neptune.ai

Neptune.ai is the experiment tracker for foundation models, trusted by teams at OpenAI to monitor and debug GPT-scale training. The company partnered with Backpack AI to establish their enterprise sales motion. Their target audience consisted of highly technical AI researchers and ML engineers at large enterprises, a group notoriously difficult to engage with traditional sales tactics. After a year of collaboration, Neptune.ai decided on a strategic pivot to focus exclusively on the high-value foundation model market. This required a new approach, moving from broad outreach to deep market intelligence and highly targeted engagement.

The Backpack AI Solution

Backpack AI’s engagement evolved from building a sales pipeline to providing the strategic intelligence needed to conquer a new market category.

Phase 1: Building the Outbound Foundation

The initial engagement focused on building a repeatable engine to generate a consistent pipeline of enterprise meetings. The strategy was built on a deep understanding of the MLOps landscape.

• Targeting Competitive Gaps: We analyzed the MLOps stack of target accounts, identifying those using open-source solutions that struggle with enterprise scale.
• Signal-Based Outreach: We tracked critical buying signals, such as the hiring of new AI executives, to time engagement for maximum impact.
• Event-Driven Pipeline: We supported the Neptune.ai team before and after major industry events, filling their calendars with qualified meetings to maximize ROI.

Phase 2: Guiding the Strategic Pivot with Market Intelligence

As Neptune.ai pivoted, our role transitioned from direct outreach to providing the intelligence needed to navigate the nascent foundation model ecosystem.

• Deep Market Intelligence & TAM Mapping: We built a detailed map of the AI development landscape, analyzing the specific models being built, their scale (parameter count), and the underlying infrastructure (GPU usage).
• Buying & Talent Signals We monitored the market for critical growth and intent triggers. We tracked the movement of top-tier talent from major AI labs like OpenAI to target accounts, flagging key new hires. We also identified “greenfield” projects where new teams were being formed to build models from scratch, indicating an immediate need for an experiment tracking solution before a tech stack was locked in.