Stop spending 80% of your time finding data and writing queries. Kent remembers every analysis, automates recurring reports, and turns a 2-hour fire drill into a 2-minute answer.
Triage
The VP of Marketing walks over at 4:30 PM: "Can you pull the numbers on last week campaign performance by channel? I need it for a board slide by 5." You have 6 campaigns across 3 ad platforms, the data lives in your warehouse, and the attribution model changed last quarter so you need the new logic. Normally this is a 2-hour fire drill.
Kent is connected to your analytics warehouse. Ask: "Weekly campaign performance by channel for the last 7 days, using the multi-touch attribution model." Kent generates a CTE-based query that joins the campaigns, spend, and attribution tables, applies the correct 7-day window, and groups by channel. You run it, get the results, highlight the output table and run a "Write Executive Summary" skill. Kent drafts: "Paid search drove 43% of attributed conversions at $12.80 CAC, down 15% from the prior week. Email remained flat at 28%. TikTok spend increased 60% but conversions grew only 12%, suggesting creative fatigue." Copy into the slide. Done at 4:38.
Investigation
Monday morning. The weekly revenue dashboard shows a 23% drop. The CEO pinged you at 8 AM asking for an explanation before the 10 AM leadership meeting. You have 2 hours to investigate across 4 data sources: the transactions table, the pricing config, the feature flags log, and the deploy history. Because Kent is connected to your PostgreSQL warehouse and your deploy log via REST API, it can query all four sources from a single conversation. Last time this happened, it took your team 3 days to trace it to a pricing experiment.
Screenshot the revenue dashboard showing the drop. Kent reads it: "Revenue declined 23% starting Thursday at 2 PM EST." Ask Kent: "What changed Thursday afternoon?" with your warehouse connected. Kent queries the pricing_experiments table and deploy log, then reports: "Experiment pricing-v3-test (started Thu 1:45 PM) applied a 30% discount to the Enterprise tier for segment B. This segment represents 38% of total revenue. The experiment has no end date configured." You walk into the 10 AM meeting with the root cause, the revenue impact ($47K), and a recommendation to end the experiment. Your director asks how you found it so fast.
Quality
Marketing reports 12,000 monthly active users. Finance reports 8,400. The board deck has both numbers on different slides and nobody noticed until the CEO did. Marketing counts anyone who opened the app. Finance counts anyone who completed a core action. Neither definition is documented. This fight has been happening for 18 months.
Drop the Marketing dashboard spec, the Finance reporting query, and the product analytics schema doc into Kent. Over the next day, Background Discovery flags: "Metric conflict detected. marketing_active_users.sql defines MAU as any session event (login, page_view, app_open). finance_quarterly.sql defines MAU as users with at least one core_action event (purchase, subscription_change, api_call). The difference: 3,600 users who open the app but never take a core action." You create a shared Metric Definitions workspace in Kent, drop the resolution into it, and now every analyst on the team can ask Kent "how do we define MAU?" and get one answer.
Automation
Every Monday you run the same 4 queries, paste the results into a Google Sheet template, write a 3-paragraph summary, and post it to Slack. It takes 90 minutes. You have tried to automate it three times with scripts that broke when the schema changed. With your PostgreSQL warehouse and Slack both connected to Kent, the entire pipeline lives in one place. Last Monday you were out sick and nobody sent the report. Two VPs emailed asking where it was.
Set up a Ghost Mode rule: "Every Monday at 7 AM, run these 4 saved queries against the analytics warehouse, compare results to the prior week, and draft a Slack-ready brief with deltas and callouts for any metric that moved more than 10%." Monday morning, you wake up to a formatted report already waiting: "Revenue: $412K (+8% WoW). Churn: 2.1% (-0.3pp, lowest in 6 months). New trials: 847 (-12%, likely due to holiday weekend). Action item: Trial-to-paid conversion dropped from 34% to 28% -- investigate onboarding flow changes from Friday deploy." You review for 2 minutes, hit send. Even when you are on vacation.
Optimization
The quarterly cohort retention query takes 47 minutes to run. The data engineering team is backed up for 3 sprints. You need the results for a board presentation Friday. You are not a DBA and the query has 6 CTEs, 3 window functions, and joins across 4 tables. Every time you try to optimize it, you get different numbers.
Paste the 47-minute query into Kent. Kent analyzes the execution plan: "The bottleneck is the second CTE (user_first_actions) which scans the full events table (2.3B rows) without a date filter. Adding WHERE created_at >= dateadd(month, -12, current_date) reduces the scan to 180M rows. Additionally, the self-join in CTE 4 can be replaced with a LAG() window function, eliminating a full table scan." Kent rewrites the query, preserving the exact same output columns and business logic. You run it: 3.2 seconds. Same numbers, verified row-for-row. You did not need to understand a single thing about query plans.
Context
Three months ago, Sales asked for a "quick" segment analysis. You spent 4 hours on it, shared the results in Slack, and moved on. Today Sales asks for the same analysis with "slightly different filters." You cannot find the original query. It is buried in a Slack thread from October. You rebuild it from scratch, spending another 3 hours. This happens at least twice a month.
Every analysis you run through Kent is automatically saved in the project workspace with full context: the original request, the query, the results summary, and the conversation. Three months later, Sales asks again. You ask Kent: "What was the segment analysis I did for Sales in October?" Kent finds it instantly: "On Oct 14, you ran a segment analysis breaking customers into 4 tiers by LTV and usage frequency, using the query in workspace sales-requests. The original filters were: plan_type IN (pro, enterprise), signup_date > 2024-01-01." You modify two filters, rerun in 5 minutes. Say "update the segment analysis with Q4 data and add a churn risk column" into voice and Kent transcribes the request, pulls the original query, and generates the updated version.
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