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Project essence
Core Functionality: Firebender (https://firebender.com/) is a platform that helps B2B product developers find companies that are likely to become their customers.
Technology: The platform utilizes an AI engine that analyzes over 100 information sources to compile a list of the most probable buyers for a specific product from millions of potential companies.
On the platform, you can find companies based on various criteria, like "startups developing AI products with pre-seed or seed funding," "customer support managers at companies with a support phone number listed on their website," "companies with 10+ employees selling their own branded food products through retail stores," "startups providing medical services and using electronic medical records," or "logistics companies with IT systems that handle customs clearance."
The problem is, most address databases only let you find companies based on very formal criteria like industry and employee count. This forces B2B salespeople to spend extra time digging up additional information about these companies to identify the most likely buyers for their products – scouring company websites, LinkedIn and Facebook posts, job listings, and other sources.
Firebender does this long and tedious work for salespeople. Their AI engine proactively scours numerous sources for any additional information about companies, gathers this information into a unified database, regularly updates it, and allows you to search this database using natural language queries, which can then be refined with more formal filters.
One of the startup's clients managed to find "thousands" of companies selling their goods and services to private customers, who have a FAQ section on their website and more than 10 employees in their customer support department, all thanks to the platform.
Additionally, the client even received a list of decision-makers in those companies on the platform, complete with links to their LinkedIn profiles and email addresses.
Standard pricing for using the platform is $199 or $599 per month, depending on the number of database queries. However, the size of the initial database in this case is "only" 100,000 companies. Searching among the promised "millions" of companies is available only on the enterprise plan, for which pricing requires contacting the startup directly.
Firebender was founded last year and recently graduated from Y Combinator, receiving the customary $500,000 investment.
What's interesting?
The use of AI for compiling lists of companies based on various criteria and for different purposes has proven surprisingly relevant and in demand. In this Y Combinator batch alone, there were at least 3 startups focused on this theme.
Here's who else was there besides today's Firebender:
Openmart (https://www.openmart.ai/) helps manufacturers and national distributors find local resellers who might be interested in reselling their products. These resellers can be stores, beauty salons, dentists, hotels, restaurants, cafes, bars, tour agencies, museums, regional distributors, and marketplaces.
OffDeal (https://offdeal.io/) helps national companies and private equity funds compile lists of local companies that could be interesting acquisition targets.
Therefore, the use of AI to create lists of companies based on a large set of "informal" criteria, which require scouring and combining multiple sources to obtain information, is a hot topic right now. Otherwise, Y Combinator wouldn't have selected so many startups in this space for their accelerator.
The key to the success of such startups lies in choosing the right informal criteria for searching for companies.
That said, it seemed to me that today's Firebender formulated a very interesting criterion in their main offer on the first screen of their website – but one that they didn't really develop further in the subsequent description of their product.
They proposed that salespeople find not just potential buyers, but "early adopters" of the products and technologies being sold.
This term comes from the well-known theory of the sequence of new product penetration into the market.
First, a small number of "innovators" start using the product, those who actively love trying out everything new.
Then, penetration stalls briefly, after which a larger number of "early adopters" begin using the product – those who are willing to try something new, but only after it has been tested and proven by others.
Then comes a rather long "chasm" that not all products can cross. The product either disappears in this chasm, or after some time, the "early majority" starts using it – and the product begins to grow rapidly and significantly.
Then, the more conservative representatives of the "late majority" start using the product.
Followed by the most conservative "laggards."
In other words, the demand for a product depends not only on the problem it solves and its features, but also on the stage of the product itself! Because most people are fundamentally not ready to use new products – simply because they are new.
Therefore, the most critical audience for new products is the early adopters:
First, they are willing to buy new products.
Second, there are significantly more of them than the truly risky innovators.
Third, the number of early adopters acquired determines whether the product can survive on its accumulated user base until the "chasm" ends – that is, until the product becomes "old" or "proven" enough to attract the attention of the early majority.
The problem is, the behavior of early adopters differs from that of innovators. Innovators are enthusiasts who actively monitor their area of interest and seek out new products themselves. Early adopters, on the other hand, are not such "enthusiasts in perpetual search" 😉. They are open to trying new things, but you need to reach out to them and showcase what you have to offer.
Therefore, you need to actively seek out early adopters. However, this can be quite challenging and expensive:
it's a completely informal criterion that cannot be specified in typical advertising targeting.
such adopters are a minority. So, by advertising based on formal criteria, we will mainly reach those who are not yet ready to buy our product at an early stage. This will negatively impact conversion rates and customer acquisition costs.
Therefore, the task formulated by today's startup – finding people and companies who could become early adopters of the product being sold – is very important and interesting. However, they haven't explicitly formulated a solution to this problem, at least not yet.
Where to Run
The general direction is to follow Y Combinator's clearly stated interest 😉 That means creating platforms that use AI to find potential buyers (resellers, partners) based on a set of informal parameters relevant to a specific field. It seems that we shouldn't limit ourselves to B2B sales, as similar approaches can work in B2C as well.
Here's what to explore:
In which areas do sellers spend a lot of time and effort searching for potential buyers (resellers, partners)?
What informal criteria do they use to qualify leads?
What additional information sources do they use for this purpose?
What other information sources could be used to draw similar conclusions and assumptions?
How can this be automated?
A narrower focus would be to create platforms and tools for finding not just potential buyers, but early adopters.
I think the concept of "early adopters" overlaps nicely with the concept of "superconsumers." Here's a surprising fact: in every product category, 10% of buyers generate 30-70% of all revenue in that category. They buy more often and spend more than the rest, earning them the title of "superconsumers."
The interesting part is that superconsumers in one category are usually superconsumers in 9 other categories! And it doesn't have to be related categories – it can be anything. This is because it's not necessarily a passion for something specific that makes people superconsumers, but rather their general character and financial means.
This means we can spot superconsumer behavior in one category... and make a pretty good guess that we can "super-sell" them something from a completely different category – simply because that's the kind of person they are.
Similarly, if we find that someone is an early adopter of a particular product, we can assume with a fair degree of confidence that they may become an early adopter of a product from another category – simply because they are, by nature, not afraid to try new things.
Given that in every company, decisions are initiated and/or made by specific people, the same can be true for B2B sales.
Roughly speaking, if the owner of a small company recently bought a product from a startup, we can offer him another product from a different area from another startup – because the lack of widespread adoption of the product is not a fundamental obstacle to purchase for him. Or, if the director of customer support at a large company regularly buys new and unusual phones and cameras, our startup has a chance to sell him a new and unknown program for automating customer support 😉
Even if we don't dig specifically in this narrow direction, it's a cool illustration of how choosing unexpected informal criteria can become the basis for an interesting product within today's theme.
So, what set of informal criteria and in what areas can you suggest for your own interesting product? 😉
About Company
Firebender
Website: firebender.com
Last Round: $500K, December 1, 2023
Total Investments: $500K, Rounds: 1
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