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ShedBox Agent for Data Analysts

Accelerate your analysis workflow with natural language data operations.

The Data Analyst Challenge​

Data analysts spend too much time on:

  • Writing repetitive SQL queries
  • Connecting to multiple data sources
  • Building reports from scratch
  • Manual data cleaning and prep

ShedBox Agent automates the tedious work so you can focus on insights.

How Data Analysts Use ShedBox Agent​

Instant Multi-Source Analysis​

"Join our PostgreSQL customers table with Stripe payments
and segment by lifetime value"

✓ Connects to both sources
✓ Performs the join
✓ Calculates LTV
✓ Creates segments
✓ Generates visualization

Natural Language Queries​

Skip the SQL for common analyses:

"Show me week-over-week growth in signups"
"Find customers who purchased in Q1 but not Q2"
"What's the correlation between support tickets and churn?"

Automated Reporting​

Turn any analysis into a scheduled report:

# Weekly metrics report
schedule:
  cron: "0 8 * * 1"

data_sources:
  metrics:
    type: postgresql
    connection_env: DATABASE_URL
    query: |
      SELECT
        DATE_TRUNC('week', created_at) as week,
        COUNT(*) as signups,
        SUM(revenue) as revenue
      FROM users
      WHERE created_at > NOW() - INTERVAL '4 weeks'
      GROUP BY 1

output:
  type: email
  to: stakeholders@company.com
  format: markdown

Key Benefits for Analysts​

1. Faster Time to Insight​

  • Natural language instead of SQL for simple queries
  • Instant visualization suggestions
  • Pre-built aggregation patterns

2. Multi-Source Power​

Connect and join across:

  • Data warehouses (PostgreSQL, MySQL)
  • SaaS APIs (Salesforce, HubSpot, Stripe)
  • Files (CSV, JSON, Excel)
  • REST APIs (any authenticated endpoint)

3. Reproducible Analysis​

Every conversation produces a YAML pipeline:

data_sources:
  sales:
    type: postgresql
    query: "SELECT * FROM orders WHERE created_at > '2024-01-01'"

processing:
  aggregate:
    group_by: region
    metrics:
      - revenue: sum(amount)
      - orders: count
      - aov: avg(amount)

output:
  type: file
  path: regional_sales.csv

4. Self-Service for Stakeholders​

Create pipelines that stakeholders can run themselves:

# Stakeholder runs your pre-built analysis
shedboxai run monthly_metrics.yaml

Common Analyst Workflows​

Cohort Analysis​

"Create a cohort analysis showing retention by signup month"

Funnel Analysis​

"Show me conversion rates through the signup funnel,
broken down by traffic source"

A/B Test Results​

"Calculate statistical significance for experiment XYZ
using our events data"

Executive Dashboards​

"Create a weekly executive summary with key metrics
from our data warehouse"

Integration with Your Stack​

ShedBox Agent fits into your existing workflow:

  • Version control: YAML configs work with Git
  • Scheduling: Built-in cron or integrate with existing schedulers
  • Output: Export to your preferred format or destination

Get Started​

Start analyzing data faster with natural language.

Try ShedBox Agent →