iMerit

What Was Built

Ground Control was the enterprise-grade AI data-operations platform I created as product owner at iMerit. It gave customers one place to see throughput, quality, anomalies, workforce risk, and customer-specific governance across distributed annotation programs.

The platform unified fragmented annotation data across 6,000+ annotators, 20+ labeling tools, and 5 time zones so internal teams and enterprise customers could understand what was happening before delivery, quality, or compliance broke down.

I also helped take Ground Control directly to market with enterprise customers. Alongside iMerit's go-to-market and delivery teams, I translated complex operating needs into configurable workflows, metrics, governance controls, and customer-ready proof that teams could trust, adopt, and scale.

Ground Control supported customer and internal operating programs across autonomous systems, industrial AI, inspection, mapping, and delivery operations. Each program had different tools, metrics, and review paths; the product work was turning that variation into reliable operating visibility without forcing every customer into the same workflow.

Amazon
Netflix
John Deere / Blue River
Athena
UV Eye
Nuro
Bosch
Internal finance and delivery operations

What the Product Did

Turned fragmented labeling activity into shared operating dashboards for delivery, quality, and customer teams.
Standardized metrics across projects, tools, customers, and time zones.
Captured edge cases and anomalies from browser-based labeling environments.
Supported role-based access, data lifecycle management, audit trails, encryption, and custom customer configurations.
Gave enterprise customers evidence that data work was moving, exceptions were being handled, and risk was visible.

My Role

I was the product owner and Senior Product Manager for Ground Control. I owned product vision, roadmap, stakeholder discovery, feature requirements, prioritization, direct customer conversations, and alignment across engineering, program management, operations, compliance, go-to-market, and customer-facing teams.

The work was not just dashboard shipping. It required turning messy customer asks into reusable platform primitives: shared definitions, escalation workflows, anomaly lifecycle management, role-based access, and customer-ready proof.

Ground Control 2024 roadmap organized into Connect, Comply, Govern, and Extend phases — The 2024 roadmap translated a messy analytics platform into concrete product tracks: integrations, compliance, governance, documentation, and categorization.

Product Scope

The hardest product problem was not a single feature. It was turning many one-off operational requests into an enterprise platform that could flex by customer without becoming impossible to maintain.

That meant writing requirements for configurable workflows, clarifying ownership across technical and delivery teams, defining success metrics, and separating what should be custom from what needed to become reusable product infrastructure.

Enterprise-grade data governance matrix for standard, advanced, and custom governance levels — Governance became a product surface: access, audit trails, encryption, lifecycle controls, custom integrations, and customer-specific configurations.

Edge Case System

One of the clearest examples was the Edge Case Module. The product needed to help teams capture a screenshot or event from a labeling workflow, route it into the right review path, preserve metadata, and make the resolved case usable for training and delivery improvement.

Ground Control screenshot-governance workflow for allowlisted and blacklisted customers across Custom Configuration, AWS, and the Edge Case Module — The Edge Case Module turned browser activity into structured evidence that could be reviewed, resolved, or discarded based on customer configuration.

Ground Control Edge Case Module workflow from labeling tool through expert review, client feedback, resolution, and training — The workflow connected labelers, subject matter experts, clients, resolution steps, and training loops. The platform tracked 4,500+ edge cases captured, reviewed, and resolved.

Result

Ground Control gave teams earlier visibility into quality and throughput problems across large-scale annotation programs. It helped identify broken workflows and low-performing segments before they damaged delivery.

The platform supported enterprise revenue through customer programs with Amazon, Netflix, John Deere, and Cruise. It captured and resolved 2,500+ unique dataset anomalies for Cruise, contributed more than $3 million in revenue impact, and scaled data platform operations by roughly 50%.

Why It Mattered

Enterprise AI programs fail when the work behind the model is invisible. Customers need proof that data is moving, edge cases are being handled, and quality risk is being caught before it becomes model risk.

Ground Control made distributed human-in-the-loop AI work legible enough to manage, improve, and defend in front of customers.

What This Proves

I can create and own enterprise-grade AI platforms from customer discovery and direct sales through product delivery and scaled adoption: messy workflows in, clearer decisions out, with enough structure for teams, customers, and compliance requirements to trust the system.