In a time when artificial intelligence is beginning to revolutionize various industries and markets, not every company claiming to utilize machine learning and AI is created equal. The companies that can genuinely grasp and leverage these innovative tools to enhance their products or services will distinguish themselves from those merely tacking on the label ‘AI’ to conventional tools. A key aspect of this emerging distinction lies in defining and comprehending the problem space to effectively implement these advanced tools.
Zayd Ali, the young 21-year-old founder of Valley, dedicates considerable time pondering these issues. Valley, a rapidly expanding startup in the sales development sector, aims to automate the $62 billion-a-year appointment setting market in a manner previously unattainable. The company is pioneering the use of exclusive generative AI models and algorithms to revolutionize business-to-business sales interactions.
Despite his youth, Ali is no stranger to entrepreneurship, having founded, grown, and sold two prior companies in the sales industry. His most recent exit was Advisor Appointments, which was acquired through a private equity sale. Drawing from his past experiences, Ali is leveraging his knowledge to propel his current venture forward.
The Innovative Use of Generative AI by Valley
Zayd Ali’s journey towards innovating sales technology commenced as a non-technical founder, a title often laden with challenges, especially in the tech-centric landscape of Silicon Valley. However, Ali’s past triumphs equipped him with a wealth of industry-specific insights. This expertise served as the foundation for Valley’s strategy in disrupting the sales automation sector. By identifying and structuring an industry-specific open problem that traditional machine learning and AI methods could not solve independently, Ali laid the groundwork for Valley’s success.
At its essence, Valley harnesses cutting-edge generative AI and large language models to automate and streamline the laborious processes involved in identifying, nurturing, and scheduling business-to-business appointments. The goal was to create a system capable of emulating sales development representatives across diverse industries, a technically demanding objective that pushed the boundaries of existing AI technology.
Generative AI encompasses algorithms that learn from datasets and generate new, similar data points. Depending on the training and application, it proves to be a potent tool for machine inference, bridging connections between data points meaningfully. Large language models, such as ChatGPT, fall within the realm of generative AI, focusing on comprehending and generating human language and dialogue interactions based on extensive datasets.
Within Valley’s context, this entailed crafting responses and interactions mirroring those of a human orchestrating appointment setups. While a formidable challenge, especially in providing contextual industry expertise, large language models were deemed well-suited for the task.
Prioritizing Quality in Developing an Industry-Specific Generative AI Tool
Valley adopted a distinctive approach by creating a proprietary model — the Valley Reinforced Learning from Sales Feedback model. Unlike the prevalent belief that ‘bigger is better’ in data modeling, Valley placed its bet on specificity and precision. The model underwent training not on the entire spectrum of sales interactions but on the most successful ones, refining its ability to mimic accomplished sales representatives. This iterative feedback loop mechanism ensured continuous learning and enhancement of the model’s performance.
The Valley team is actively engaged in a significant area of research concerning large language models and generative AI. The primary challenge with extensive models like those supporting ChatGPT lies in their immense size, comprising billions of parameters that necessitate costly training and maintenance. To address this, ongoing research in academia and industry focuses on developing more compact networks that can deliver comparable or superior performance as data quality and specificity improve.
Valley’s strategic approach to differentiation revolves around compounding data, whereby the system grows more intelligent, efficient, and valuable with each interaction. Consequently, users encounter a high switching cost as the system evolves to better understand and cater to their distinct requirements, discouraging them from seeking alternatives.
Harnessing Industry Data and Experience for Success
Developing such a sophisticated system demanded surmounting significant obstacles, spanning algorithm design to data acquisition and processing.
The team, comprising individuals from Samsung AI, Columbia University, Salesforce, Yext, and other renowned entities, initiated the process by building upon the application layer of existing generative AI frameworks. This layer customizes the model’s broad capabilities to suit specific tasks, such as appointment setting. The workflow creation involved structuring sequences of interactions likely to culminate in successful appointments, akin to the approach of a seasoned sales representative.
In contrast to other startups rushing to amass extensive datasets for generative AI, Valley opted for quality over quantity. Their focus lay in curating a high-caliber dataset with accurately labeled data points relevant to the appointment-setting task. This data specificity empowered their model to engage in high-fidelity interactions with promising leads, tailored to the nuances and requisites of diverse industries.
Reinforcement learning played a pivotal role in this endeavor, enabling the model to evolve continually based on feedback from sales outcomes. This strategy ensures that the AI’s responses undergo constant refinement for enhanced effectiveness in real-world sales scenarios.
Addressing Industry Challenges with a ‘Quality Over Quantity’ Approach
The sales technology sector presents a myriad of challenges, from exorbitant customer acquisition costs to the imperative of scalable, predictable revenue. Valley endeavors to tackle these challenges head-on by substantially reducing the cost associated with acquiring new appointments and, consequently, the cost per conversion. This optimization offers businesses newfound efficiency and scalability unattainable through traditional methods.
Ali elucidated: “There are only three options for companies that wanted to set appointments with prospective buyers – and they were all sub-optimal. One is founder-led prospecting where if you have a really early stage company you can spend two to three hours a day trying to get in touch with the prospective buyers – a huge waste of time. The second option is hiring an appointment sending agency for \(3000 to \)4000 a month – very expensive and many of them rarely do their jobs well. And the final option is if you have the capital you could build a full sales development team which when scaled quickly becomes a 7 figure per year investment.
So the experiment with Valley was could we turn that \(85,000 a year sales development representative salary or that \)4,000 a month agency budget into a $400 a month software expense. Build a product that could take a cold stranger and turn them into a book sales meeting with zero human involvement, and by doing so dramatically change the customer acquisition calculus that our customers were performing. And dramatically alter the allocation of time towards prospecting versus other critical areas of an early stage company.”
While Ali chose not to disclose revenue figures, he highlighted that since the pilot program’s inception in March of this year, Valley has sustained a robust 30% month-over-month growth rate and has secured seven figures in signed expansion letters of intent from existing customers.
As they continue to expand, some of Valley’s prominent clients include Darwinian Ventures, Front.com, and Masterworks. The recent announcement of a $2 million pre-seed funding round from investors like Antler, Jason Calacanis, Rough Draft Ventures, O’Shaughnessy Ventures, ID8 Investments, Transform VC, and John Pleasants, the former CEO of Ticketmaster, Match, and Evite, underscores the industry’s confidence in Valley’s innovative approach.
