Rebuilding Rocketlane's RevOps from 0-1 and 1-10

Introduction

In this webinar hosted by Srikrishna Swaminathan, co-founder of factors.ai, Dhiraj Kumar, Head of RevOps at Rocketlane, shares the operational playbook that supported Rocketlane's growth from Series A to Series C. The session covers how Rocketlane scaled its GTM team, increased ACVs by 10x to 15x, and managed over $100 million in funding without bloating headcount. Readers will learn how to build a three-layer RevOps framework, make high-conviction tooling bets, and deploy AI agents to eliminate operational friction.

About Dhiraj Kumar

Dhiraj Kumar heads RevOps at Rocketlane, where he has spent three and a half years scaling the company's revenue operations from Series A through Series B and Series C. During his tenure, Rocketlane has raised over $100 million in funding and achieved 10x to 15x growth in ACVs. Prior to Rocketlane, Dhiraj held roles at Salesforce and other technology companies, where he developed his expertise in CRM administration, data engineering, and first-principles operational execution.

The Three-Layer RevOps Framework

Dhiraj's team operates on a structured, three-layer framework designed to scale operations systematically rather than reacting to ad-hoc requests.

• Systems and data hygiene: When Dhiraj joined, Rocketlane had 3 to 4 AEs, 3 to 4 CSMs, and a GTM team of 10 people. The first step was auditing the existing HubSpot setup, which had been built by an external agency, to ensure the sales stages matched Rocketlane's actual sales process rather than a generic playbook.
• Metrics and alignment: Dhiraj prioritizes organizational alignment over lagging indicators. Instead of tracking every standard metric like MQL-to-SQL conversion rates, the team continuously questions whether a metric directly ties back to revenue.
• The retail analogy: Dhiraj compares obsessing over minor metrics to a retailer focusing solely on "revenue per square foot" and shrinking their store to optimize it, which ultimately hurts overall revenue.
• Programs execution: Rocketlane delayed launching complex operational programs until the first two layers (systems and metrics) were stable. Today, the RevOps team runs revenue-generating programs directly, but any task that does not touch revenue is relegated to "Friday second-half" work.

The Early Bet on Snowflake and Tableau

At Series A, Rocketlane made a contrarian decision to invest in enterprise-grade data warehousing rather than hiring manual analysts to pull reports.

• The manual burden: To build a funnel report, Dhiraj originally had to download data exports from 5 to 6 different tools daily, manually map them using primary keys, and reorganize the spreadsheets whenever a metric changed.
• The headcount alternative: Instead of hiring regional analysts for every regional leader as the GTM team scaled toward 50 people, Rocketlane's founders, Vignesh and Deepak, backed Dhiraj's proposal to build a centralized data stack.
• The tech stack: Rocketlane invested in Snowflake and Tableau at Series A, a move Dhiraj notes is typically reserved for Series E or F companies.
• Engineering support: Because Dhiraj was not a data engineer, co-founder Deepak allocated internal engineering resources to assist with the initial evaluation, data ingestion, and normalization setup.
• Hiring a data engineer: Instead of hiring a traditional business analyst to perform repetitive reporting tasks, Rocketlane hired a dedicated data engineer to build self-serve data models.

Prioritizing Tooling Over Headcount

Rocketlane enforces a strict policy of using advanced software APIs and automation to solve operational bottlenecks before adding headcount.

• The enrichment bottleneck: When scaling outbound sales, many GTM teams hire manual list-builders to copy-paste contact details from tools like ZoomInfo.
• API-first integration: Rocketlane chose to pay for the highest-tier plans of enrichment tools to access their APIs directly, automating the data flow rather than hiring manual data cleansers.
• The "Builder" hiring profile: Dhiraj avoided hiring point-solution specialists, such as dedicated Salesforce Admins. Every RevOps hire is expected to be a first-principles problem solver who can manage multiple systems.
• Managing friction: Dhiraj acknowledges that automation can introduce minor friction, such as requiring an AE to click an "enrich" button in a UI rather than receiving a pre-cleared spreadsheet, but the team continuously works to minimize these clicks.

Deploying Slack-Based AI Agents

Rather than building custom software from scratch, Rocketlane integrated AI agents directly into their existing communication channels to reduce meeting overhead.

• The Slack interface: To avoid the friction of multi-factor authentication and logging into Tableau, Rocketlane built its AI agents to deliver data directly inside Slack.
• The documentation layer: Rocketlane uses Notion to document business definitions. The AI agents reference this documentation to understand context-specific terminology before querying databases.
• Context-aware definitions: The agent is trained to know that a "mid-market" account means a specific sales cycle length to an AE, but refers to an ARR threshold when asked by a CSM.
• Automated hygiene scorecards: Every Monday, a scheduled "CRM hygiene doctor" agent posts a scorecard in Slack, tagging AEs with direct HubSpot links to resolve warnings and missing fields.

First-Principles Tool Buying and RFPs

Dhiraj warns against buying software based on marketing hype or social proof, advocating instead for a rigorous RFP process.

• RFP-driven evaluation: Dhiraj recommends evaluating at least 3 competing tools to build a custom Request for Proposal (RFP) based on actual organizational needs, rather than adopting a vendor's default framework.
• Roadmap verification: When evaluating vendors, Dhiraj analyzes their recent product releases and historical adherence to their product roadmap to gauge their execution velocity.
• Buying frameworks, not features: A great tool vendor should offer industry-best frameworks and thought leadership from their founders, not just a static feature set.

Conclusion

The thread connecting Rocketlane's rapid scale from Series A to Series C is a relentless focus on revenue-producing activities and a cultural commitment to tooling over headcount. By establishing a robust three-layer RevOps framework, investing early in a centralized data warehouse (Snowflake and Tableau), and deploying context-aware Slack agents, Dhiraj Kumar's team has built a highly efficient, self-serve operational engine. For GTM leaders, the primary takeaway is to ruthlessly deprioritize non-revenue-generating metrics and empower a lean team of builders with best-in-class technology.