What we built and how

Choosing Sustainability as V1

The first question was where to start. Full product compliance was the core business, but also the most complex entry point. Together with the PM and executive team, we decided to validate the AI-first approach through a Sustainability vertical first — lower regulatory complexity, faster path to a testable product.

From there, I led the definition of V1: deep customer discovery to understand what compliance SMEs and business leaders actually needed from an automated system, what would block adoption, and what "good enough to trust" looked like in this domain. I designed V1 and led the validation with early customers.

Results: ~20 paying customers, ~$500k ARR, including enterprise accounts like ABF and Starbucks. More importantly, it told us exactly what was blocking broader adoption — content credibility and coverage confidence. That evidence shaped everything that came next.

Defining Product Compliance

With V1 validated, I led the definition of the next product: full Product Compliance. Same approach — rapid discovery, close customer liaison, definition before engineering commitment, then design. The shift in product framing that came out of that work:

From: Assess each regulation manually
To: Can I sell this product in this market, and what do I need to do?

The sequencing decision that unlocked trust

The first version shipped full AI automation — it was accurate, but users focused on what was wrong rather than what was right. Onboarding stalled. Working with the team, we introduced deterministic logic first, then layered AI on top. That gave users a fast, reliable baseline before surfacing AI refinements. It shifted the experience from "prove the AI is correct" to "here's a starting point you can build confidence in." That sequencing decision — not the AI itself — was what unlocked adoption.

Migration sequenced like a product launch

The migration off the legacy platform was treated as a product problem, not an ops handover. The first cohort was ~$200k ARR — selected for low data complexity and low migration friction. The goal was to use it to build the playbook for the higher-risk accounts, not to move fast.

Solving the adoption problem

The biggest risk wasn't the system. It was whether compliance SMEs — whose professional identity was tied to doing this work manually — would actually use it. They weren't resistant because the AI was wrong. They were resistant because automation challenged their expertise. That's an identity problem, not a usability problem. The response: prioritise the business outcome experience for P&L owners, give SMEs transparency and control — not to prove accuracy, but to let them maintain their expert role within an automated system.

What this taught me

Correct AI outputs aren't enough. Users need to understand why before they'll act. Building trust is a product strategy problem, not an engineering problem.

And adoption is the product. The system working is a necessary condition, not a sufficient one. The question is always: will the right people actually use this in their daily workflow?

Impact

• ~$500k net new ARR from zero (Sustainability vertical)

• 9 new logos, 7 upsells/retention saves, 4 renewals in first phase

• $3.76M at-risk ARR in active protection motion

• Onboarding time: weeks (solution engineer-led) → minutes (self-serve)

• Sales cycle: ~5-month enterprise cycle → self-serve / faster conversion

• V1 → V2 shipped, ~30-customer beta, positioned for scale