Jonathan Corbin, is the Founder & CEO of Maven AGI. Previously, because the Global Vice President of Customer Success & Strategy at HubSpot, Jonathan led a team of roughly 1,000 customer success, partner success, and contract managers across multiple regions and verticals. His responsibilities included driving customer retention, revenue growth, and value realization for over 200,000 customers worldwide, starting from startups to enterprises.
Maven AGI is a comprehensive Generative AI native solution designed to rework the client support landscape – without the headache. While in stealth mode, Maven’s technology autonomously resolved over 93% of customer inquiries, cutting support costs by 81%, enhancing the general customer experience, at scale, after resolving hundreds of thousands of interactions in over 50 languages for early customers.
You were previously the worldwide Vice President of Customer Success & Strategy at HubSpot, where you led a team of about 1,000 customer success, partner success, and contract managers across multiple regions and verticals. What were some highlights and key takeaways from this era in your life?
During that time period, Hubspot was one among the five fastest-growing B2B SaaS corporations with over a billion dollars in revenue. There are only a few individuals who have had the chance to construct, grow, and manage at the size that we were operating at. Firms that grow at this speed aren’t normally that size, and firms our size didn’t grow at that speed. I spent a whole lot of time specializing in creating scalable approaches to planning and growth, ensuring that we were setting very clear objectives, aligning incentives across multiple organizations to create the outcomes that we were searching for as a corporation, ensuring we had the systems to create visibility to what was happening within the organization, and planning over multiple horizons. Anything that we rolled out needed to work not only for our current customers but needed to have the power to keep up continuity at exponential growth.
Are you able to share some insights on what inspired you to launch Maven AGI, and the way long you’ve gotten been in stealth mode?
I’ve been obsessive about customer experience since very early on in my profession and that’s why I’ve spent a lot time at industry-leading corporations on this space (Adobe, Marketo, Sprinklr, Hubspot, etc). Back in 2017, I used to be getting back from a West Coast swing, meeting some great customers like Apple and Nike, and we had these incredibly in-depth conversations in regards to the potential to unlock siloed data and create these very personalized experiences right down to the person user level. I’m not talking in regards to the segmented approach of you falling into this age category or demographic. No, that is the power to totally deploy all the knowledge that you’ve gotten shared with us to anticipate customer expectations and proactively engage with them. There was massive excitement from the purchasers however the technology didn’t really exist on the time.
My co-founders – Sami Shalabi, Eugene Mann, and I even have all the time chatted about personalization at scale and the potential that transformers could have because the research first got here out of Google. Sami built one among the most important personalization engines on this planet at Google News (1B+ users) and Eugene led personalization for it so we’ve all the time had deep, insightful conversations about the chances that we could unlock as technology evolved. The applying of this to what we were doing on the time is that I used to be fighting with the ability to create an awesome experience at scale for our Hubspot users, Eugene was how you can productize LLM capabilities at Stripe, and Sami was sharing his insights on what worked well at Google.
After we first heard about what OpenAI was doing and commenced using among the LLMs that had develop into available, we realized that we were at the purpose where the technology now existed for us to create the right customer experience at scale. Firms have had to make a choice from cost efficiencies and good customer experience leading to all types of things like complex segmentation strategies designed to limit customer interactions, creating things which are essentially roadblocks that they called self-serve, or burying your support contact information somewhere that it might’t be found.
We began Maven AGI a few yr ago in stealth mode because what we prioritize at Maven is impact – and once we announced what we were doing we wanted to offer real examples of our impact and metrics, not only that we existed and had raised some money. We’re incredibly grateful for our early customers who believed in us enough to work with us in rolling out cutting-edge technology and pushing the boundaries to develop a greater customer experience.
Are you able to define for us what AGI is within the context of Maven AGI?
AGI is basically well defined from a language perspective – it’s artificial general intelligence. What does that really mean within the business sense? We’re specializing in something that we’re calling business AGI and define it as the power to handle complex tasks using functional AI agents which are specially trained for specific responsibilities with an orchestration layer that enables them to work together.
An example of this is likely to be a checking account user engaging with their bank and asking if their deposit has cleared – what we all know from account history is that they need a small bridge loan to to gap their bills and check cashing. Maven will understand the historical context and offer the loan while handling all the paperwork that is likely to be related to it comparable to background checks, credit checks, filling in loan paperwork, understanding the risks, approval, and a certain amount that falls throughout the risk profile, approving the loan, and moving the cash to the person’s account.
One other example can be someone going to their CRM support team and asking how you can deploy a campaign. What we might understand from that is that they don’t need to know how you can create a campaign, but they need a certain variety of leads by a certain date. Users would have the power to say, “Give me 100 leads next month” and Maven would undergo the incredibly complex task of delivering those.
What are among the biggest problems with how AI has historically been integrated in customer support?
Historically, AI in customer support used machine learning models that were highly deterministic and took months to coach. These models worked on a basic if-then logic: if a user selected X, they’d be given the Y option. This simplistic approach fell wanting expectations, leading to disappointing outcomes and leaving many CX professionals skeptical of AI’s potential. True success in AI-driven customer support hinges on dynamic personalization, the power to reason, and take meaningful actions.
What are the important thing steps involved in training Maven AGI to handle customer support inquiries?
It’s really easy. . . just give us access to any information that you just would use to coach humans on. We will have it up and running for you with a high degree of accuracy inside days– not weeks or months. It would use your specific tone of voice, vernacular, and whatever emojis you would like.
How does Maven AGI assist in reducing customer support costs and improving overall customer satisfaction?
Firms deploy Maven AGI in a wide range of different fashions but the perfect approach to have the fastest impact is to insert Maven at the top of your support queue on the endpoints or channels that your customers need to use (chat, web, search, Slack, in product, SMS, etc). That permits us to supply fast, personalized results + actions to customers with no wait time while ensuring that those amazing support agents are doing what they do best, working with customers who really want human interactions to resolve their problems.
What technological advancements have enabled Maven AGI to attain such high rates of autonomous issue resolution?
I consider we’ve got recruited among the best engineering teams on this planet to resolve that comes down to an information problem. Good folks who’ve worked on challenges like search at Google, and personalization at scale at Meta and Amazon, and have been excited about solving these styles of problems for years. Data is fragmented and siloed, and to ensure that us to reply customers’ questions and take actions we would have liked to have the option to ingest more data than anyone else. The second part is the power to take actions and construct our motion engine because we all know that just answering questions isn’t enough. To ensure that us to attain business AGI we’d like to have the option to anticipate users’ needs and have interaction them with intention.
Are you able to provide more details in regards to the recent $20M Series A funding and the way it can be utilized?
We were fortunate to be hitting on all cylinders in what we wanted to attain with our seed round: construct an awesome engineering team, a product that solves real problems, and have customers who were getting value out of our product. We raised our seed round lower than a yr ago but had some really great investors who desired to be a part of the journey with us. After spending time with M13 we were really excited to proceed to construct the long run of Maven AGI along with them. The $28M that we’ve raised over the past yr shall be used to construct out our GTM team, spend money on constructing out the partner ecosystem, and proceed to rent engineers as we expand our motion engine (™) and platform capabilities.
How do you see the role of AI evolving in the client support industry over the subsequent five years?
The long run won’t be divided into support, services, sales, and various functions. As an alternative, customer support will develop into a part of a seamless, unified customer experience without messy handoffs and siloed data. As customer expectations evolve, so will the ways we serve them.
Today’s customers needs fall into 3 categories:
- Those that need to self-serve – the power to seek out the answer or answer to an issue.
- Those that want access to self-service but need validation that they are taking the proper motion.
- Customers who demand white glove service and want human assistance.
The long run also has 3 categories but expectations from customers shall be far different:
- Expecting fast answers to their questions.
- Anticipate their needs and questions with personalisation, usage data, full historic context, and the power to take motion and have interaction with them on the channel of their selecting.
- The power to interact with customer support agents without wait times and lengthy lines, who’ve answers available to their questions, full historic context, and the power to immediately take actions.
