How a Leading Digital Creative Agency Turned Every Role into an AI-Native One with elvex

A large, award-winning digital creative agency with a global footprint and a roster of Fortune 500 clients. The agency's work spans brand identity, website redesigns, digital transformation, and integrated marketing. Their teams are cross-disciplinary by nature: copywriters, strategists, designers, product managers, and engineers all collaborate on the same engagements.

The challenge was making AI useful to everyone, across all of those roles, on all of those projects, at once. They chose elvex.

Results: Faster Delivery, Broader Adoption, and a New Way of Working

• Competitive analysis timelines compressed from days to hours. Work that previously required manual side-by-side review of competitor sites and copy could be done conversationally, with agents pulling directly from live sources.
• Persona development cut from weeks to a single working session. Research-backed personas that once required extended synthesis cycles were generated and refined in real time, with agents drawing on dozens of proprietary interview transcripts.
• PRD and ticket drafting reduced by an estimated 60%. Product and engineering teams reported spending significantly less time on first drafts of requirements documents, user stories, and acceptance criteria.
• Transcript review went from a bottleneck to a utility. Teams stopped treating stakeholder transcripts as archives to dig through and started treating them as searchable, queryable project resources.
• Adoption grew without a top-down mandate. New agents were created for each new client engagement as a matter of course, with teams building on patterns established by early adopters across the agency.

Before elvex: Context-Free Chat Wasn't Cutting It for Complex, Multi-Client Work

The agency's teams juggle multiple complex client engagements simultaneously. Each workstream generates massive amounts of context: stakeholder interviews, brand guidelines, competitive audits, design systems, content strategies, and technical documentation.

Before elvex, the AI experience was shallow. Someone would paste a chunk of a document into ChatGPT, get a generic answer that didn't know anything about the client or the project, and move on. The people who got value out of AI were already power users. Everyone else hit the limits of context-free chat and went back to working the old way.

They needed a platform where any team member could build AI workflows connected to the actual materials driving their projects.

Adoption Started Organically, Across Both Creative and Technical Teams

The agency adopted elvex, and people started building immediately. What stood out was the range: creative teams and technical teams independently started building agents tailored to their own work. They connected their own project data, wrote their own rules, and created tools that matched how they actually think and operate. 

Creative Teams: Brand Voice, Persona Research, and Pitch Copy at the Speed of Conversation

Brand Voice Agents. For a major client website redesign, teams built agents loaded with brand narratives, stakeholder interview transcripts, audience research, verbal identity guidelines, and site architecture documents. The agents made all that context immediately accessible, so writers could start drafting with phrasings grounded in the source material. Companion agents connected competitor websites as data sources, turning multi-day competitive copy analysis into a conversational workflow.

Persona Generation. For a financial services client, agents were built on top of 45+ proprietary interview transcripts and segmentation research. Teams could generate credible, research-backed personas structured for design discussions in minutes.

Pitch Development. For a healthcare website redesign pitch, an agent synthesized tone and voice across a client site and multiple competitors, pulling from Figma breakouts, strategy decks, and reference materials simultaneously to help draft patient-facing copy.

Technical Teams: From Transcript Review to Dev-Ready Tickets in Minutes

Project Knowledge and PRD Drafting. For a major platform redesign, agents loaded with all project documentation and stakeholder transcripts served as institutional memory: synthesizing research, producing feature lists and PRDs, surfacing open questions, and flagging gaps that still needed human input.

Quote Extraction and Meeting Recall. Agents turned hours of transcript review into structured retrieval. One pulled exact stakeholder quotes tagged by project pillar with confidence scoring, ready for presentations. Another made meeting transcripts searchable, so teams could instantly recall what a client said about a specific feature.

Dev Ticket Writing. For a publication platform build, an agent produced user stories, acceptance criteria, element breakdowns, accessibility considerations, and responsive layout specs from feature descriptions or design screenshots.

QA Automation Support. An agent loaded with testing documentation, implementation roadmaps, and design pattern conventions provided architect-level guidance for QA engineers working with Playwright frameworks.

Real Data In, Real Adoption Out: Why the Approach Spread on Its Own

The pattern is what stands out here. People across creative and technical roles independently built deeply contextualized tools, loaded with proprietary client research, brand guidelines, transcripts, and technical docs, that transformed how their teams work. The value came from connecting AI to real business data, and the adoption spread naturally: when one person builds something that saves hours, teammates notice, and the approach replicates across new projects and clients.

This is what it looks like when AI is treated as a team sport. An entire organization building new capabilities into how they work, role by role, project by project.