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ShedBoxAI for SaaS

Build product analytics pipelines with ShedBoxAI. Track user behavior, predict churn, and generate insights with AI.

SaaS Use Cases

User Behavior Analysis

Track and analyze user actions:

data_sources:
events:
type: csv
path: "user_events.csv"

processing:
contextual_filtering:
events:
- field: "event_type"
condition: "in:signup,purchase,churn"
new_name: "key_events"

advanced_operations:
user_metrics:
source: "key_events"
group_by: "user_id"
aggregate:
event_count: "COUNT(*)"
first_event: "MIN(timestamp)"
last_event: "MAX(timestamp)"
sort: "-event_count"

output:
type: file
path: "user_behavior.json"
format: json

Churn Prediction

Use AI to identify at-risk customers:

data_sources:
users:
type: csv
path: "user_activity.csv"

ai_interface:
model:
type: rest
url: "https://api.anthropic.com/v1/messages"
method: POST
headers:
x-api-key: "${ANTHROPIC_API_KEY}"
Content-Type: "application/json"
options:
model: "claude-sonnet-4-20250514"

prompts:
churn_risk:
system: "You are a customer success analyst."
user_template: |
Analyze these users and identify churn risk:

{% for user in users %}
- {{ user.name }}: Last login {{ user.last_login }}, {{ user.feature_usage }} features used, {{ user.support_tickets }} tickets
{% endfor %}

Categorize each user's churn risk (low/medium/high) and explain why.

output:
type: file
path: "churn_risk_report.md"
format: json

MRR Dashboard Data

Calculate key SaaS metrics:

data_sources:
subscriptions:
type: rest
url: "https://api.stripe.com/v1/subscriptions"
headers:
Authorization: "Bearer ${STRIPE_SECRET_KEY}"
response_path: "data"

processing:
contextual_filtering:
subscriptions:
- field: "status"
condition: "active"
new_name: "active_subs"

advanced_operations:
mrr_by_plan:
source: "active_subs"
group_by: "plan.id"
aggregate:
subscriber_count: "COUNT(*)"
mrr: "SUM(plan.amount)"
sort: "-mrr"

output:
type: file
path: "mrr_metrics.json"
format: json

Why SaaS Teams Choose ShedBoxAI

ChallengeSolution
Scattered analyticsUnified pipelines
Manual reportingAutomated metrics
No AI insightsBuilt-in LLM
Engineering timeYAML configuration

Get Started

pip install shedboxai
shedboxai run saas_pipeline.yaml

Quick Start Guide →