An AI-native operations layer for revenue teams.
A conversational layer across the entire GTM stack. Classifies intent in plain English, dispatches to the right specialist, pulls live context from every connected system, and learns from every correction. Replaces the operational glue work that used to eat a full day every week.
A revenue team today touches a dozen systems before lunch. CRM. Marketing automation. Revenue intelligence. CDP. Reverse ETL. A data warehouse. A spreadsheet that nobody talks about. Each tool is good at what it does. Each one assumes the human between them is going to read every signal, apply every rule, and execute every handoff in the right order.
That assumption is where revenue operations quietly burns a full day every week. Not on strategy. Not on judgment calls. On the operational glue — the classification, the context-gathering, the figuring out which rule applies to which record in which pipeline at which moment. It's work the stack should be doing for the humans, not the other way around.
Cascade sits between the human and the tools. A prompt comes in. The system classifies intent, pulls relevant context from an indexed knowledge base, picks the right specialist for the work — a skill, a sub-agent, or a live production system — and returns a result that's ready to act on.
The infrastructure that took real work is the memory. A hybrid store: TF-IDF semantic search over 550+ indexed knowledge entries for "what do I know about this," plus a native SQLite FTS5 log for "what did I actually do about it last week." Every prompt queries both. Every tool call writes back. The system builds institutional memory as a side effect of doing the work.
20+ learned attribution rules. 95% accuracy, up from 50%. That isn't a model. That's institutional memory, finally captured.
Cascade is a runtime that coordinates six loosely-coupled components. Everything below fires on every session without asking.
Cascade runs every workday. Real operational work, real corrections, real memory. The stats on the homepage aren't marketing numbers — they're the system's own runtime metrics, read out of its observations database.
The quiet win is what the work becomes. An hour and a half a day that used to go to classification, context-gathering, and the ten systems of record shows up somewhere better: strategy, higher-leverage judgment, the calls and conversations a human should still be in. The execution got automated so the work could get more strategic.