Led · Series C · Jul 2021

Tailor Brands provides AI-driven tools that automate logo creation and other branding and marketing services for small businesses. The company is assembling components into a one-stop SaaS platform that delivers design, branding and marketing capabilities to help owners launch and scale. Headquartered in New York and Tel Aviv, Tailor Brands is onboarding some 700,000 new users per month and says more than 30 million businesses have used the platform. It began monetizing offerings at the end of 2019 and reported triple-digit annual revenue growth. The company plans to use new funding for R&D, to double the team, build additional capabilities and pursue potential acquisitions. Tailor Brands aims to leverage its data to proactively guide customers through next steps and create an integrated ecosystem where users can manage their business without leaving the platform. Tailor Brands automates branding and marketing for small businesses by applying machine learning to logo design, copywriting, and social media strategy. CEO Yali Saar positions the company at the intersection of design and machine learning, with automated logo creation as a flagship feature. Users can try creating logos for free but must pay to access high-quality image files. The technology has been used to create 45 million logos; the company reported 3.86 million customers last year and is adding roughly half a million new businesses to the platform each month. Tailor Brands launched at TechCrunch’s Startup Battlefield in 2014 and plans to use new funding to expand globally, add more languages, and introduce additional tools to its full branding suite. The company has raised a total of $20.6 million to date. Tailor Brands offers an automated branding service that generates logos, promotional materials, and social media campaigns using machine-learning models. CEO Yali Saar aims to automate the full-service branding agency by combining design best practices with algorithmic generation. The company employs an interdisciplinary team of engineers and designers who are required to learn the intersections of design and machine learning. Tailor Brands collects high-volume behavioral data — users create a new design every 1.5 seconds — which Saar says can surface macro trends in design and branding. Pricing ranges from $24 to $99 per package, and the company also offers a $9.99/month subscription plan. The product has improved since its 2014 beginnings but still exhibits occasional design faults. Tailor Brands uses algorithms to generate logos and a range of branding items, including business cards, bags, mugs, pens and online materials such as social media profiles for small businesses. The site lets users download logos for free and offers unlimited free revisions while monetizing via higher-resolution images and designed asset packages. Paid offerings start at $24 for a single job and go up to $99 for a complete package; the company provides design files but does not print or ship physical cards. Tailor Brands launched in beta at Disrupt San Francisco and has done business in over 35 countries since that launch. The company raised $1.1 million in seed funding from Disruptive Fund and various angel investors to build out its automated design process. CEO Yali Saar said the funds will be used to hire more designers and expand services such as app design as the team experiments with additional offerings.

Participated · Series B · Jul 2021

Teikametrics offers Flywheel 2.0, an AI-driven marketplace optimization SaaS that automates advertising bidding, market intelligence, financing access, inventory forecasting and multi-marketplace management for online sellers. The platform is used by thousands of sellers and optimizes more than $8 billion in GMV for customers including Munchkin, mDesign, Clarks, Nutribullet, Conair, Nutrafol and Solo Stove. Teikametrics reports rapid growth on Walmart’s marketplace (900% year-over-year) and aims to expand beyond Amazon and Walmart into additional marketplaces. The company positions Flywheel 2.0 to provide full-spectrum ecommerce tools—ads, inventory, financing and analytics—to help brands scale. Founded in 2015 and based in Boston, Teikametrics intends to use new funding to accelerate product development and marketplace expansion in 2021. The business emphasizes AI-enabled decision-making to improve sell-through, ROI and supply-chain efficiency for sellers. Teikametrics builds the Flywheel platform to help retailers and marketplace sellers optimize online ad buying using retailer data such as transactions, inventory and pricing. The company launched focused on Amazon sellers and has expanded to a partnership with Walmart to offer cross-marketplace campaign optimization. Teikametrics says it is AI-first, positioning its product as automation and intelligent decision-making for advertising. The startup plans to launch products beyond advertising later this year and envisions an operating system that optimizes inventory, pricing and other aspects of a retailer’s business. It works with more than 3,000 brands, including Clarks, Razer, Power Practical, Zipline Ski and Mark Cuban’s Brands, and recently hired former Amazon ad executive Srini Guddanti as chief product officer. The Boston-headquartered company has raised additional funding, including a $10M Series A in 2018 and a new $15M round announced in this article. Teikametrics operates Flywheel, a retail optimization platform that helps third-party sellers optimize Amazon advertising by incorporating transaction, inventory, and pricing data to measure gross profit margins and profitability after ad spend. CEO Alasdair McLean‑Foreman says the company uses big data to model seller-side economics that sellers and brands lack in-house. Its platform is used by advertisers representing 1 percent of all sales on Amazon and customers include Razer, Power Practical and Zipline Ski. Teikametrics was founded in 2013 and had been bootstrapped until this round. The company plans to expand its technology beyond Amazon to other marketplaces. The newly raised capital is intended to hire a significant number of machine-learning engineers to better model its large datasets.