Product
Cutting New Hire Ramp Time With an AI Twin
The first three months for a new engineer on an unfamiliar codebase follow a predictable pattern. A lot of asking questions. A lot of waiting for answers. A lot of context that has to be transferred person-to-person because there's nowhere else to get it.
This is expensive in two directions: it slows the new hire down, and it pulls the senior engineers they're relying on away from their own work.
The knowledge transfer bottleneck
Senior engineers don't mind mentoring. They mind being interrupted seventeen times a day to answer the same category of question: why does this work the way it does?
The answer usually exists. It's in someone's head, or buried in a Slack thread, or embedded in a comment that doesn't have enough context. Getting to it requires either a synchronous interruption or enough time to go spelunking through history.
A Twin as an onboarding interface
Imagine a new hire who can ask your team's AI Twins: Why was the payments service extracted from the monolith? The Twin doesn't guess. It answers based on the actual reasoning of the engineers who made the decision — synthesized from the conversations, code reviews, and internal documents where that reasoning was originally expressed.
The senior engineer still exists. The mentoring relationship still matters. But the routine context questions — the ones that interrupt flow without adding value to either party — get answered without a synchronous interrupt.
What this looks like in practice
Teams in our closed beta are seeing two shifts. First, new hires reach independence faster — not because they're learning faster, but because the information they need is actually accessible. Second, senior engineers report fewer context-switch interruptions during deep work.
The knowledge was always there. It just needed a better interface.