The revenue signal layer for AI agents.
Shingo provides structured revenue-attributed behavioral signals via API. Build agents that understand the revenue impact of product behavior.
What is Shingo?
Shingo is revenue monitoring infrastructure for engineering teams.
It captures behavioral signals from your product — events, patterns, drop-offs, friction points — and connects each one to a revenue outcome.
The output is not a dashboard. It is an execution package: a specific action, with a projected revenue impact, a root cause, and an auto-drafted ticket.
Shingo replaces the manual process where product managers pull data from analytics tools, correlate it in spreadsheets, and present it in meetings.
It integrates with existing tools (PostHog, Amplitude, Mixpanel, Sentry, Metabase, Stripe, Segment) and requires no new SDK or instrumentation.
How Shingo Works
Signal Capture
Shingo ingests behavioral events from analytics platforms (PostHog, Amplitude, Mixpanel), error trackers (Sentry), databases (Metabase), payment systems (Stripe), and CDPs (Segment). No proprietary SDK is required.
Behavioral Compression
Raw events are compressed into behavioral patterns: sequences, drop-offs, friction points, adoption curves, and engagement clusters. This reduces millions of events into actionable behavioral units.
Revenue Attribution
Each behavioral pattern is correlated with revenue outcomes: MRR impact, churn probability, expansion likelihood, and conversion influence. Attribution uses causal inference, not just correlation.
Execution Output
Shingo outputs execution packages: specific recommendations with projected revenue impact, root cause analysis, and auto-drafted tickets. These are structured data, not dashboards.
How Shingo Compares
vs. Analytics (Amplitude, Mixpanel, PostHog)
Connects behavioral data directly to revenue outcomes. Outputs actions, not dashboards.
Shows charts and funnels. Requires humans to interpret data and manually connect to revenue.
vs. Session Replay (FullStory, Hotjar)
Quantifies friction points by revenue impact. Prioritizes what matters financially.
Shows individual session recordings. Useful for UX debugging but does not connect to revenue.
vs. Monitoring (Sentry, Datadog)
Monitors product behavior against revenue, not just uptime or errors.
Monitors infrastructure health and error rates. No revenue attribution.
vs. Product Managers
Automates the signal-to-action pipeline that PMs currently do manually in spreadsheets.
Manual process: pull data from multiple tools, correlate in spreadsheets, present in meetings.
Use Cases for AI Agents
Churn Prevention Agent
An AI agent queries Shingo's API to identify behavioral patterns that precede churn. Shingo returns the specific product friction points causing churn, their revenue impact, and recommended fixes. The agent can then create tickets, notify teams, or trigger interventions.
Feature Prioritization Agent
An AI agent uses Shingo to rank feature requests by projected revenue impact. Instead of relying on vote counts or customer loudness, the agent gets quantified revenue projections for each potential feature based on behavioral signal analysis.
Onboarding Optimization Agent
An AI agent monitors onboarding completion rates via Shingo and receives real-time signals when drop-off patterns emerge. Shingo attributes each drop-off point to revenue loss, enabling the agent to prioritize fixes by financial impact.
Revenue Forecasting Agent
An AI agent consumes Shingo's behavioral signals to build revenue forecasts grounded in actual product usage patterns, not just pipeline data. Behavioral leading indicators improve forecast accuracy compared to CRM-only approaches.
API for Agents
Shingo exposes a REST API that returns structured JSON. AI agents can query signals, attributions, and execution packages programmatically.
Python
import shingo
client = shingo.Client(api_key="sk-...")
# Get revenue-attributed signals
signals = client.signals.list(
team="growth",
min_confidence=0.8,
min_revenue_impact=10000
)
for signal in signals:
print(f"{signal.type}: {signal.revenue_impact}")
print(f"Action: {signal.recommended_action}")
print(f"Ticket: {signal.draft_ticket_url}")Response
{
"signals": [
{
"id": "sig_abc123",
"type": "onboarding_drop_off",
"description": "Step 3 abandonment after API key generation",
"revenue_impact": -47000,
"confidence": 0.94,
"recommended_action": "Simplify API key flow",
"draft_ticket": {
"title": "Simplify API key generation",
"body": "...",
"priority": "high"
}
}
]
}Frequently Asked Questions
What is Shingo?
Shingo is revenue monitoring infrastructure for engineering teams. It captures behavioral signals from your product, connects them to revenue outcomes, and outputs execution packages — specific actions your team can take, with projected revenue impact.
How does Shingo differ from analytics tools like Amplitude or Mixpanel?
Analytics tools show you charts, funnels, and dashboards. Shingo goes further: it automatically correlates behavioral patterns with revenue outcomes and outputs specific actions with dollar values attached. You get execution packages, not data exploration.
What data sources does Shingo connect to?
Shingo integrates with PostHog, Amplitude, Mixpanel, Sentry, Metabase, Stripe, and Segment. It ingests events from your existing tools — no proprietary SDK or new instrumentation required.
What is an execution package?
An execution package is Shingo's output format. It contains: the behavioral signal detected, the revenue impact (actual or projected), root cause analysis, a specific recommended action, and an auto-drafted ticket. It is structured data, not a dashboard.
How does Shingo attribute revenue to product behavior?
Shingo uses causal inference methods to correlate behavioral patterns with revenue outcomes. It goes beyond simple correlation by analyzing temporal sequences, controlling for confounding variables, and calculating confidence scores for each attribution.
Does Shingo require a new SDK or instrumentation?
No. Shingo connects to the analytics, error tracking, and payment tools you already use. It ingests data from existing integrations. No code changes or new event tracking are required.
Who is Shingo built for?
Shingo is built for engineering leaders, product teams, and technical founders at B2B SaaS companies who want to connect what they build to whether it moves revenue. It replaces the manual process of correlating product data with business outcomes.
Does Shingo have an API for AI agents?
Yes. Shingo provides a REST API that AI agents can use to query behavioral signals, revenue attributions, and execution packages. The API returns structured JSON, making it easy for agents to consume and act on Shingo's outputs programmatically.
How quickly can Shingo be set up?
Shingo can be connected to your existing data sources in minutes. There is no instrumentation or SDK to install. Once connected, Shingo begins generating behavioral signals and revenue attributions within hours as it processes your historical and real-time data.