Coactive AI: website

Context and business problem

Coactive AI is a multimodal AI / content intelligence platform whose clients include NBC, Thomson Reuters, Fandom and Emplifi. The website had to explain a technical platform and turn visits into sales.

My job was to help Coactive get better results from Webflow: to make sure the build matched the brief, was well structured, and was delivered in the right order and at the right quality.

Users and their goals

• Website visitors: product, data and marketing leads at media and content companies evaluating whether the platform fits their content library; they need to understand the product quickly enough to keep reading and get in touch.
• The client’s team: a site they can grow and manage in Webflow.
• The developers: clear scope, priorities and architecture decisions so they can build without rework.

Constraints

• I was not the main developer: my impact came through scope, decisions, supervision and quality control.
• Design came from Figma; the work was in Webflow implementation, not redesign.
• Distributed, cross-time-zone collaboration, which is why the work mixed live sessions with asynchronous reviews.

My role and responsibilities

• Defining the scope from the client brief
• Setting the implementation order
• Supervising the whole build and other developers’ work
• Ongoing technical advice and Webflow architecture decisions
• Quality control
• Synchronous consulting (live decisions) and asynchronous consulting (brief analysis, planning)

Process and key decisions

This was a consulting and supervision engagement, so the “process” is the delivery process around the build, not a design process.

1. Brief → scope

I translated the client brief into a defined scope of work, so the developers knew exactly what was in and out.

2. Implementation order

I set the order in which the site was built.

Decision: build the shared structure and components first, then the pages.

Why: changes land in one place instead of being repeated page by page.

3. Architecture decisions

I made the Webflow architecture calls during the build. The architecture decisions that mattered most: the component system and the interactions.

4. Supervision and quality control

I reviewed the developers’ work and kept quality consistent against the Figma source and the brief.

5. Sync + async

Live sessions for decisions that needed a conversation; async for brief analysis and planning.

Where AI fits

I used AI to speed up reviewing the brief and preparing QA checklists; every finding was checked by me in Webflow before it went to the developers.

Solution

A live Webflow website, coactive.ai.

Outcome and impact

Webflow improvements that kept visitors on the site longer, and that time turned into purchases.

What I learned and what’s next

• Supervising other developers taught me that the biggest quality lever is the order of work and clear decisions up front, not fixes at the end.
• Next time I would agree on the success measures (e.g. time on site, demo requests) with the client at kickoff, so the impact can be reported with numbers.