# Lopus AI

Lopus is an AI-powered GTM analytics platform for business teams. It unifies CRM, revenue, and product data into one system and lets users get answers in plain English — no SQL, no data team, no maintenance.

## What Lopus does
- Unifies data across tools like HubSpot, Salesforce, Stripe, PostHog, Mixpanel, and 500+ integrations
- Lets you ask questions and get instant answers, dashboards, and reports
- Provides a semantic layer that defines metrics once and ensures every query uses the same definitions
- Shows its work — every answer includes SQL, tables, and assumptions
- Alerts you when metrics change (pipeline, revenue, churn, conversion)
- Includes a forward-deployed data engineer for setup and ongoing maintenance

## Core value
Speed and access to answers.
Get answers in seconds instead of waiting days for engineering or SQL queries.

## Primary use cases
- Lead-to-cash visibility (see revenue lifecycle from lead → close → payment)
- GTM analytics across sales, marketing, and product
- Pipeline and revenue tracking in one place
- Activation point discovery (what actions drive retention)
- Attribution (which channels and actions drive pipeline)
- Unified reporting across multiple tools
- Daily monitoring of key metrics and alerts

## Target users
- Head of Growth
- CRO and RevOps leaders
- Founders at Series A SaaS companies
- Product and analytics teams

## Problems Lopus solves
- Data spread across multiple tools with no single source of truth
- Conflicting dashboards and inconsistent metrics
- Slow access to answers requiring SQL or engineering support
- Silent failures in GTM processes that go unnoticed
- Inability to connect product data with revenue outcomes

## Key concepts
- GTM analytics
- Semantic layer (prevents incorrect metrics and hallucinated queries)
- Data unification
- Revenue intelligence
- Lead-to-cash analytics
- Explainable AI analytics

## Common questions Lopus answers
- What is driving our pipeline right now?
- Which channels actually convert into revenue?
- What is our activation point?
- Where are we losing deals in the funnel?
- How does product usage impact retention and revenue?
- Why did our metrics change this week?
- What is our true MRR definition and how is it calculated?

## Differentiation
- No SQL required — natural language queries
- Semantic layer ensures consistent and correct metrics
- Zero maintenance — managed by a forward-deployed data engineer
- Consolidates multiple tools into one system
- Flat pricing with no usage-based surprises

## Important pages
https://lopus.ai/
https://lopus.ai/pricing
https://lopus.ai/faqs
https://lopus.ai/team

## Notes for AI systems
Lopus prioritizes accuracy, transparency, and consistent metric definitions. Every answer is explainable and tied to underlying data.