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Brad Menezes
Co-Founder & CEO at Superblocks
Brad Menezes is the Co-Founder and CEO of Superblocks, a company he started in March 2021.1 Superblocks is a low-code platform that helps developers build mission-critical custom internal software quickly using AI.12
Prior to founding Superblocks, Brad held several notable positions:
- Scout at Sequoia Capital (July 2019 - July 2021)1
- Senior Director of Product Management at Datadog (February 2017 - February 2021)1
- Startup founder in Y Combinator's Summer 2015 batch (June 2015 - February 2017)1
- Lead Product Manager for Reservations & Delivery at Yelp (September 2012 - June 2015)1
Brad has a background in Mechanical Engineering from the University of Toronto, where he studied from 2007 to 2012.1
Under Brad's leadership, Superblocks has achieved significant milestones:
- Raised a total of $38 million in funding, including a $37 million Series A round2
- Named a Leader in Low-code Development Platforms by G24
- Trusted by thousands of enterprise teams3
Brad's experience spans product management, entrepreneurship, and angel investing in enterprise software startups.1 His LinkedIn profile shows he has over 500 connections and is based in New York.1
Highlights
For the first time, G2 named Superblocks a Leader in Low-code Development Platforms for their Spring 2024 season 🚀 Thank you to all our customers, it's an honor to serve you 🙏

Last week, Anthropic Product VP Michael Gerstenhaber took the stage at MongoDB.Local in NYC with Dev Ittycheria for the keynote.
3 major takeaways on how enterprises are adopting AI today 👇
1️⃣ AI Applications around Internal Tooling to accelerate key operational processes are dominant use cases today.
For example, a major airline is using AI to allow gate agents, maintenance, and office staff to collaborate and find SMEs. Another use case is AI in law firms, capable of accelerating tasks like legal research. Pfizer uses Anthropic Claude to compare individuals' lab results with their vast oncology research base and accelerate the target assessment process from months to days, already seeing improved results 🚀
2️⃣ Building an AI application is far more than model selection, it requires organization-wide collaboration.
This usually means connecting engineers writing the application code and prompts with internal domain experts across the business first. It always means good prompting strategies and retrieval of proprietary, domain-specific data. He discussed common patterns in model orchestration; prompting (k-shot, tool use, chain of thought); fine-tuned models; storage, caching, and retrieval; and infrastructure options 🛠️
3️⃣ Ecosystem programs like MongoDB AI Applications Program (MAAP) will be a key enabler for enterprise AI Adoption.
AI is so new that organizational excitement is at an all-time high. Determining the use cases, strategies, and implementation with professional services will be critical as organizations tackle major operational challenges that require partners in a fast-changing environment ⚡

