Viget + AI

We’ve always valued well-rounded teammates who understand the whole system being built. People who look to new tools for productivity gains and quality improvements, but who can instinctively spot risky decisions and avoid brittle solutions that won’t last. These qualities helped our early clients in 1999 and they still guide our thinking to this day.

What’s different today is that AI tools allow small teams of generalists to accomplish what previously required larger, cross-functional groups of specialists. The skill lines are blurring across engineering, design, strategy, and research in ways that make everyone more capable. Roles and responsibilities are evolving quickly, and we’re adapting as we staff projects and extend client teams.

Our work has always been delivered by small, empowered, and focused teams. In this era of AI, we’re thriving.

AI has touched nearly every discipline at Viget. The honest version of that story is messier and more interesting than a bullet list of tools we’re using. Our internal cross-discipline AI Committee has been working since 2024 to ensure all roles at Viget stay current on capabilities, and all client work is improved thanks to AI.

Engineering. Our developers currently use Claude Code, Cursor, and Codex for agentic development, and have published an open-source library of reusable AI agent skills used across the team and available to the broader developer community.

They've built AI-powered search into client CMS platforms using Meilisearch and RAG (retrieval-augmented generation), prototyped a Viget-specific chatbot using Next.js and MCP to search our articles and project history, and shipped functional AI applications during our annual Pointless Palooza hackathon.

They've also done the harder, less glamorous work: establishing shared standards for AI-assisted codebases so that generated code doesn't become tomorrow's technical debt.

Strategy. Our strategy team has spent real time working through what using AI as a thinking partner – not a faster search engine – actually means for client work. The conclusion: AI is most useful when it sharpens judgment rather than replaces it, and the quality of what comes out mirrors the clarity of what goes in. That framing has shaped how we approach AI-assisted strategy engagements.

Project Management. Our PMs use AI for meeting transcription, action item extraction, and synthesis – freeing attention for the decisions that actually require human judgment. We evaluated more than a dozen transcription tools before selecting candidates for a structured month-long pilot across project types, security requirements, and team workflows. That's representative of how we approach tool adoption generally: methodically, not impulsively.

Research & Design. At Pointless Palooza 2026, a cross-functional team built a functional AI language app in 24 hours. The gap between research insight and working prototype is narrowing fast, and our research and design team is actively closing it. That kind of hands-on experimentation feeds directly back into our client work.

AI isn't just changing how we work internally, it's showing up in the products we're making for clients. Viget has been building AI-powered  products and features across finance, healthcare, and consumer platforms.

We've built AI-powered search and content discovery for enterprise CMS platforms, autonomous internal workflows replacing manual production processes at scale, and AI-integrated native apps and product experiences for clients in a variety of industries. Our work with Robinhood spans native app, product design, and AI product work across a 23M+ user platform. Standard AI, which pioneered AI-powered autonomous retail checkout, and Spectrum AI, an AI-driven healthcare data platform, are two clients we started working with well before the current LLM era.

Content-focused websites remain a core part of what we do. Increasingly, a great website isn't just optimized for search engines, but for the AI answer engines (ChatGPT, Perplexity, Google AI Overviews) that are becoming many users' first point of contact with a brand. Our AEO (Answer Engine Optimization) approach helps clients understand and improve how they appear in AI-generated answers as part of discovery, content strategy, and post-launch measurement.

We've also formalized how we talk to clients about AI use in their own projects. Our AI Acceptable Use Policy covers data privacy protocols, approved tool evaluation criteria, and how we maintain quality standards when AI is part of the delivery process. It reflects a simple commitment: every deliverable gets the same human review and quality bar, regardless of what tools were involved.

We're investing seriously in AI because the upside for our clients and our team is real. We're also clear-eyed: not every problem needs an AI solution, not every tool that launches this quarter will matter next year, and the existential questions about AI's impact on creative work and our industry are ones we're engaging with directly, not waving away.

What we offer is experienced judgment in a space where experienced judgment is genuinely scarce. We've been learning by doing – on our own time, in our own tools, and with clients who trust us to help them move carefully and confidently through a genuinely uncertain moment.

If you're evaluating how AI fits into your digital products and platforms, that's exactly the kind of conversation we're built for.