platoseed
Growth Operations Data Platform
Pointing an AI at all your data is a recipe for disaster - it will confidently lie, hallucinate, and drive your business into the ground. What if you could trust your AI the way you trust a calculator? That's Lopus. We build a self-healing semantic layer that captures your business logic. Including every metric, every definition, every edge case. Defined once, and trusted everywhere. No SQL. No dashboards. No waiting on the data team.
Lopus is an Operations Data Platform that unifies CRM, revenue, and customer data to enable rapid ad-hoc analytics without requiring data cleaning. It emphasizes a single source of truth for business context and connects multiple tools and data warehouses to deliver insights quickly.
Lopus connects CRM, billing, product, support, and warehouse tools in one place, provisioning a separate instance and warehouse as needed. It provides a semantic layer to map messy data, enables automated alerting with root-cause investigations when metrics move, and offers a governance framework with permissioned access. The platform emphasizes no engineering tickets to connect tools, rapid setup (hours), and an emphasis on data accuracy through clarifying questions, defined semantics, and confidence scoring with auditable query work.
Who itβs for: RevOps/BizOps teams, growth teams, and analytics-focused organizations seeking a unified data source for sales, marketing, and product analytics.
Backed by Y Combinator
Co-founder and CEO of Lopus AI. CS @ Dartmouth. Full-stack/infra/ML dev building in public. Extensive experience with LLM-centered ML research. Previously worked on the DIGIHEALS project at DARPA.
Building Lopus - Analytics for the rest of us. Ex. 2+ years of award-winning R&D for startups @ DALI Lab, Dartmouth, deep experience in AR/VR development, product & system design. Led multiple ivy league technical teams at research labs, tech consulting groups, & quant teams.
Formerly βLevo UIβ Β· why startups rename β

Navigate any set of data - no matter how large - in minutes.

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