The Data Scientist Problem: Every Team Needs One, Nobody Can Hire Enough

We Built Something That Doesn't Exist: Every Employee Gets Their Own Data Scientist

We gave every employee at your company their own personal data scientist.

Not a dashboard. Not a BI tool. Not a chatbot that answers questions about data.

An actual analyst that writes Python code, builds custom reports, and runs them automatically on whatever schedule you want.

In plain English. Through conversation. No code required.

Nobody else has this.

Here's How It Actually Works

You: "Show me customers who spent over $10K last quarter but haven't purchased anything in the last 60 days"

Your Analyst Cerebral: "Building that report now..."

[30 seconds later]

Cerebral: "Found 47 customers. Here's the breakdown by account manager, sorted by total lifetime value. Want me to adjust anything?"

You: "Perfect, but only show accounts where we haven't had email contact in the last two weeks"

Cerebral: "Updated. Now showing 23 accounts with no recent email engagement."

You: "This is exactly what I need. Send this to me every Monday morning at 8 AM."

Cerebral: "Done. You'll get this report every Monday. I'll email you if the list changes significantly week-to-week."

But here's what makes this different from every other automated report:

The Cerebral doesn't just send you data. It sends you analysis.

Every Monday, you get:

The Report: 23 at-risk accounts

The Commentary:

• "Up from 18 last week (+5 accounts)"
• "3 accounts moved from medium-risk to high-risk: Acme Corp (no email response in 21 days), Global Industries (usage dropped 45%), TechStart Inc (support tickets increased 3x)"
• "Good news: 2 accounts from last week's list made purchases and are no longer at-risk"
• "Recommendation: Prioritize Acme Corp - they're your largest account on this list ($340K LTV) and haven't responded to outreach in 3 weeks"

Just like a Wall Street analyst writes commentary on earnings reports.

Not just the numbers. The context. What changed. What matters. What to do about it.

And you can dial this up or down:

• Minimal: Just the data
• Standard: Key changes highlighted
• Detailed: Full analyst commentary with recommendations

That just happened in under 2 minutes.

No SQL. No Python. No ticket to the data team. No three-week wait.

Just you and your embedded analyst, building exactly what you need.

What Your Analyst Cerebral Actually Does

Behind that conversation, here's what happened:

1. Cerebral wrote a Python script to query your data
2. Ran it in a sandboxed environment with test data to make sure it works
3. Showed you the results
4. You iterated - "actually, change this"
5. Cerebral adjusted the code and ran it again
6. When you approved - saved the script
7. Now runs automatically every Monday on real data
8. Emails you the report

You never saw the code. You just described what you wanted and refined the output.

The Cerebral handled everything else.

This Isn't a BI Tool

BI tools show you pre-built dashboards.

Analyst Cerebrals build custom reports on demand.

Tableau/Power BI:

• Data team builds dashboards
• You view what they thought you'd need
• New question? New ticket.

Analyst Cerebral:

• You ask your question
• Cerebral builds the analysis
• Iterate until it's perfect
• Runs automatically forever
• Provides analyst commentary on every report

It's the difference between a spreadsheet and a Wall Street analyst.

BI tools give you numbers in a dashboard.

Analyst Cerebrals give you:

• The data
• What changed since last time
• Why it matters
• What to pay attention to
• What action to take

The Commentary Layer Nobody Else Has

Every automated report you've ever gotten just dumps data.

"Here are your numbers. Good luck figuring out what changed."

Analyst Cerebrals write commentary. Just like human analysts.

Standard Report (what everyone else does):

Q4 Customer Churn Report
- Total churned: 47 customers
- Churn rate: 8.2%
- Revenue impact: $340K


Analyst Cerebral Report (what we do):

Q4 Customer Churn Report

Total churned: 47 customers (up from 32 in Q3, +47% increase)
Churn rate: 8.2% (above our 6.5% target)
Revenue impact: $340K

KEY CHANGES:
- SMB segment churn accelerated: 15.3% vs 9.1% last quarter
- Enterprise churn stable at 3.2%
- Geographic shift: West Coast churn up 65%, all other regions flat

PATTERNS IDENTIFIED:
- 73% of churned customers had support tickets unresolved >14 days
- Price-sensitive segment (annual contracts <$10K) showing elevated churn
- Customers who didn't adopt Feature X in first 60 days: 4x higher churn rate

RECOMMENDATIONS:
1. Priority: Address West Coast support ticket backlog (23 open tickets >14 days)
2. Consider pricing adjustment for <$10K annual segment
3. Implement Feature X onboarding campaign for new customers

NEXT REPORT: January 15
ALERT THRESHOLD: Will notify immediately if churn rate exceeds 9%


That's not a report. That's an analyst.

And you can configure how much commentary you want:

Minimal Mode: Just the numbers, no commentary

Standard Mode: Key changes highlighted, basic patterns noted

Detailed Mode: Full analyst write-up with context, patterns, and recommendations

Executive Mode: Bottom-line summary first, details available on click

Most executives want Executive Mode. Most analysts want Detailed Mode.

Same report. Different presentation. You choose.

What This Unlocks Across Your Organization

Sales Teams

Sales Rep: "Show me enterprise accounts that haven't ordered in 90 days, have declined 30%+ in order frequency, and still have open support tickets"

Cerebral: [Builds report] "Found 12 accounts matching that criteria. Want this weekly?"

Rep: "Yes, and flag any new accounts that match this pattern immediately"

Now that sales rep gets every Monday:

The Report: 12 at-risk enterprise accounts

The Commentary:

• "New this week: DataCorp added to list (went 91 days without order, previously ordered every 45 days)"
• "Escalating: MegaSoft support tickets up 400% while order frequency down 60%"
• "Positive movement: TechGlobal placed order Friday, removed from list"
• "Priority action: Contact DataCorp today - historically your #3 account by revenue"

Not just a list. An analysis of what changed and what to do about it.

Finance Teams

Controller: "Flag any invoices where we gave more than 15% discount to a non-VIP customer on orders under $50K"

Cerebral: [Builds report] "Showing 8 invoices from last month. This looks like potential discount policy violations."

Controller: "Exactly. Send me this daily and alert me if any single invoice is over $25K"

Now finance gets every day:

The Report: Invoice discount violations

The Commentary:

• "Higher than usual: 8 violations today vs 5/day average"
• "Large exception: Invoice #45782 - 18% discount on $42K order (non-VIP customer)"
• "Pattern detected: Sales Rep J.Smith responsible for 3 of today's 8 violations"
• "Month-to-date: $340K in total discounts given, trending 15% above last month"

Proactive compliance monitoring with context.

Operations Teams

Operations Manager: "Which vendors had more than 2 late deliveries this month, with average delay over 3 days, on orders over $10K"

Cerebral: [Builds report] "Found 5 vendors. Here's their performance data."

Manager: "Perfect. Send me this monthly and include their on-time percentage for context"

Now operations has:

• Vendor performance monitoring
• Automated monthly reports
• Context for supplier negotiations

No manual tracking. Built once, runs forever.

Customer Success Teams

CS Manager: "Show me customers who decreased usage by 30%+ this month, haven't contacted support in 60 days, and are up for renewal in the next 90 days"

Cerebral: [Builds report] "14 at-risk customers. Want me to break this down by CSM assigned?"

Manager: "Yes, and send each CSM their list every Friday"

Now customer success has:

• Proactive churn prevention
• Personalized alerts per CSM
• Weekly cadence automatically

Every team. Every role. Custom analysis on demand.

The Iteration Loop Changes Everything

Most "AI analytics" tools fail because they give you one shot:

You: "Show me high-risk customers"

AI: [Shows generic report]

You: "That's not quite what I meant..."

AI: "Try rephrasing your question"

You give up.

With Analyst Cerebrals, you iterate:

You: "Show me high-risk customers"

Cerebral: "I'm defining high-risk as: decreased usage + late payments. Is that your criteria?"

You: "Close, but also include support ticket volume and product adoption metrics"

Cerebral: "Updated. Now tracking usage, payments, support tickets, and feature adoption. Here's the report."

You: "Better, but only flag them if 2 or more of these indicators are present"

Cerebral: "Done. 34 customers meet that threshold. Want me to show the breakdown of which indicators each customer triggered?"

You: "Perfect. Send this to me weekly and alert me immediately if any customer triggers 3+ indicators"

You shaped the report through conversation until it perfectly matched your mental model.

Then it runs forever.

The Air-Gapped Safety

"You're letting AI write code that runs on my data?"

No. We're letting AI write Python on data you piped into our data lakehouse.

Your production systems are never touched.

Here's the actual architecture:

Phase 1: You Tell Cerebral What Data You Want

You don't even need to know how to pipe data.

You: "I want to analyze customer orders from Shopify"

Analyst Cerebral: "Got it. I'll set up a read-only pipe from your Shopify orders. This will sync nightly. Should I include order items, customer info, and shipping details?"

You: "Yes, and also pull in support tickets from Zendesk"

Cerebral: "Done. Created two pipes: Shopify orders (nightly sync) and Zendesk tickets (hourly sync). Both are read-only - I can't write back to your systems."

The Cerebral writes the pipe code for you.

But here's the critical safety:

• Read-only access - Cerebral can pull data, never write back
• Governance-enforced - Write verbs to production systems are blocked
• You approve the pipe - Nothing syncs until you say yes

Phase 2: Sandbox Testing

• Cerebral writes Python script
• Runs in isolated sandbox environment
• Uses test/sample data first
• Shows you the results

Phase 3: Iterative Refinement

• You review the output
• Request changes
• Cerebral adjusts and reruns
• Repeat until perfect

Phase 4: Your Approval

• You explicitly approve the script
• Only then does it run on the piped data in the lakehouse

Phase 5: Scheduled Production

• Runs on your schedule
• Against data in the Cerebral lakehouse (not your production systems)
• You can pause/modify anytime
• Full audit trail of what ran when

The AI writes:

1. The code to pipe data in (read-only)
2. The code to analyze that data
3. The code to generate reports with commentary

You just describe what you want in plain English.

Your production systems are never accessible to the AI for writes. Ever.

Governance blocks it at the architecture level. The Cerebral literally doesn't have write verbs available to production systems.

Why Nobody Else Has Built This

Because it requires:

1. Safe code execution environment -Can't let arbitrary Python run on production databases

2. Conversational refinement -Most AI tools are one-shot, not iterative

3. Data workspace per employee -Isolated environments, not shared dashboards

4. Scheduling infrastructure -Reports need to run automatically

5. Business context understanding -AI needs to know your schema and business rules

Most companies build either:

• BI dashboards (static)
• AI chatbots (can't write code)
• Analytics platforms (require SQL skills)

Nobody built conversational analysts that write code iteratively and run it automatically.

Until now.

What This Actually Means

Every employee becomes a power user.

Sales rep who can't code → Builds custom pipeline analysis

Finance analyst who knows Excel → Builds automated compliance audits

Operations manager with no SQL → Builds vendor performance tracking

Customer success lead → Builds churn prediction monitoring

The bottleneck isn't data anymore. It's asking the right questions.

And now every employee can ask their questions and get answers immediately.

Not in three weeks.

Not through the data team.

In three minutes. Through conversation. With their own embedded analyst.

The Shift This Creates

Before Analyst Cerebrals:

• Data teams are bottlenecked
• Employees wait weeks for reports
• Dashboards go stale
• Questions go unasked because the friction is too high

After Analyst Cerebrals:

• Every employee has analytical capabilities
• Custom reports built in minutes
• Insights run automatically
• Decision velocity increases dramatically

This isn't incremental improvement.

This is a fundamental shift in who can access and analyze data.

From "people who can write SQL" to "anyone who can ask a question."

The Bottom Line

We didn't build better BI dashboards.

We didn't build a fancier analytics platform.

We built personal data scientists for every employee.

Conversational. Iterative. Automated.

Ask a question in plain English.

Get a custom report in minutes.

Iterate until it's perfect.

Approve once.

Runs forever.

No other platform does this.

Not Tableau. Not Power BI. Not Looker. Not any AI analytics tool.

This is new.

And it changes what's possible when every employee—regardless of technical skill—can analyze data and build custom reports through conversation.

That's what Cerebral does.

Every employee gets their own data scientist.

Not someday. Not with training. Not if they learn Python.

Today. Through conversation. With zero code.

Nobody else has this.

We do.

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