First deals within 3 months, from scratch…

TL;DR: 

LLMOps SaaS startup Langtail had to shift its go-to-market strategy to sell complex tooling to larger accounts, but the team had no outbound experience. Langtail engaged AlgOps and its partners to develop a tailored outbound strategy based on a single case study. Using AI, AlgOps enriched and forecasted LLMOps dependency among thousands of companies based on various criteria such as existing AI features or the likelihood of building one. After starting from scratch, Langtail closed its first deals within 3 months. 

Challenge | Shifting to Sales-Led Growth in LLMOps

Langtail, an all-in-one toolset for SaaS companies developing and implementing AI features based on LLMs like OpenAI or Claude, was facing a new market landscape. Their previous startup, Avocode, had grown purely via inbound in a different market with a smaller competitive landscape. Yet as Langtail developed high-value tooling for larger businesses, the founding team recognized the need to adapt their go-to-market strategy to compete in the rapidly evolving industry of LLMOps and AI tooling.

Essentially, Langtail shifted from a strategy of product-led growth to one of sales-led growth because  it sells a tool that is tightly integrated into its customers’ core operations. Yet that meant that it had to persuade multiple stakeholders within a given company to commit to a more substantial contract. 

Langtail approached AlgOps with a challenge: make outbound work fast.

Solution | Using AlgOps to Succeed in a Niche with Outbound Sequences Based on a Specific Case Study

Building on the success of a previous customer story with Deepnote, AlgOps prepared a custom target account list for Langtail.

The ICP and account list were generated based on: 

• Confirmation that accounts are SaaS providers
• Identification of AI features such as “Generating code and formulas,” “Generating text and unstructured content,” and “Analyzing structured data”
• Evaluation of the potential for LLM integration with each account’s products and operations
• Assessment of companies’ financial runways based on finance data including funding

This data was integrated into a forecasting algorithm that calculated an ICP score for each company. Then AlgOps enriched relevant companies with key decision-makers' names, LinkedIn profiles, and valid email addresses. After that, AlgOps partner Saleshero provided Langtail’s team with custom sales training to ensure they were equipped to generate and close deals effectively.

Results: Closing Deals Within 3 Months

AlgOps enriched and qualified approximately 3,000 companies segmented by use case to deliver over 500 hyper-relevant accounts for Deepnote’s case study in the first phase. The remaining 2,500 accounts are earmarked for other use cases and case studies.

If Langtail had done this research manually, it would have taken at least 250 hours — virtually two months of work for a full time employee. With AlgOps, Langtail had filled a pipeline with opportunities in that same timeframe, before two months were up.