Startup Pitches! LAUNCH Accelerator 32 Public Demo Day

Investor Introductions (0s)

• Welcome to the Public Demo Day for LAUNCH Accelerator Cohort 32, where seven founders will pitch their startups to a panel of guest investor judges and Syndicate members, with the format consisting of a three-minute pitch, followed by a Q&A session with two minutes for the founders to answer questions (14s).
• The guest investor judges are Gary Benov from M Ventures, Katie Stanton from Moxy Ventures, Sandy Cass from Red Swan Ventures, and Stu from Cough Drop Capital, who will introduce themselves, share their firm's name, average check size, and thesis (1m13s).
• Gary Benov from M Ventures is a Commerce enablement fund that invests in anything that removes friction from a transaction, with an average check size of $300K (1m22s).
• Katie Stanton from Moxy Ventures is a generalist early-stage Venture fund with a median check size of $1.5 million (1m41s).
• Sandy Cass from Red Swan Ventures invests in consumer-driven businesses with an average check size of $650K, focusing on pre-seed and seed-stage companies (1m55s).
• Stu from Cough Drop Capital is a pre-seed fund that invests in B2B software companies with check sizes ranging from $25K to $100K (2m18s).
• The event is also attended by Syndicate members, who will vote for their top pick in a poll, and can ask questions through the Q&A box at the bottom of the screen (52s).
• The event is live-streamed on YouTube, and the URL for the LAUNCH Accelerator is shared in the chat for those who want to tune in and help founders (2m43s).

Linda (Mastertech) (3m5s)

• MasterTech AI is a company that uses AI to make auto repair faster, easier, and safer for shops and DIYers by aggregating thousands of service documents for specific vehicle models, with the goal of reducing misdiagnosis and incorrect repairs in the industry (3m13s).
• The company's AI co-pilot helps technicians like Will, who works at Davis Repair, to easily root cause issues by entering symptoms and answering follow-up questions to narrow down the root cause (4m4s).
• MasterTech AI provides step-by-step factory procedures based on the technician's level of experience and helps shops discover and convert additional services, with an average of $600 worth of services per vehicle (4m33s).
• The company has established industry partnerships, including with Carfax for vehicle service history data, and has data licensing agreements for over 90 vehicle makes (4m54s).
• MasterTech AI launched its B2B product in May and has 37 shops on active monthly subscription, as well as a contract with an automotive technical school (5m4s).
• The company's product roadmap includes enhancing OEM data with user-submitted content, leaning into voice and camera-based assistance, and expanding into adjacent verticals like the HVAC industry (5m32s).
• MasterTech AI's team consists of experienced individuals, including the founder with 17 years of engineering experience leading teams at Microsoft and Niantic, and two team members with a combined 32 years of experience in the auto repair industry (5m57s).
• The company aims to become the go-to platform for automotive repair, with a goal of reaching 10 million ARR by partnering with under 2,800 shops, and 100 million ARR by partnering with under 28,000 shops (5m27s).
• The data set being used includes service data for specific vehicles, which is not publicly available and requires licensing deals with OEMs through a data aggregator provider. There is a high barrier to entry due to licensing costs and data compliance requirements. Most necessary approvals have been obtained, with only two OEM data sets still pending. (6m47s)
• Additional data integration involves shop management systems, allowing access to shop history records, customer records, vehicle history, and Carfax data. A proprietary user content submission pipeline is also being developed to enhance the platform. (7m55s)
• The target market is independent auto repair shops, with a focus on small to medium chains. The company plans to start mid-market and then move to larger enterprise chains with thousands of locations. Pilots with some larger chains are already underway. (8m16s)

Rami (Querio) (9m1s)

• Ramy, the co-founder and COO of Quero, presented the company's data intelligence solution, which aims to bridge the gap between business and data teams by providing useful insights from data at any technical level (9m20s).
• The current process of getting insights is expensive and slow, with business people lacking data skills and data people lacking business context, resulting in a "telephone issue" that wastes most of the time spent on getting insights (9m30s).
• Quero's solution is demonstrated through a customer example, Enver, CTO of a B2B SaaS company called Growash, who was spending $150,000 on data solutions and was about to hire two data analysts before switching to Quero, which saved them $150,000 a year (9m55s).
• Quero's platform allows non-technical users to ask questions and create charts, while also providing a co-pilot feature to help technical users write Python code and produce visualizations (10m23s).
• The company has gained traction, signing $880,000 in revenue since February, with half of the contracts converting from pilot to paid periods, and growing at an average of 20% month-over-month (11m32s).
• Quero is initially targeting CTOs at SaaS, logistics, and e-commerce scale-ups, but plans to expand to any company that needs internet to run (11m21s).
• The founding team consists of Ramy, who spent five years at Amazon in engineering and product roles, and his co-founder, who has a background in technical and business roles and has exited two startups (11m43s).
• The discussion addresses the scalability of data tools, highlighting that Looker is suitable for companies needing a SQL querying tool with dashboarding and a basic data model, but may not meet all needs as companies grow larger and more complex. (12m31s)
• Looker differentiates itself by providing a Python environment, allowing data teams to perform cloud-based Python operations, which is not offered by all competitors. (13m8s)
• The approach to preventing data hallucinations involves building a custom data model optimized for AI, ensuring transparency and allowing users to audit queries. This model is similar to DBT or LookML but tailored to provide AI with the necessary context for accurate queries. (13m44s)
• The accelerator program has seen a significant increase in applications, allowing for selective recruitment of strong technical teams with high product velocity. The traction and progress of these early-stage companies, particularly in AI, are impressive, with rapid development and funding. (14m47s)

Marc (ConvertMate) (15m22s)

• Mark, the co-founder of Convermat, presents a solution for making e-commerce SEO easy, addressing the issue of rising acquisition costs that reduce profits for retailers relying on paid ads. (15m29s)
• Convermat automates manual SEO tasks for e-commerce companies, starting with keyword research using Google Search Console integration to target keywords that drive traffic. (16m2s)
• The platform integrates keywords into product and collection copy, allowing teams to review changes monthly, and creates blog articles with internal linking to boost search rankings. (16m27s)
• A case study of Gary, an owner of a costume store in Australia, shows that Convermat helped triple his organic traffic and increase high-profit sales by optimizing his product listings. (16m54s)
• Convermat plans to expand its services to other channels like Google Merchant Center and TikTok, aiming to enrich product data for better ranking at lower costs. (17m17s)
• The company operates as a B2B SaaS with three pricing plans, experiencing growth by doubling prices and increasing monthly recurring revenue by 39% to $12.7k in October. (17m37s)
• Convermat's go-to-market strategy includes SEO-driven content marketing, partnerships through white-label offers and referral schemes, and outbound sales. (17m51s)
• The broader vision is to target the product information market, differentiating by using AI to enrich product data for high ranking across distribution channels, with continuous product improvement. (18m2s)
• The team includes Mark, with eight years in e-commerce SaaS enterprise sales, and Boris, who previously created an e-commerce platform with hundreds of retailers. (18m17s)
• The product aims to become an ongoing need for clients through three ways: continuously adapting products to match user intent and search requests, helping clients with ongoing product additions, and improving the topical authority of the store to rank higher overall (19m34s).
• The company differentiates itself from other Shopify apps or SEO tools through transparency, providing users with information on why changes are made and how they will help, using data from sources like Google Merchant Center and third-party SEO data (20m34s).
• The product information market is targeted because it is considered broken, and the company aims to disrupt it like Zapier did with integration, helping clients adapt to new shopping channels (21m6s).
• The company's goal is to make its product a platform, not just a tool, by expanding its features and making it sticky and impossible for clients to unsubscribe (22m3s).
• The company works with its founders to develop a wedge strategy, finding a beachhead and then expanding the product to provide more value to clients (21m53s).
• The product's features page shows its potential for continuous product optimization anywhere, which is particularly useful for companies that need to optimize their products across multiple platforms like Amazon, Shopify, and TikTok (22m11s).
• The company's specialty is taking great toolkits and making them more sticky and impossible for clients to unsubscribe from (22m41s).

JP (Ellis) (22m58s)

• Alexander, co-founder and CEO of Ellis, presents the sales platform for startups, showcasing how it simplifies the process of setting up cold email campaigns, (22m58s).
• Ellis helps users register domains, set up email marketing, and connect inboxes, replacing multiple point solutions with a single platform, (23m56s).
• The platform also assists in writing personalized and engaging emails using AI hyper-personalization, pulling data from integrated CRM and private data sources, (24m50s).
• Ellis claims a 5x better conversion rate for its customers, with less than one in a thousand emails going to spam, (25m8s).
• Suuk, founder and CTO of Weave, shares his experience using Ellis, which helped him land a five-figure customer in his first month, (25m14s).
• Ellis is building a CRM that handles inbound, outbound, and CRM operations, with plans to aggressively move upmarket, (25m22s).
• The platform currently charges $500 and $2,000 a month, with 23 startups in an Enterprise pilot and 60% month-over-month growth in MMR, (25m30s).
• Ellis aims to reach $1 million ARR and sustain its 60% month-over-month growth to reach $10 million ARR, (26m5s).
• The company's competition includes point-to-point solutions, Enterprise platforms, and AI BDRs, but Ellis claims to have an advantage with its CRM data and personalization capabilities, (25m57s).
• The company is targeting Enterprise customers and scale-ups, aiming to reach $100 million in annual recurring revenue (ARR) by winning less than 1% of the annual CRM spend (26m11s).
• The founders are two ex-Uber engineers who want to help startups get customers, and they are open to answering questions (26m20s).
• The ideal customer profile (ICP) has shifted from startups and startup founders to targeting scale-ups, typically with a head of Business Development or a chief marketing officer (27m20s).
• These ICPs are frustrated with the setup requirements and costs of Salesforce, and the company offers a solution that can be set up quickly and automate data entry, outbound, and inbound marketing (27m41s).
• The company's strategy is to offer a better, faster, and cheaper solution than Salesforce, making it an easy sale for their ICP (28m3s).
• The company started as an email marketing platform but has shifted its focus to a data mode, where customer data flows into their platform, providing staying power and allowing teams to get used to their automations (28m10s).
• The data mode is the key to the company's strategy, providing a way to keep customers and expand to other industries (28m36s).

Chef Reactions (28m48s)

• Chef Reactions, a former chef turned content creator, has grown an audience of 6 million people across all platforms with over 10 billion views, and is now launching Back of House Spice Company, a kitchen culture-based line of premium spice blends with bold, authentic flavors. (28m54s)
• The company's spice blends are made by chefs, high in quality, freshly ground, non-GMO, gluten-free, vegan, and kosher, and will be packaged in eco-friendly milk cartons with a bold color palette and playful artwork. (30m39s)
• Back of House Spice Company will launch with three blends, a finishing salt, and a cracked black pepper blend, and will collaborate with a network of food creators for content and send out influencer packs prior to launch. (31m6s)
• The company plans to start with direct-to-consumer sales and partner with a centrally located co-packer for quality control and efficient fulfillment, with the goal of eventually moving into retail. (31m27s)
• Chef Reactions has built an audience for distribution, with a 1% conversion of his community potentially reaching 60,000 units organically and theoretically without any ad spend. (31m37s)
• The company's founder, Chef Reactions, is joined by his marketing partner, Mary, and they look forward to questions and feedback on their new venture. (31m51s)
• The product's long shelf life could be a double-edged sword, as it may prevent customers from purchasing more frequently, but the company plans to encourage usage through content, competitions, and contests to drive sales (32m12s).
• The staple products, such as salt and pepper blends, are expected to be constantly renewed items that people use in every recipe (32m55s).
• The company plans to use its brand to stand out in the market, similar to Liquid Death, and build a sense of community through direct-to-consumer sales initially, with potential expansion into retail, wholesale, and restaurant supply (33m21s).
• The company has a theory that most people cannot make consumer packaged goods (CPG) work, but those who can may build a billion-dollar business (34m2s).
• The company is partnered with MrBeast, Science, Peter Fam, and Mike Jones, and has learned from Chef's authenticity and ability to build a massive following (34m11s).
• Chef's influence and authenticity have taken the challenge of acquiring the first 10,000 to 100,000 customers out of the picture, and the company sees potential in partnering with him (34m35s).
• Chef has built relationships with high-end brands, such as Thermador, and has a strong following, with fans like French Laundry, who created a custom menu for him (34m46s).
• The company values authentic connections over influence, and has learned from Chef's operating system and rule set for creating content (35m47s).
• Chef's content creation approach is elegantly simple yet complex, and the company sees value in his thoughtful approach to building a massive following (36m4s).
• Chef is considered a key figure in his field, with a unique authenticity that has been emulated by others, including Gordon Ramsay, who allegedly stole his format (36m12s).
• The concept of influence is closely tied to the idea of community, with Chef having cultivated a community of people rather than just followers or subscribers (37m7s).
• Chef's content is entertaining and educational, with a format that combines laughter and learning, making it addictive and engaging (37m33s).
• The architecture of All In and This Week in Startups has always been "laugh and learn," and Chef's content follows a similar concept (37m32s).
• Chef's subtle tips and knowledge, such as the "wet hand dry hand" technique for making bread cutlets, are embedded in his content and make it valuable (37m38s).
• Follow-through Partnerships and Found University are two parts of the funnel that have given a significant advantage, with Chef being a part of it (38m2s).
• A $25,000 investment was made in Chef's company to help get it started, as many people are afraid to write the first check in the industry (38m21s).
• Chef made great progress in the first year, doubling or tripling influencer revenue, and then received a $125,000 investment and worked with the program for another 14 weeks (38m41s).
• The program, Found University, gets people off the bench and helps two or three person teams, with 250 teams receiving a $25,000 investment, and then some teams receive a $125,000 check if they want to join the accelerator (39m0s).
• The goal is to help these companies get ready for a Series A round, with the help of investors making $250,000 to $500,000 checks (39m20s).

Vinay (Layerpath) (39m31s)

• V, the founder and CEO of Lpath, presents his company's solution for creating AI-powered training and onboarding content in minutes, not weeks, citing his over a decade of experience in Enterprise SaaS and the challenges of effectively communicating product value to employees and users (39m37s).
• Lpath's platform allows users to create content quickly, as demonstrated by Daniel, a customer success manager at Turbox, who uses Lpath to create a training content for HubSpot's Privacy feature in under 30 seconds (40m20s).
• The platform automatically captures the user's intent and actions, producing content in three different formats: tour guide, video, and more, with the option to edit using AI and translate into 15 different languages (40m40s).
• Lpath has been transformative for Daniel, cutting down his content creation time by 80% while still providing multiformat content (41m15s).
• Since its launch in February 2024, Lpath has grown to 5,000+ signups, 1,300 paid beta users, and 20 teams on subscription, with mid-market customers adopting the product in the last two months (41m24s).
• Lpath is self-serve and product-led growth (PLG) for small and medium-sized businesses (SMBs) and startups, but plans to focus on Enterprises with multi-year contracts starting at $5,000 a year (41m47s).
• Lpath distinguishes itself from the crowded market with its multimodel content creation, which is its technical wedge, and the growing demand for the market, as seen in the recent acquisition of WalkMe by SAP in June 2024 (41m59s).
• Lpath's goal is to reach $100 million by starting with video-crafted training and onboarding, then redefining knowledge bases and learning management systems, and finally entering the digital adoption market (42m25s).
• To achieve this goal, Lpath is focusing on product-led growth and channel partnerships, leveraging the founding team's experience in creating technical documentation and designing videos (42m41s).
• The company is operating in a new category that requires educational-driven marketing to create demand, as there was no direct demand four years ago, and they need to latch into existing categories (44m0s).
• The company has been perfecting its inbound content marketing over the last six months, focusing on how people are searching and including alternative demand to show that the solution exists (44m12s).
• The company is moving forward with a hybrid model of inbound and outbound sales, which was learned from securing two mid-market deals that came through their content and had a sales cycle of less than 48 hours (44m35s).
• The company is exploring Channel Partnerships, including white label partnerships, with one partner already doing this for commercial real estate, and they have a targeted revenue of $10,000 in two months through this partnership (45m4s).
• The company's average contract value (ACV) is $5,000, which makes it tough to incentivize outbound sales, but they are finding ways to make it work through their hybrid sales model (43m31s).
• The company is creating a must-have solution by showing people that the solution exists and educating them on its value, and they are using their content marketing to drive this effort (44m8s).

Masoud (Rapha) (45m32s)

• Massud, one of the founders of Rafa, presents their startup, which aims to disrupt LinkedIn's dominance in the recruiting space by providing a contextual recruiting platform that understands context, captures culture fit, and hard skills from applicants without conducting a phone screen (45m38s).
• The platform was tested by Tes, the founder of Dementia Dev, who received over a thousand applications for two job postings and struggled with the qualifying problem, but after using Rafa, he was able to decrease spam applications by 98%, increase the pass rate by 50%, and save 60 hours a week (46m5s).
• Rafa's platform allows job creators to add burning questions and technical behavioral candidate discovers, enabling applicants to showcase their merits beyond their LinkedIn profiles and resumes (46m24s).
• The platform also facilitates team consensus-building by allowing team members to review and listen to transcripts and summaries, making hiring a more efficient and effective process (46m41s).
• Rafa has launched its public beta, onboarded Fortune 10 and Fortune 50 Enterprise companies, and is growing 35% month-over-month, with over 6,000 MRR and 200 companies on trial (47m11s).
• The company's initial go-to-market motion has been cold outbound and founder-led marketing, with a pricing model of $200 per month and $10 per seat for startups and agencies, and $2,000 per month per seat for Enterprise companies (47m35s).
• Rafa's founders have a combined experience of over 40 years in building products, with Massud being a design lead at Meta and his co-founder having worked at Cloud Kitchen, Salesforce, Yahoo, and Amazon (48m8s).
• The company differentiates itself from LinkedIn by being a three-dimensional company that provides more results at a lower cost, with customers paying a fraction of the cost of a LinkedIn recruiter seat (47m58s).
• The startup has been found by potential clients through both inbound and outbound channels, as well as through social media platforms such as LinkedIn and Twitter, where the founder is always actively promoting the business (49m2s).
• The recruiting space has seen an increase in AI-powered recruiters, but this has led to a more transactional process that often neglects the human element, which is a key aspect of the hiring process (49m15s).
• The startup aims to differentiate itself by focusing on candidate experience, allowing candidates to share their stories and sentiments, which has been well-received by both candidates and companies (49m45s).
• Companies are attracted to the platform because it allows them to showcase their brand and values through the recruiting process, which is seen as a reflection of how they manage their company (50m13s).
• The startup creates its own dataset, which is an advantage over other AI recruiters that rely on web crawlers, web scrapers, and outdated resumes from job boards (50m29s).
• The founder has been engaging with the audience, responding to questions and comments, including one from Katie in the chat (50m45s).

Panelists (50m48s)

• The investors are asked to give their top three picks from the seven companies presented, in reverse order, with a brief explanation for their top choice (50m51s).
• The companies presented are MasterTech, AI co-pilot for automotive repair; Querio, enables companies to do analysis and reporting on data; ConvertMate, uses AI to automate SEO for e-commerce; Ellis, sales platform for startups; LayerPath, uses AI to help companies create interactive product demos and studio-quality explainer videos; Rafa, applicant training system; and Chef, a company with a strong brand and founder market fit (51m41s).
• Gary's top three picks are Chef, Ellis, and MasterTech, with MasterTech as his number one due to its alignment with his thesis and a great pitch (52m25s).
• Katie's top three picks are Rafa, MasterTech, and Chef, with Chef as her number one due to its strong brand, storytelling, and founder market fit (52m48s).
• Sandy's top three picks are MasterTech, Querio, and Chef, with Chef as his number one due to its great category, strong unit economics, and easy shipping (53m18s).
• Andy's top three picks are Chef, MasterTech, and Ellis, with Ellis as his number one due to its potential to combine CRM with outreach and customer data (53m57s).

Final Scores (54m37s)

• The overall SC score was calculated week to week, and the overall winners were announced, with Chef in first place with 5 points, MasterTech in second place with 4.5 points, and LS in third place with 3 points (54m47s).
• The Syndicate poll results showed MasterTech in first place with 37% of the votes, Coro in second place with 19%, and Chef in third place with 14% (55m2s).
• The overall cohort 32 results showed MasterTech in first place, Coro in second place, and LS in third place (55m27s).
• The accelerator received 20,000 applications, resulting in 4,000 phone calls, and ultimately selected 30 people to join the accelerator out of 1,000 who attended Founder University (55m46s).
• The accelerator invested $25,000 in 50 to 75 companies out of the 1,000 who attended Founder University (56m7s).
• The team thanked everyone for joining and announced that Andre would be running the accelerator after Jackie, who is now working on LP relationships and the whisper Network (56m28s).
• The whisper Network is a secret program that informs trusted partners about companies that are a good fit for them, and it includes a database of 200 firms and 800 individuals (56m43s).
• The program allows partners to update their profiles and receive information about companies that match their interests (57m30s).
• The final advice given to the companies was to focus on product, team, and customer, and to block out everything else to increase their chances of building an important company (57m54s).