Felicis

We raised $21.5 Million for Wherobots! Our journey began when I, then a computer science professor, met Jia Yu, who was completing his PhD at ASU. We quickly bonded over our shared passion for geospatial data and saw its largely untapped potential. We realized that existing data warehousing and analytics tools were fundamentally built for internet data, not geospatial data. As a result, they underperform, and lack key features for intuitive geospatial analysis. On the other hand, legacy geospatial tools live in a silo, are often closed-source, or built on outdated architectures, making geospatial solutions inaccessible for most organizations. To address this, we created Apache Sedona, an open-source geospatial compute framework that has since achieved over 40 million downloads and is used by companies like Amazon and Land O’ Lakes for planetary-scale workloads. After years of growing Apache Sedona, we saw the need for a more comprehensive enterprise solution and launched Wherobots, a fully managed, scalable cloud platform designed to make geospatial solutions easy to build within the modern data and AI ecosystem. This round is led by Felicis—huge thanks to Aydin Senkut and Gabriella G.! Felicis continues to revolutionize the VC industry with their founder-friendly approach and bold investments. We’re also grateful for the strong support from our existing investors, Wing Venture Capital and Clear Ventures. A big thank you to Peter Wagner from Wing VC  who has a keen eye and the patience to back transformative data and AI infrastructure companies like Snowflake, Pinecone, and now Wherobots.  Our friends at Clear Ventures deserve special recognition—Chris Rust and Noor Kamruddin, your targeted AI platform for identifying promising investments and your early belief in us during the uncertain pre-commercial phase mean so much. Lastly, thank you to P7 (Abhishek Shukla) and JetBlue (Ryan Chou) for your participation in this round. We’re excited for the journey ahead! I want to take a moment to thank and applaud the incredible Wherobots team. Over the past two years, we've experienced an astounding 1000% growth. I'm truly humbled to work alongside such talented individuals who make my job both enjoyable and rewarding. This team's dedication and talent inspire confidence in achieving remarkable outcomes now and in the future. Let’s keep it going, Botsters! In closing, I want to emphasize that this is just the beginning. We understand that bringing our vision to life requires collaboration, which is why we’re proud to partner with leading cloud providers like AWS. If you’re already on AWS, you can start leveraging Wherobots today to power your geospatial workloads. The journey ahead is exciting, and we’re thrilled to have you with us!

Two weeks ago, we at DatologyAI released our first results demonstrating massive gains from data curation on contrastive image-text models. Today, I'm incredibly excited to share our newest results, applying our curation pipeline to LLMs. It's absolutely astonishing to see what a small, incredibly talented group of individuals can accomplish, and boy have we cooked! Starting with an exact-deduplicated version of Red Pajama v1 as our baseline and by manipulating only the training data for the model: Train Faster -- Training on our curated data reached the same baseline performance 7.7x faster, meaning results cost dramatically less to obtain and drastically improving iteration speed. Train Better -- Push the frontier of what's possible with a given budget, improving performance by 8.5 absolute percentage points (60.5% Datology vs. 52.0% RPJv1). This isn't just because of Red Pajama: compared to the strongest publicly curated datasets, DataComp-LM and FineWeb-Edu, improve performance by 4.4% and 6.1%, respectively. Train Smaller -- Better data enables you to train smaller models. Reduce cost per query at inference by 2.1x while simultaneously increasing performance over the baseline by 5.7%. As with our image-text results, we present these results both at a high-level (https://lnkd.in/g_hMR5Tx) and with an extremely meaty technical deep-dive for all of you who want the nitty-gritty details (https://lnkd.in/gY5tpq3s). We are just getting started on our journey and are so excited about what's in store. Are you training or customizing your own text models and want to improve performance, training efficiency, and inference efficiency through better data? Get in touch (https://lnkd.in/gSGckr6s)! Are you a data-obsessed researcher, engineer, or somewhere in between who wants to push the bounds of what's possible with better data? We're hiring Members of Technical Staff across a number of roles (https://lnkd.in/gHCwPk8e).

We’re super excited to announce the close of our $100M Series C, led by DCVC! This investment comes on the heels of our passive cooldown demonstration, a critical milestone test of a full-scale, non-fueled reactor, and brings the company’s total venture funding to $160 million. The funds will primarily be used to complete our Kaleidos Development Unit (KDU), which we will fuel and operate at Idaho National Laboratory's Demonstration of Microreactor Experiments facility. The KDU is the same reactor design that will be mass manufactured and sold to customers. Radiant is excited to welcome new investors Felicis, Washington Harbour Partners LP, and Chevron Technology Ventures, and appreciates the continued support of Andreessen Horowitz, Union Square Ventures, Founders Fund, Decisive Point, McKinley Alaska Growth Capital, Boost VC, and Also Capital. We've got the team. We've got the hardware. And now we've got the money...it's time to build a reactor!! If you want to learn more, check out Alan Neuhauser's writeup in Axios: https://lnkd.in/gW8k-E57

Models are what they eat: high quality data lead to high quality models, enabling faster training of better models with fewer parameters. However, identifying and curating high quality data at scale, automatically, is an incredibly challenging problem requiring deep expertise. Our goal at DatologyAI is to make state of the art data curation accessible to anyone who wants to train a model, and we’ve been hard at work realizing this vision over the last year. On a personal note, I am so proud of the incredible work our small, but mighty team has accomplished, and today, I’m incredibly excited to share our first set of results at DatologyAI! We focused on contrastive models (ala CLIP) trained on the large-scale DataComp dataset, and the results we’ve been able to achieve have exceeded our already high expectations! Train Faster - Training on DatologyAI’s optimized dataset, we were able to reach the same performance with up to ~98% less compute, meaning that models cost dramatically less to train and train dramatically faster! Train Better - Models trained on our optimized data for the same compute budget achieve up to 13 absolute percentage points better performance relative to models trained on raw data. Train Smaller - Train models with >60% fewer parameters to better performance by training on our curated data. Check out our high-level blog post here (https://shorturl.at/jkYqk), and if you’re interested in all the nitty, gritty details, check out our technical deep dive here (https://shorturl.at/Mt0k9). We are so excited about these results, and we are just getting started! Stay tuned for more exciting results on text models coming very soon!