Global marketing network
AI-assisted user onboarding
An onboarding assistant that drafts profile content from reputable sources, grounded in what the user actually provided, inside a strict latency budget.
- 2×
- partner profile completion rate
- Model benchmarking, Evaluation sets, Latency budgeting, Human-in-the-loop
- stack
Problem
- A global marketing network needed to lift the completion rate of partner profiles during onboarding. Incomplete or low-quality profiles reduce the chance of a partner finding a match on the platform, which directly affects platform revenue.
- Every suggestion had to be grounded in what the partner had actually provided, because partners lose trust quickly at that stage of the relationship.
- End-to-end latency had to stay low enough that partners did not abandon onboarding while waiting on the assistant.
Approach
- Led design and development of the AI system end to end through a highly iterative process, with significant input into the onboarding UI itself.
- The assistant proposes draft profile content from reputable sources, which the partner can accept, edit, or reject inline.
- Benchmarked models and prompting strategies against evaluation sets covering grounding, suggestion quality, and latency under realistic onboarding scenarios.
- Selected the configuration that met the reliability bar inside the latency budget, delivered against a deadline tied to a platform-wide onboarding revamp.
Outcome
- Partner profile completion rate roughly doubled after launch.
- Achieved with no measurable increase in partner drop-off during onboarding.
Have a system that needs to work in production?
Tell us what you are building, or what has stopped working. Thirty minutes, no pitch deck.