RevvedUp: redesigning an account-based marketing software MVP for scale.

We worked as an embedded part of RevvedUp’s team, designing alongside development and moving in short, focused cycles. Our process looked like this:

• close collaboration with stakeholders, treating feature discussions as working sessions to clarify goals and edge cases early;

• fast design iteration, moving quickly from concepts to review and preparing clear specs for development;

• AI-assisted prototyping, using lightweight prototypes to validate complex flows and gather feedback;

• tight alignment with engineering, adjusting designs during implementation to stay realistic and buildable.

Everything in RevvedUp starts with a clean, enriched contact list. We redesigned the contact experience to make key information easy to find from the start:

• contacts can be added from multiple sources and immediately enriched with company and role context;

• personas and segments are applied automatically, so users don’t need to sort lists by hand;

• all relevant details, past activity, and campaign history live in one place;

• filters make it easy to slice audiences by persona, tier, or industry when planning campaigns.

RevvedUp’s AI research engine (Revveal) helps teams understand who to engage and why. We shaped the experience so insights appear exactly where users choose who to engage:

• contacts are automatically matched to the right company, removing manual cleanup;

• AI generates a short company summary, so users understand what a business does at a glance;

• signals like funding, hiring, or tech changes highlight why an account might be worth engaging now;

• ICP Fit and Potential scores help teams prioritize accounts instead of relying on gut feel;

• research is shown inline in a scannable layout, keeping decisions close to the workflow.

Sequences are where research turns into action, but setting them up used to take effort and attention away from actual outreach. Teams needed a way to move from insight to execution without friction.

We redesigned the sequence experience to make it easier to plan, review, and scale outreach:

• a clear, linear flow helps users set up channels, steps, and timing without second-guessing the order;

• improved step hierarchy makes it obvious what happens next at every stage;

• AI-generated messages use persona, company context, and signals to speed up personalization;

• users can switch smoothly between AI-generated copy and their own writing;

• review and approval flows support teams that need quality control at scale.

For many RevvedUp users, personalized campaigns didn’t stop at messages, but creating matching landing pages often meant switching tools and losing context.

We introduced a landing page builder directly inside the platform so teams can create and personalize pages without breaking their workflow:

• users can build reusable landing page templates within RevvedUp;

• flexible components make it easy to adapt layouts for different audiences;

• once approved, AI personalizes content automatically for each lead;

• pages stay connected to campaigns, sequences, and performance data.

As RevvedUp grew, launching a campaign meant juggling multiple decisions at once. Teams needed a clearer way to bring goals, audiences, and sequences together without jumping between screens.

We redesigned campaign setup into a single flow:

• campaign goals and success criteria are defined upfront, setting intent from the start;

• audiences can be built using persona, tier, or industry rules instead of manual lists;

• existing sequence templates can be reused across campaigns;

• scheduling and pacing are controlled from one place;

• teams can review exactly which contacts are included before launching.

For RevvedUp users, sending messages is only half the job. Teams needed to see what actually resonates and where engagement builds over time without digging through reports.

We shaped engagement tracking so insights stay close to everyday work:

• engagement signals like opens, clicks, replies, bounces, and LinkedIn interactions are captured automatically;

• conversations land in a unified inbox, keeping replies and context in one place;

• engagement rolls up from individual contacts to account- and campaign-level views;

• AI highlights patterns across messages, steps, and personas to show what’s working;

• key engagement events sync back to CRM and can trigger next actions.

The result is an ABM software experience that supports scale while staying intuitive for day-to-day revenue work.