix root causes once.
Pattern extraction solves a universal problem: when you have too much data to review manually, how do you find the recurring themes that matter?
Collect observations. Group by similarity. Count occurrences. Surface the clusters that cross a threshold. This transforms scattered signals into actionable intelligence.
Below are 15 real questions from a team over one week. Click "Extract Patterns" to see what recurring themes emerge.
Click "Extract Patterns" to analyze
When you know what to look for
Define keywords, phrases, or structures to detect. "Any message mentioning refund + complaint" or "Any ticket with delivery + late." Fast, predictable, but only catches what you define.
When you want patterns to emerge
Convert data to embeddings, group similar items together, then label the clusters. "These 200 tickets are all about billing confusion." Discovers patterns you did not know existed.
When context matters
Use a language model to read items and extract themes. "Summarize the top 5 recurring issues in these 500 messages." More nuanced than rules, more structured than clustering.
Your team answers dozens of questions daily. Some are one-offs. Some repeat constantly. This flow analyzes a month of team communication, extracts recurring themes, ranks them by frequency, and surfaces the documentation gaps you should actually fix.
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You had 30 tickets last month. You ran pattern extraction and found 'billing questions are common.' You restructured your billing page. Next month you learn 20 of those 30 tickets came from one confused customer.
Instead: Set a minimum sample size. Patterns need volume to be meaningful.
Your system surfaces 15 patterns. You try to address all of them. You spread thin, fix nothing completely, and next month you have 15 slightly different patterns.
Instead: Rank patterns by frequency and impact. Fix the top 3 before moving on.
Beautiful dashboard. 'Top issues this week' displayed prominently. Nobody looks at it. The same patterns show up month after month. The extraction was the point, not the beginning.
Instead: Patterns must trigger workflows. Detection without action is expensive noise.
You've learned how to surface recurring themes from scattered data. The natural next step is understanding how to detect when something breaks the pattern.