The problem
Make enterprise analytics useful in the flow of work: help people move from a business question to an answer they can act on.
Level AI · Agentic analytics
From a business question to a useful answer.
An enterprise agentic analytics product, taken from pre-launch strategy through launch, iteration, and adoption.
The short version
Complexity,
made useful.
What I led
I led customer discovery, requirements, experience design, roadmap, pricing, go-to-market, launch, and iteration. My scope connected what the product could do with how customers would adopt it.
What changed
The platform reached 70+ active enterprise tenants in a 30-day window and 25K+ cumulative platform runs. Adoption became an ongoing product discipline, supported by cohort and account-level telemetry.
Inside the experience
A question is just the beginning.
Explore an illustrated product story.
The examples use sample data.
AI WORKERSPRODUCT EXPLORATION
What is driving repeat contact?
From question to contextIntelligence, working together.
Find evidenceConnect signalsSynthesize
THE ANSWER, WITH CONTEXT
Refund follow-ups need a clearer next step.
Customers are returning after an expected refund date passes. The sample conversations point to unclear timelines and missing confirmation.
Connected to supporting evidence
ApplicationsIntelligenceEvidence
The product work
Where I
focused.
Strategy, decisions, and the details
behind the experience.
Own the path from discovery to adoption.
Connect customer needs, product experience, pricing, and launch in one product strategy.
Inside the product scope
My remit covered customer discovery, requirements, experience design, roadmap, pricing, GTM, launch, and iteration. This brought product and commercial decisions into the same scope, rather than ending ownership at delivery.
Measure what happens after the first try.
Use breadth, depth, retention, and account concentration to understand adoption.
How I approached adoption
I built success metrics across those four dimensions and used cohort and account-level telemetry to prioritize interventions. Weekly active users grew 3.5x over 10 months, with 75% first-month retention among users who tried the product.
Bring the experience together.
Consolidate independent workers through a shared orchestrator and an agent harness.
How the experience evolved
The shipped evolution brought skills and rules into an agent harness, personalization using memory across users and teams, and third-party context through MCP plugins.
The outcomes
70+
active enterprise tenants in a 30-day window
25K+
cumulative platform runs
75%
first-month retention among users who tried the product