Customer Success, Reimagined for AI

Breaking the Mold of Traditional Customer Success

Customer Success has too often been boxed into renewals management or a reactive support function. At Deepgram, we’ve flipped that narrative. Because we’re an AI-native company, our CS team plays a fundamentally different role. Customer Success isn’t a department off to the side, it’s the center of gravity. Our customers’ voices, needs, and outcomes flow not just through our people, but also through the automations and systems we’ve helped design to surface insights, connect teams, and drive action. That centrality isn’t an accident; it’s how we ensure our customers’ success fuels the company’s success.

A Different Ownership Model

One of the biggest differences at Deepgram is how we structure ownership. Our CSMs aren’t “account owners.” Instead, Deepgram owns the account. That distinction matters.

In traditional CS models, a single CSM “owns” the relationship, which can create silos and bottlenecks. At Deepgram, success is the responsibility of the entire account team unit, Sales, CS, Engineering, Research, Data Ops, and Product working in lockstep. The CSM plays a pivotal role as the connector, strategist, and voice of the customer, but they don’t carry ownership alone. 

At Deepgram, this requires a different skill set than traditional CS. Our CSMs need to understand how AI models are built, tested, and deployed. They’re not just managing relationships, they’re guiding customers through the complex, iterative process of bringing AI into production. With AI, it’s not always about one right answer; it’s about understanding patterns, probabilities, and performance. Our role extends beyond guidance: we act as the customer’s voice inside Deepgram, bringing the full account team together to drive alignment, build true partnership, and shape product strategy around what customers are building, not just what we assume they need. This model is still evolving, but each step pulls us closer to a fully collaborative, customer-driven way of operating.

What this looks like in practice:

• Shared Accountability: If a customer is stuck in onboarding, Engineering, CS, and Product collectively own unblocking them. If expansion is on the table, Sales and CS jointly craft the path forward.

• Faster Alignment: Instead of waiting for a “handoff,” customers experience one unified Deepgram team.

• Stronger Outcomes: Because everyone is accountable, customers don’t get lost in translation. Every team is tuned into the business outcome, not just their function’s deliverable.

This model means Account Executives don’t close deals and disappear, and CSMs aren’t left holding the bag. Instead, everyone at Deepgram has a role in customer success. A customer has a feature request or a problem? The CSM doesn’t just take notes and relay feedback, we pull Product directly into the conversation. The same applies to Research, Engineering, and Marketing. We believe the best way to build the right thing is to let teams hear it firsthand from the source.

That approach creates collective accountability and ensures every part of the company understands their role in owning the customer’s success.

Turning Insights Into Action

Every conversation with a customer generates insight: what’s working, what’s blocking adoption, where opportunities exist. Instead of leaving those insights siloed, our account teams bring them directly into the rooms where decisions are made:

• With Research: We share patterns on model performance, use case complexity, and where AI-natives and AI-adopters are pushing boundaries. Here again, CSMs bring a unique skill: translating nuanced model behavior into business context. For many customers, it’s not intuitive why the same input might produce slightly different outputs. Our team has to demystify that and frame it in terms of reliability, trust, and how to measure success when working with AI.

• With Product: We highlight not just feature requests, but outcome-driven needs that shape the roadmap. This means going beyond “customers want X feature” and instead surfacing the why, the business outcomes customers are trying to achieve. By bringing customers directly into the conversation with Product, we make sure roadmaps are informed by real-world use cases and urgency, not just abstract requests. We’re still building the muscle for this across every account, but it’s already reshaping how Product prioritizes work.

• With Engineering: We ensure escalations and technical challenges are understood in context, why they matter to the customer’s business outcome. We take it a step further with our Applied Engineering team, not only pulling them into customer conversations but also partnering with them to shape how we scale our support function. Together we ask: what resources do customers truly need to be successful? That collaboration has led us to rethink the tools, processes, and even AI-powered solutions we build, ensuring support evolves in lockstep with customer needs.

• With Sales: We partner on growth, identifying upsell opportunities, and aligning account teams that ebb and flow based on customer needs. This requires deep collaboration, not a handoff. Sales brings market perspective, while CS brings adoption and outcome data, particularly from our top accounts where we’re already working closely together. To extend this approach across our entire customer base, we’ve provided the requirements and context to build automations and notifications that surface health signals, usage shifts, and expansion triggers, enabling Sales to engage at scale. The goal is to build account strategies that are dynamic, shifting as customers grow and their priorities evolve. In an AI-native model, this alignment is critical because customer adoption isn’t just about usage, it’s about trust in the technology.

Operationalizing & Centralization

One of the most powerful ways we operationalize this central role is through weekly cross-functional meetings like our RAM (Resource Allocation Meeting). These meetings are only 30 minutes, tight, focused, and fast. Pre-work is required, so when we come together, it’s in-and-out with decisions made on the spot. That speed and discipline are unique to how we operate as an AI company, where both our customers and our product teams are moving quickly, and we have to match that pace without sacrificing clarity.

In RAM, CS and Sales bring a holistic view of customer priorities, what’s critical, what’s at risk, what’s expanding, and work with Product and Engineering leadership to align resources. We’re not just talking about issues, we’re reallocating resources live in the meeting as needed. Each week, account owners and team members are responsible for providing updates, ensuring visibility and accountability.

When Customer Success is centralized, customers don’t feel like they’re navigating a maze of disconnected teams. They experience a single, coherent Deepgram, one that listens, adapts, and invests in their outcomes. For us, this isn’t just operational efficiency. It’s how we build trust, unlock expansions, and create long-term growth. 

Of course, no model is perfect. Do we always get it right? No. But our model allows us to quickly identify gaps and respond, whether that means deploying automation, building new tooling, streamlining our processes, or reallocating resources, so customers continue to feel supported. That ability to adapt has laid the groundwork for the next phase: scaling Customer Success in a way that matches the pace and complexity of an AI-native company.

Building for Scale & Impact

Scaling Customer Success at an AI-native company means building differently. When CSMs join Deepgram, I tell them to forget everything they think they know about Customer Success. We’ve thrown out the old playbooks. Instead, we expect them to adapt, to ebb and flow with one guiding principle: success is always defined by the customer.

Being a productive AI company has reshaped how we think about CS altogether. Our CSMs aren’t just managing accounts; they’re helping design the very systems, automations, and tools that will define how we support customers at scale. They work side by side with RevOps, Engineering, Marketing, Product, and Sales, ensuring customer insight drives how we prioritize, what we build, and how we deliver value.

We also push our CSMs to dogfood our own technology. They experiment, create prototypes of dashboards and other tools for Product to consider from the lens of our customers, and test how AI can be applied to CS itself, which often results in co-developing solutions with Applied Engineering, our Labs team, and others across the org.

For example, when our onboarding manager wanted to support more customers without adding headcount, they partnered with DevRel and Applied Engineering to shape requirements to build what will soon be a Voice Agent. This agent will help run pre-sales calls, onboarding calls, and eventually power many other touchpoints. It’s a glimpse into the future of CS at Deepgram: teams that don’t just serve customers but actively shape how AI transforms the customer experience.

At Deepgram, we like to say Customer Success is not a team, it’s a mindset. But it’s also the hub that makes the mindset real. By sitting at the center, we ensure that every decision, whether in research, product, or engineering, is grounded in the reality of our customers’ success. And we’re just getting started, I’m looking forward to showcasing what we have in the works.