Our Team
Core Values
Mission-Focused
We have one central goal: deliver best-in-class software and services to enable our clients to solve the biggest, most complex marketing data challenges.
Courage
We stand up for what we believe in, do what needs to be done, are adept at problem-solving, and ask for help when needed.
Integrity
We foster a culture of trust, internally and externally, by relying on employees with a strong moral code and sense of personal responsibility.
Curiosity
We have a thirst for knowledge and are never afraid to ask tough questions. We try new things to drive new innovations in order to progress ourselves personally and professionally.
Leadership Team Members
Daniel Jaye
Mark Sneathen
Mark Costanzo
Jeff Storan
Tom Burg
Scott Sprunger
Joshua Brandt-Rauf
Tom Winchell
Michael Keohane
Narayanan R
Working at Aqfer
Our global workforce is a diverse group of individuals with a wide range of backgrounds and skills all coming together for one important goal: to help our clients and their customers overcome today’s most pressing data-related marketing and advertising challenges. We’re dedicated to understanding the constantly changing needs of our clients, and together we work to provide them with the solutions and services needed for them to thrive in today’s digital world.
Our core values are at the heart of everything we do at Aqfer and we’ve built an inclusive culture where our employees are able to grow personally and professionally. We of course value hard work, but we also believe strongly in a healthy work/life balance which is reflected in our remote-first work environment and flexible paid time off. Even though we may not always be together, building connections and strengthening our company culture are always top priorities, and weekly video gatherings and quarterly in-person events ensure we do just that.
Our Thoughts
The Agentic Mesh Is a Mess, But…
Nate’s takedown of the mesh struck a nerve because it spoke the quiet part out loud: most companies aren’t ready. But that doesn’t mean a fluid and dynamic agentic future is nonsense. It just means we need to focus less on diagrams – and more on the plumbing.
The real future of AI in the enterprise won’t be defined by self-directing agents hammering on a million keyboards instead of monkeys, but by reliable foundations for collaboration. Autonomous agents with exponential relationships are not the answer. However, in case I’m wrong, let me be the first to welcome our robot overlords.
Recent Acquisitions Advance the AI Infrastructure – But We’re Still Missing a Critical Layer
The acquisitions made by Snowflake, Databricks, and Salesforce are smart bets on where the AI infrastructure stack is going. But they also create an opportunity (and a need) to build out capabilities that turn data into understanding.
This means helping enterprise teams move beyond real-time ingestion and toward real-time interpretation. For marketing use cases, that means AI systems that understand customer history, resolve identity with confidence, and avoid the blind spots that lead to wasted spend or flawed personalization.
Crystal Ball Gazing: Why I’m Betting on MCP
The diversity of MCP implementations – from enterprise giants like Spring AI MCP to creative innovators like Figma MCP – reflects the protocol’s versatility. As AI adoption skyrockets, these MCPs are reducing integration friction, cutting costs, and enabling AI to deliver hyper-relevant results across industries.
The MadTech MCP Stack: Building Industry-Specific AI Standards
Imagine browsing a marketplace where you can discover, evaluate, and deploy marketing AI components like app store applications. A/B test different attribution models. Swap personalization engines without rewriting integration code. Deploy new optimization algorithms and measure performance impact immediately.
This isn’t speculative – it’s the logical evolution of MCP adoption in marketing technology.
From Prototype to Production: Simulating AI Solutions with AI
How do you test complex, interconnected AI systems before they go live? I’ve been experimenting with something that initially sounded absurd but has become indispensable: using AI to test AI systems.
The Architecture Revolution: Building Chainable AI Systems
I had a realization last month: I was spending more time thinking about orchestration than implementation. That’s when it struck me – we’re experiencing the same architectural transformation that revolutionized web development, but compressed into a timeframe that’s frankly breathtaking.