Takeout

Instant answers. No SQL required.

Takeout transforms how teams access data insights. Analysts define the data once, and AI serves it up instantly - like a takeout window for your organization's knowledge.
How it works

The Takeout Window Model

Like a restaurant with a kitchen and service window, Takeout separates data preparation from data consumption.

Kitchen (Analytics Team)

Define and certify data domains

  • Connect to your data warehouse
  • Define semantic layers and relationships
  • Document business logic and gotchas

Window (Takeout Interface)

Natural language questions

  • Ask questions in plain English
  • Get instant, accurate answers
  • See the SQL and assumptions

Service (AI Layer)

Intelligent translation

  • Convert questions to optimized SQL
  • Apply business context automatically
  • Validate and serve results

The hidden cost of 'quick questions'

Every day, data teams field dozens of variations of the same queries. What if it was Germany? What if it was under 16? Analysts become human SQL machines, writing the same queries over and over.
  • 15 minutes per query
    The average time an analyst spends on each ad-hoc request, from context switching to validation.
  • 40+ requests per week
    What a typical 5-person data team handles from executives, PMs, and stakeholders.
  • 70% repetitive
    Most requests are slight variations of previous queries with different parameters.

Promote your analysts from query writers to insight architects

Stop answering the same questions. Start building reusable knowledge domains that scale across your organization.
  • Define once, answer forever
    Create semantic data domains with business context, relationships, and gotchas baked in.
  • Your expertise, amplified
    Every query applies your knowledge automatically. No more explaining why certain joins matter.
  • See what people really need
    Analytics dashboard shows which questions are asked most and where your domains need improvement.
Data Domains
Customers
Orders
Products
Payments
Add Domain

Customer Domain

Published

Core customer data including demographics, segments, and lifetime value

Core Tables
users
Primary user table
user_segments
User segmentation data
user_attributes
Extended attributes
Key Metrics
Customer LTV
SUM(order_value) * retention_rate
Churn Rate
1 - (active_30d / total_customers)
Acquisition Cost
marketing_spend / new_customers
Average Order Value
total_revenue / order_count
Business Context
  • •UK customers have different age brackets due to GDPR requirements
  • •Always exclude test users (WHERE NOT is_test_account)
  • •Customer segments are updated weekly on Sundays
Last updated 2 hours ago by Alex Rivera

Get answers at the speed of thought

No more waiting for analysts. No more learning SQL. Just ask your question and get trusted, accurate answers instantly.
  • Ask naturally
    Type your question exactly as you'd ask a colleague. The AI understands context and intent.
  • Instant insights
    Get answers in seconds, not hours. Export to Slack, email, or spreadsheets with one click.
  • Full transparency
    See the confidence level, SQL generated, and assumptions made. No black box magic.

Takeout Chat

Ask anything about your data

Connected
What's our monthly recurring revenue for Q3 2024?
You • 2 minutes ago
$2,847,392
Your MRR for Q3 2024 (July-September) averaged $2,847,392 per month, representing a 12.3% increase from Q2.
High confidence 8,742 subscriptions analyzed
View SQL
SELECT SUM(monthly_revenue) as mrr
FROM subscription_metrics
WHERE date BETWEEN '2024-07-01' AND '2024-09-30'
AND subscription_status = 'active'
Takeout • Just now
Can you break that down by product category?
You • Just now
Analyzing revenue by product category...
Searching across revenue and product domains...
12 domains•2.1s avg

Scale your analysts beyond one-off requests

Build semantic layers once. Let AI handle the variations. Free your team to focus on strategic insights that move the business forward.