Enterprise Customer Success Manager

Overview

You own relationships with enterprise customers who've integrated Tavily's API into their AI applications - think companies building AI-powered research tools, autonomous agents, or AI-enhanced products that need real-time web data. You're making sure their implementation works at scale, they're getting value from the platform, and you're identifying opportunities to expand usage across their organization.

Role Snapshot

Company Context

Stage: Early-stage (75 employees), recently acquired by Nebius (backed by former Yandex leadership)

Size: 75 employees

Growth: Serving 1M+ AI builders, seeing "incredible demand" from enterprises per the post - high growth mode

Market Position: Category infrastructure play - building picks-and-shovels for AI agents, not competing with AI models themselves

GTM Reality

Customer Profile:

• Mix of AI-first companies and traditional enterprises adding AI capabilities
• Technical buyers (AI/ML engineers, product managers building AI features)
• Usage-based pricing means your accounts can grow quickly or churn fast

Team Structure:
You report to VP of Customer Success (Christie). Likely a small CSM team given company size - you'll be building processes as you go, not inheriting mature playbooks.

Cross-functional Work:
You'll work closely with product and engineering teams since customer feedback directly shapes the platform roadmap. This is real input, not just submitting feature requests into a void.

Competitive Landscape

Main Competitors: Other API providers for web data/search (traditional web scraping tools, search APIs, potentially custom-built solutions)

How They Differentiate: Built specifically for AI agents (not humans) - optimized for LLM consumption, handles scale, has built-in safeguards

Common Objections:

• "We can build this ourselves" (engineering teams at larger companies)
• Cost concerns as usage scales
• Latency/reliability questions for production use cases

Win Themes:

• Time-to-market (don't spend 6 months building web infrastructure)
• Scale handling (their caching/indexing handles volume spikes)
• Integration ease (works with OpenAI, Anthropic, Groq out of box)

What You'll Actually Do

Time Breakdown

Account Reviews (30%) | Technical Check-ins (25%) | Expansion Conversations (20%) | Internal Coordination (15%) | Admin/Reporting (10%)

Key Activities

• Weekly/Biweekly Account Check-ins: You're reviewing API usage metrics, checking for issues, understanding what they're building next. Some calls are 15-minute pulse checks, others are hour-long planning sessions with their AI product team.

• Technical Troubleshooting: When customers hit rate limits, have latency spikes, or need help optimizing their queries, you're first responder. You're not writing code, but you need to understand their implementation well enough to loop in engineering or provide guidance.

• QBR Prep and Delivery: You build usage reports, ROI analyses, and roadmap alignment presentations. You're showing them how Tavily is powering their AI features and where they could expand usage to other use cases.

• Expansion Hunting: You're identifying new teams or products within the account that could use Tavily. If they started with one AI agent, could five other teams benefit? You're mapping the org, finding champions, and running expansion deals.

• Product Feedback Loop: You're Slack-ing with product managers weekly about what customers are asking for, what's breaking, what use cases are emerging. Your input matters because they're still building this category.

• Renewal Management: You're tracking contract end dates, building business cases for renewal, negotiating terms. Given it's usage-based, renewals can be complex if usage dropped or they want to restructure.

The Honest Reality

What's Hard

• Technical Depth Required: You need to understand AI agent architecture, API integration patterns, and LLM workflows well enough to have credible conversations with ML engineers. If you don't have technical CSM experience, the ramp will be steep.

• Usage Volatility: API usage can swing wildly based on customer product launches, testing phases, or budget cuts. Your revenue can drop 40% in a quarter if a big customer pauses development, and there's only so much you can do about it.

• Early-Stage Chaos: At 75 people post-acquisition, processes are immature. You'll deal with product bugs, unclear pricing policies, and shifting priorities. If you need structure and clear playbooks, this will frustrate you.

• Customer Sophistication Varies: Some customers are sophisticated AI companies who know exactly what they need. Others are traditional enterprises experimenting with AI who need a lot of education. The latter group takes 3x the time.

What Success Looks Like

• You maintain 95%+ gross retention and drive 120%+ net retention across your book through expansion
• Your accounts are actively engaging (weekly API usage, attending roadmap sessions, providing feedback)
• You close 2-3 expansion deals per quarter, bringing new use cases or teams onto the platform
• You're surfacing insights that shape product roadmap decisions

Who You're Working With

Primary Contacts:

• AI/ML Engineers: Building the agents, integrating the API, caring about latency and reliability
• Product Managers: Own the AI product strategy, care about time-to-market and capabilities
• Engineering Leaders: Make build-vs-buy decisions, approve budget increases, care about vendor risk

What They Care About:

• Does Tavily's API keep working reliably at scale as their product grows?
• Can they ship new AI features faster using Tavily vs building web infrastructure themselves?
• Are they getting unique capabilities (quality, speed, safeguards) they couldn't easily replicate?
• Is the cost structure predictable enough to build into their product economics?

Requirements

• 3-5 years in technical customer success, ideally with API products or developer tools
• Experience with enterprise accounts ($50K+ ACV) and navigating complex organizations
• Strong enough technical understanding to discuss API integration, agent architectures, and LLM workflows - you don't need to code but you need to follow technical conversations
• Track record of driving net revenue retention through expansion and retention
• Comfortable in early-stage environment - you'll be building the CSM function, not inheriting it
• Genuinely interested in AI/agentic systems - you're going to be learning constantly as this space evolves