Tycoon solutionAI Data Analyst + AI Data Engineer connect to your database, analytics tools, and business systems. You ask questions in plain English ('show me MRR by plan tier for the last 6 months, broken out by new vs expansion'), and the AI writes and runs the SQL, builds the visualization, and explains what the data means. Weekly business reviews get automated: KPI dashboards update themselves, anomaly detection flags what changed, and the narrative summary writes itself. The data team you couldn't afford now costs $300-500/month instead of $120K/year.
How it runs
- Connect data sources
AI Data Engineer connects to your database (Postgres, MySQL, BigQuery, Snowflake), analytics tools (PostHog, Mixpanel, Amplitude), billing (Stripe), marketing (GA4, Meta Ads), and any CSV/spreadsheet data you upload. Sets up read-only access so the AI can query but never write. Schema is documented automatically.
- Ask questions in plain English
You ask AI Data Analyst: 'What's our month-over-month revenue growth rate for the last 12 months, and is it accelerating or decelerating?' AI writes the SQL, queries the database, and returns: a chart with the growth rate trendline, a 3-sentence analysis ('Growth accelerated from 8% to 14% MoM between January and June, driven primarily by enterprise plan upgrades'), and the raw data table if you want it. The entire flow — question to answer — takes 30-90 seconds.
- Automated weekly business review
Every Monday morning, AI Data Analyst generates the weekly business review: KPI dashboard (revenue, active users, churn, CAC, LTV, NPS), week-over-week deltas with flags for significant changes, anomaly detection ('signups spiked 40% on Wednesday — traced to a Hacker News mention'), and a narrative summary in plain English. Replaces the 4 hours of manual data pulling and spreadsheet work that founders do every Monday.
- Deep-dive investigations
When a KPI moves, AI Data Analyst runs root cause analysis automatically. 'Churn rate doubled last week — investigating.' It segments churned users by plan, tenure, feature usage, support tickets, and acquisition channel. Surfaces the finding: 'The churn spike is concentrated in users who signed up via a specific Meta ad campaign and never used Feature X. The ad may be attracting the wrong audience.' Not just what happened — why it happened.
- Dashboard maintenance
AI Data Analyst maintains living dashboards for the metrics that matter to your business. When you add a new product feature, it adds the relevant tracking. When you pivot pricing, it updates the revenue model. Dashboards stay current because the AI updates them — not because someone remembers to update them during quarterly planning.
- Investor and board reporting
AI Data Analyst generates the metrics section of your board deck: the KPIs that matter, the trendlines, the narrative. Every number sources from the live database, not a manually-updated spreadsheet. For fundraise diligence: cohort retention tables, unit economics, growth accounting — all pulled on demand from production data, not reconstructed from memory.
Who runs it
- hire/ai-data-analyst
- hire/ai-data-engineer
- hire/ai-cfo
What you get
- Business questions answered in 30-90 seconds instead of 4 hours of SQL
- Weekly business review auto-generated Monday morning — replaces manual data pulling
- KPI anomalies detected and diagnosed with root cause analysis within 24 hours
- Dashboards stay current because the AI maintains them, not because someone remembers
- Board and investor metrics pulled from live data — no spreadsheet reconciliation
- Data analyst cost drops from $120K/year to $300-500/month