Kalendar AI, a San Francisco-based startup that’s been building on top of GPT-3‘s language model — developing a SaaS for automating lead generation and sales outreach to make it easier for companies to get initial meetings with prospective customers — has raised $3.2 million in pre-seed funding from 500 Startups; The Lean Startup author, Eric Ries; VC firms Village Global and Metaplanet; and 20+ angel investors (including CEOs of “popular” but undisclosed companies).
“Our AI technology writes personalized invitations to ideal customers with personalized decks — inviting them to take a meeting,” explains founder and CEO Ravi Vadrevu.
The SaaS was launched in February this year, although the startup itself — which is called Kriya Inc — was founded back in 2017 and had been bootstrapping prior to raising this pre-seed.
The idea for the b2b product is to automate the time-consuming and expensive process of sales outreach, including locating and pitching leads, as well as to offer tools to streamline and enhance initial sales meetings.
Kalendar AI claims to have amassed a database of 340M+ “ideal customer profiles” upon which it unleashes its AI sales rep bots to send “personalized” pitches (including “interactive presentations that convert into one-click meetings”) to likely looking customers.
“Our solution brings down the time to initiate a conversation to book an appointment from 7 days to 30 seconds from a sales perspective,” claims Vadrevu, who also argues there are big productivity wins from a marketing perspective vs other channels.
So where is Kalendar AI getting data on all those hundreds of millions of potential customers from?
“We have our own datasets that we built over years from publicly available information,” he tells TechCrunch, specifying that it uses Common Crawl, an open-source repository of the Internet, to collect “publicly available metadata information” and “form an index for searching”.
“We get all the available B2B company domains and the people associated to those domains (this information updates every quarter),” he goes on, adding: “We only engage people on their business email, and don’t store any personal information. We disclaim this in the footer of our every interaction that our technology predicted their business email in real-time using machine learning patterns.”
For “content narratives”, Vadrevu says the AI relies upon “real-time data services” such as the latest company news, company descriptions, current weather/events around a location, industry news, etc.
“We plan to increase such contexts by investing in content R&D,” he further notes.
Asked if it’s using any data scraped from LinkedIn, Vadrevu claims not — arguing: “We don’t need LinkedIn profiles as we process names & predict emails from publicly available domain/company metadata in real-time. LinkedIn is a personal network, and most business interactions usually happen, and more effective on a business email.”
Scraping LinkedIn is “not only unethical but it’s also unnecessary”, he adds when pressed for a confirm that Kalendar AI does not scrape the Microsoft-owned professional social network to enrich its database.
The SaaS is “nearing” its first 100 customers at this stage, per Vadrevu, who says the best markets for the product so far are technology, marketing & advertising, IT & Infrastructure.
“We have customers ranging from a small team of 5 people to public companies like Upwork. However, most of our customers are digital media companies, startups, and new growth entrepreneurs building businesses on top of our platform,” he adds.
“We’ve reached $1M in ARR revenue in the last three months, consistently maintaining a 60% month-over-month growth,” he also tells us, specifying that the new funding will mostly go toward building out the engineering team and improving the product.
“Currently [we’re] focused on forming an engineering growth team responsible for optimizing our algorithms to ensure the engagement is pleasant, effective, and efficient for all of our customers and the recipients.”
Now, it doesn’t require a ghost in the machine to tell you that while AI can be a powerful productivity tool it can also be terribly hit-and-miss.
And even if the claimed “personalization” of sales pitches is spot on target, receiving an automated sales pitching might risk a potential customer feeling, well, roundly unimpressed at the robotic cold call. Or — to put it another way — like they just got spammed. So a pitch-by-robot might be all too easy to ignore.
“It’s a very hard problem and we don’t please everyone yet,” admits Vadrevu. “We have a 2% error rate that sometimes creates unpleasant experiences where we created channels for people to directly give us feedback and let them opt-out of our platform.
“What surprises us by being transparent is that more people are considerate about helping businesses than we thought.”
“If we become successful, the beauty of AI eventually empowers businesses & individuals connecting in many ways beyond sales like how we call connect today,” he adds. “We’re still very early and are grateful for our investors who’re supporting us.”
The startup’s ultimate goal for is the product is to be able to provide a fully end-to-end SaaS for sales outreach and meetings (including videoconferencing and CRM), not just fancy personalized pitches that its AI sales bots fire out at inhuman speeds.
“Our vision is to build an ecosystem around the appointments we set, including video conferencing, CRM, etc., to improve the quality of the meetings,” he says.
“The immediate impact of such technology is on sales (revenue autopilot), which otherwise involves heavy marketing & sales processes to achieve. The adjacent markets for such a technology could also be recruiting, job search, etc., which is not our focus,” Vadrevu also suggests.
The startup’s own sales pitch for its SaaS is that it can radically reduce the time and cost of booking “a high quality meeting that directly impacts revenue“.
But of course the b2b product is certainly not free.
A “basic plan” starts at $2,000/m for 20 “AI authors” (booking an estimated range of 20 meetings per month; since its fee is pay-per-meeting), according to Vadrevu.
While a “Scale plan” starts at $4,000/m for 50 AI authors (booking an estimated range of 40 meetings pm).
Vadrevu argues this pricing puts the product “on the cheaper side” vs other tools companies would be using to try to lock in leads, suggesting b2b companies’ customer acquisition costs for a single meeting range from $150 to $2,000 (i.e. “using Google SEM, digital marketing, sales tools, etc to get the same ROI”).
“We’re making it cheap for our customers by bundling meetings into SaaS,” he further argues. “At scale, we might get into a price per meeting prediction but we saw that SaaS is ideal for our customers.”
On the competitive front, Vadrevu claims Kalendar AI is breaking new ground — and that “no one else is doing what we are doing until now as it’s incredibly hard” — emphasizing that development covers product iterations on content personalization with real-time feedback loops; AI-powered matching that needs to be adequately accurate, safe and not (so) spammy; as well as compliance with variable data regulations — hence why it took the startup three years to push the button on a market launch.
But pressed to name some startup competition, he says that for the personalized outbound messaging component there are AI copywriting tools such as Copy.ai; while for inbound decks he suggests it’s competing with scheduling pages such as Chili Piper, Calendly, and “any other inbound marketing funnel tools”.
Still, Kalendar AI’s wider pitch is that it’s going further by using AI to join up a bunch of manual sales and marketing work.
“Currently for b2b companies, there’s marketing and sales working separately to book appointments. But it takes a lot of manual effort to reach a consistent number of meetings every month on autopilot,” he adds. “Our approach leverages personalized sales outreach, coupled with interactive inbound (decks with one-click meetings; we might patent it) bringing down the cost & time to book a high quality meeting that directly impacts revenue.”
