Since joining Bluefish, I’ve spent time meeting with customers and partners about what they’re building and where they’re getting stuck.  

A recurring theme in those conversations is the need to connect insight with execution. Understanding how AI represents your brand gives teams a starting point, but putting that knowledge to work requires connecting it to their existing decisions, workflows, and systems. As customers embrace AI, they’re identifying needs and generating content for review at a pace their current processes weren’t designed to support.   

That’s the opportunity behind AI Solutions, the forward deployed engineering and customer solutions offering I’m building at Bluefish. 

Building alongside the customer 

Forward deployed engineering means working closely with a customer’s team to understand a problem, design a solution, and bring it into everyday use. It requires getting into the details of their data, technology, and ways of working so that what we build fits the business. 

That context shapes which systems need to connect, how a workflow should operate, and what will make it useful to the people adopting it.  

Our approach brings solution architecture, engineering, and customer expertise together around a clear business need. We identify a useful starting point, connect the right systems, and iterate alongside the customer until the solution fits. 

Connecting insights to everyday work 

Customer conversations are pointing to several areas where these connections can make a difference. 

Some customers want to connect Bluefish insights with their own analytics to better understand how AI discovery relates to business performance. Bringing those data sources together helps teams tie changes in AI performance to changes in referral traffic, conversions, or other business outcomes.  

Others need workflows that turn AI visibility findings into draft content, route it through review, and connect it to publishing and measurement. For example, a gap in how AI describes a product could inform a content update, with teams then tracking whether AI answers reflect correct information after publication. 

We’re also bringing Bluefish data and actions into the AI tools people already use. Teams need access to insights when they’re planning, creating, and making decisions. 

These are the kinds of problems AI Solutions is being built to address. In each case, the starting point is the customer’s priorities, the systems they are building, and the people who will use the solution. 

Two changes we can address together 

Marketing teams are navigating two changes at once: people increasingly interact with brands through AI, and AI is changing the way marketing gets done.  My discussions with customers lately are about the connection between the two.  

AI agents can instantly reason across all of your marketing channels, collapsing the customer acquisition journey from a series of touchpoints into a single moment. 

Because of that collapsed funnel, marketing to this new audience of agents requires a unified process for how marketing teams act on opportunities. That process has to be centered on decision-grade data, and must be shared across teams. 

By giving every team the same view of how AI describes the brand and which sources shape those answers, teams can agree on priorities, develop content that contributes to the larger message, and measure whether their work made a difference. With that process in place, marketing teams can shorten the loop between identifying a signal and creating content for the channels AI prioritizes.  

Our agency and technology partners are experimenting with solutions alongside us and our customers. It’s inspiring to see approaches taking shape and to help turn those ideas into solutions people can use.   

That’s what’s energizing about building AI Solutions at Bluefish: the chance to work closely with customers and partners, build with a talented team, and see ideas become part of how people work.