GTM Engineer with AI, MCP for sales teams, Eng. Nights in SF
interview of builders, behind-the-scenes, AI use case of the week, meet us IRL
🪄 Hey there, welcome to the fifth issue of ‘Another one bites on Dust’, where we share what’s happening in and around Dust and some insider takes for AI builders, tinkerers and enthusiasts.
AI is fundamentally transforming both our personal and work lives, altering the way we act, the way we think, and the way we collaborate. This profound change requires new environments. We're building Dust to serve as the operating system for AI-driven companies. When we're done, work won't be the same.
We hope this email helps at least a bit to understand what to do/how to use AI in your lives, and if there’s more that we could/should be doing, feel free to leave a comment, we read them all.
Long live the builders: Lara Garrido
Hey Lara, can you introduce yourself?
Hi! I'm Lara, I'm Franco-Spanish and I recently moved to New York to join Clay as a GTM Engineer. I previously lived in Paris, San Francisco and Lisbon and I'm very lucky to speak 4 languages. My background is in Growth, both in-house and as a co-founder of an HR Tech. I've always been drawn to roles that sit at the intersection of sales, marketing, and product, which is exactly what makes the GTM Engineer position so exciting. When I'm not diving deep into data engineering challenges, you'll find me exploring new coffee shops or planning my next travel adventure.
What Clay is doing exactly?
Clay is a tool for go-to-market teams to grow revenue by providing access to over 130+ external data providers in a single place. We're essentially a data enrichment and orchestration platform that helps RevOps, Marketing, and Sales Ops leaders discover what we call their "GTM Alpha" - their unfair data advantage.
Our key features include waterfall enrichments that can search multiple providers sequentially, automated workflows that eliminate repetitive tasks, and AI agents that handle everything from research to crafting personalized messages. Instead of relying on firmographic data that everyone has access to, Clay enables teams to build hyper-specific data points at scale. Think "SaaS companies that recently raised Series B and use a specific tech stack."
This combination helps our customers, from startups to enterprises, achieve GTM efficiency at scale. It's about moving beyond spray-and-pray outreach to laser-focused, genuinely relevant engagement.
How would you describe your role? (especially since Clay pioneered the idea of a GTM Engineer)
The GTM Engineer role combines what used to be separate functions: prospecting, closing deals, and technical demos, into one position. Instead of passing leads between different people, I handle the entire customer journey, which creates a much smoother experience for everyone involved.
My day-to-day varies quite a bit. I might be taking calls with companies, brainstorming on relevant data points for their GTM strategy or building tailored Clay tables. Since I'm actually using Clay every day to build these systems, I can immediately show customers how to solve their specific challenges rather than just giving a generic demo.
What I really enjoy is the product feedback loop. When I'm working with customers and they mention a pain point or wish they could do something differently, I can take that directly to our product team. It's rewarding to see those insights turn into actual features that make both my job and our customers' lives easier.
The role is also structured differently than traditional sales, it's more product-focused than commission-focused. Sure, revenue matters, but I'm also evaluated on the quality of feedback I provide to the product team and how well I help teammates learn from the workflows I build. This makes this role super cross-functional, exciting and ever evolving.
(If you wanted to dive deeper, partnered with Clay directly and published a whole guide: Why GTM engineering is becoming one of the most popular startup roles.)
When did you start using AI in your life?
My first real encounter with AI was probably around 2022 when I saw a post from someone from my network in San Francisco that was part of the founding OpenAI Engineering team. I got super curious and like anything I get curious about, I get on a tangent and spend a lot of time. It felt exciting to have these unlimited options opened by AI that made us rethink entirely our working and ethics philosophy. I think beyond the should we or not automate question came a self-worth and tricky question of can AI do my job better than me? I always saw AI as a leverage, a fuel, that could make me, my jobs and my tools 10x better, but it was and is still an exciting time to see how we're evolving as a society having now such a powerful tool in our hands.
What were your very first experiments?
My first real experiment was building a lead scoring model that used AI to analyze company websites and social media presence to predict buying intent. I was skeptical at first: how could an AI understand the nuances of B2B buying signals? But when I tested it against our historical conversion data, it was scary accurate. My first thought was honestly a mix of excitement and mild panic: "If this works this well already, what's it going to be capable of in two years?"
You joined Clay very recently - how did AI accelerate your onboarding?
Clay's onboarding was already incredibly well-structured, but Dust drastically accelerated my learning curve. (How Clay is powering 4x team growth with Dust) I used Dust to ask a range of questions from "where is the intimate alcove room?" to "where is the sales deck?" and "what is our pricing structure?". It helped me understand both how Clay and GTME worked without having to ask anyone in the team and disrupt their daily flow. In only 2 weeks I already asked +50 questions and felt ready to start taking calls. In-person meetings were then super focused on asking more advanced questions and getting to know people on my team. Dust not only accelerated my knowledge but also my confidence level. Knowing you can always have a quick answer to a question even when you're taking your first call, changes your whole mindset.
How would you describe your relationship to AI (both professionally and personally) today?
At work, AI is deeply integrated into everything I do at Clay, it's my coworker in so many ways. It is what helps me create magic and this wow moment for clients. I use our own model, Claygent, to turn unstructured data into structured data points. With some good prompting and creative skills you can turn any research such as "what company was recently SOC II type II certified and switched CISO" into an actual data point that could be leveraged by companies as a signal and get in front of a strategic account. What would take them hours to find can now be generated at scale in a few seconds.
Personally, I've been recently using AI to get around NYC and so far the suggestions have been right on point, from transportation and scenic routes to work to finding the best restaurant spots. I'm also building some tailored prompts to automate administrative tasks I dread the most, which really adds up when you've been living in different cities.
Can you share your most frequent use cases?
My most frequent use case is probably using Dust to help me find data points and common industry use cases drawn from our client tables to prepare tailored demos super fast. Clay is also used by Clay to automate most of our pre-meeting notes and follow-up emails. I receive every day super specific and tailored research about companies I'm meeting, so I know their most recent product launch, recent leadership changes or strategic GTM hirings and initiatives and can ask more specific questions or answer in a more tailored way.
I also have my own tailored prompts on Claude. Two of them were created by my colleagues, Rahul and Alex. One helps me generate more complex emails that include a lot of resources we share and adapts to various scenarios. The other one helps me generate ICS, Ideal Customer Signals for a given company. This helps me start brainstorming with a client with some great ideas in mind and get creative from there.
And the one that blew your mind the most?
Recently, I was working with a client who wanted to identify companies based on a very specific sponsorship criteria. Before using Claygent, it was done manually, researching each account individually, which was incredibly time-consuming and limited in scope. By leveraging Claygent, our AI model, I created a prompt that would surface these opportunities at scale based on their ICP criteria. This unlocked entirely new market segments and opportunities that their team had never been able to identify before. It was crazy to see how we're not just automating processes with AI but seeing in practice how finding the right data point and prompting it well can have a drastic and immediate impact on sales teams.
How do you see your work changing?
I think the GTM Engineer role is going to become even more strategic. As AI gets better at handling routine data enrichment tasks, I'll spend more time on high-level strategy and creative problem-solving. Instead of manually building every workflow, I'll be designing AI-powered systems that can automatically adapt enrichment strategies based on campaign performance.
I also see myself becoming more of a "data translator", helping non-technical teams understand what's possible with AI-powered data enrichment and how to implement it effectively. The companies that win will be the ones that can most creatively combine human insight with AI capabilities.
What would you advise a 'young Lara' starting to explore growth and GTM today?
First, get comfortable with data, not just analyzing it, but thinking creatively about where valuable data might exist and how to access it. The future of GTM is about data advantages, not just better messaging.
Second, learn to prompt AI effectively. This is becoming as important as knowing Excel was a decade ago. The professionals who can clearly communicate complex data requirements to AI systems will have a huge advantage.
Finally, always remember that AI is a tool, not a strategy. The most successful GTM professionals will be those who can combine AI capabilities with deep understanding of customer psychology and market dynamics. Don't let the technology distract you from the fundamental goal: creating genuine value.
Last but not least, where would you love to see AI evolve in the future?
As far as go to market, I'm most excited about AI becoming better at understanding context and nuance in B2B relationships. Right now, AI is great at identifying signals, but it still struggles with the subtle relationship dynamics that drive B2B decision-making.
Ultimately, I'd love AI to help dogs live longer and achieve world peace but I think this one is still on humans to figure out, AI can't solve all of our tedious problems which in a way is great news for all the great innovators yet to come!
This is awesome, thank you Lara!
Behind-the-Dust
🤖 Direct news and links about Dust from people inside and outside of the company.
Aubin detailed how we taught AI agents to navigate company data like a filesystem (here).
Talking about data, Thibault shared how Alan, a €4 billion digital health insurer across four countries, scaled analytics with Dust (here).
Pauline published our latest Product Update (here). Your agents can now create email and calendar invites, be chained together for complex workflows, and integrate with Salesforce, HubSpot, and Notion in new ways.
VentureBeat documented our $6 million in annual revenue milestone; as well as our selection in Anthropic’s “Powered by Claude” ecosystem (here).
We enjoyed our second Session for Leaders in Paris where Gabriel hosted a roundtable discussion with two AI transformation pioneers who have successfully scaled AI across their organizations: Claire Lebarz, CTO at Malt, and Stéphane Tang, AI Transformation Expert at Qonto.
Use case of the week
Our latest Session for Builders showcased the game-changing potential of MCP for sales teams. Instead of outdated, rigid automation, MCP brings intelligent, AI-first solutions that seamlessly integrate with your existing sales tools.
You can enjoy below 👇 the full replay and Remi took a closer look here at the use cases covered during the session.
Meet us IRL
We mentioned last time our very first Eng. Night in San Francisco on July 9. Register here.
Well, considering the level of enthusiasm, we decided to run another one the week after, on July 15.
French Touch 🇫🇷
We have offices in San Francisco and Paris (and we’re hiring like crazy in both spots), so we do have people within the team and customers who speak ‘the language of Molière’ (as we say in French!).
