Building Numa AI: Creating a Scalable AI Platform

Project Summary

When I began exploring AI opportunities at Enumerate, the organization had identified AI as a strategic priority, but there was no unified vision for how it would fit into the product ecosystem. Individual ideas were emerging across the business; from reporting and bank reconciliation to workflow automation and portfolio management, but each initiative risked becoming an isolated feature with its own experience, adoption challenges, and maintenance burden.

I led the experience vision, product strategy, and end-to-end design for Numa AI, a unified AI platform spanning both Enumerate Central (B2B) and Engage (B2B2C). Rather than treating AI as a collection of disconnected features, I defined a scalable platform strategy that combined contextual AI experiences, conversational interfaces, workflow automation, portfolio-level operations, and financial intelligence into a cohesive offering.

The result was Enumerate’s first AI product package. Our initial business goal was to acquire five customers and generate $65,000 ARR within the first three months. We exceeded that target in the first week, signing six customers and generating approximately $120,000 ARR.

Business Context & Opportunity

Enumerate serves property management companies responsible for managing communities, homeowners, accounting operations, requests, violations, architectural reviews, financial reporting, and more.

Over time, the platform had grown into a comprehensive operational system, but many workflows remained highly manual. Customers spent significant time navigating between communities, configuring repetitive settings, generating reports, reconciling bank statements, and maintaining operational consistency across large portfolios.

At the same time, AI adoption was accelerating across the software industry. We recognized that AI would play an important role in Enumerate’s future, but several strategic questions remained unanswered. These questions became the foundation of my work:

Understanding Where AI Could Create Value

Rather than starting with AI capabilities, I started with customer workflows.

Using product analytics, customer feedback, support insights, onboarding observations, and cross-functional workshops, I identified the highest-friction workflows across our customer base. These became the foundation of our AI roadmap.

The Strategic Insight

The biggest realization was that AI should not be treated as individual features. It should be treated as a platform. Instead of creating separate AI experiences throughout the product, I proposed a unified ecosystem that combined:

Key Decisions & Tradeoffs

Building a Platform Instead of Individual Features

The fastest path would have been releasing AI capabilities independently. Instead, I advocated for a unified platform approach that would scale over time and create a more cohesive customer experience. This required additional upfront strategy work but significantly reduced long-term complexity.
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Prioritizing Value Over Novelty

Rather than pursuing highly visible AI demonstrations, I focused on workflows with measurable business impact. Reporting, reconciliation, portfolio management, and automation were selected because they represented significant operational burdens for customers and clear opportunities for efficiency gains.
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Ruthless Prioritization‍

One of the biggest constraints was time. We had approximately four months to define, design, and deliver a comprehensive AI offering. As a result, I worked closely with stakeholders to prioritize foundational capabilities while intentionally deferring several larger initiatives into future releases. The goal was not to build the entire vision immediately but to establish a scalable foundation that could evolve over time.