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Industries
AI & Machine Learning
Company Size
501-1,000
Company Stage
Series D
Total Funding
$940M
Headquarters
Toronto, Canada
Founded
2019
Cohere provides advanced Natural Language Processing (NLP) tools and Large Language Models (LLMs) through a user-friendly API. Their services cater to a wide range of clients, helping businesses improve content generation, summarization, and search functionalities. Cohere's business model focuses on offering scalable and affordable generative AI tools, generating revenue by granting API access to pre-trained models that can perform various tasks such as text classification, sentiment analysis, and semantic search in multiple languages. The platform is customizable, enabling businesses to create tailored solutions, and its multilingual support allows for effective use in international contexts.
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Total Funding
$940M
Above
Industry Average
Funded Over
5 Rounds
Industry standards
Nvidia has significantly increased its investments in AI startups, participating in 49 funding rounds in 2024, up from 34 in 2023. This surge is part of its strategy to expand the AI ecosystem. Notably, Nvidia participated in a $6.6 billion round for OpenAI in October, contributing $100 million. These investments exclude those by its corporate VC fund, NVentures, which also increased its activity, engaging in 24 deals in 2024 compared to 2 in 2022.
Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more. The advent of natural language search has encouraged people to change how they search for information, and LinkedIn, which has been working with numerous AI models over the past year, hopes this shift extends to job search.LinkedInâs AI-powered jobs search, now available to all LinkedIn users, uses distilled, fine-tuned models trained on the professional social media platformâs knowledge base to narrow potential job opportunities based on natural language. âThis new search experience lets members describe their goals in their own words and get results that truly reflect what theyâre looking for,â said Erran Berger, vice president of product development at LinkedIn, told VentureBeat in an email. âThis is the first step in a larger journey to make job-seeking more intuitive, inclusive, and empowering for everyone.âLinkedIn previously stated in a blog post that a significant issue users faced when searching for jobs on the platform was an over-reliance on precise keyword queries. Often, users would type in a more generic job title and get positions that donât exactly match. From personal experience, if I type in âreporterâ on LinkedIn, I get search results for reporter jobs in media publications, along with court reporter openings, which are a totally different skill set. LinkedIn vice president for engineering Wenjing Zhang told VentureBeat in a separate interview that they saw the need to improve how people could find jobs that fit them perfectly, and that began with a better understanding of what they are looking for. âSo in the past, when weâre using keywords, weâre essentially looking at a keyword and trying to find the exact match
In just 2 days, TechCrunch Sessions: AI takes over UC Berkeleyâs Zellerbach Hall for a one-day summit built for the minds shaping AI. Expect hard questions, bold ideas, and real insightâdesigned to challenge your assumptions, sharpen your competitive edge, and maybe â just maybe â redefine the trajectory of your research, your product, or your company.Whether youâre an AI researcher, engineer, or founder, this summit offers insights into the forefront of AI development and its applications. Donât miss the opportunity to engage with leaders shaping the future of artificial intelligence.Experience it all at a low rate while you still can: Save $300 on your ticket and get a second pass for 50% off. Prices jump when the event doors open. Register your tickets now.Voices shaping the future of AI â live on our main stage and in breakouts. Jared Kaplan, Co-founder and Chief Science Officer, Anthropic â On building responsibly with frontier models.Artemis Seaford, Head of AI Safety, ElevenLabs â On real-world safety protocols and ethical architecture.Jae Lee, CEO, Twelve Labs â Redefining whatâs possible in multimodal AI.Sara Ittelson, Partner, Accel â Where investors see the next wave of AI opportunity.Logan Kilpatrick, Senior PM, Google DeepMind â On developer tooling and scaling responsibly.Ann Bordetsky, Partner, NEA â How operators-turned-investors think about the current AI boom.Oliver Cameron, CEO, Odyssey â Lessons from launching against incumbents in frontier tech.Iliana Quinonez, Director, Google Cloud â Infrastructure deep dives for AI startups.Ion Stoica, Co-founder, Databricks and Professor, UC Berkeley â On data infrastructure at massive scale.Kanu Gulati, Partner, Khosla Ventures â The tactical VC perspective on AI product-market fit.Astasia Myers, General Partner, Felicis â On founder storytelling and strategic capital
Ivan Zhang says AI firms have their work cut out winning back clients âburnedâ by failed pilots
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreWith demand for enterprise retrieval augmented generation (RAG) on the rise, the opportunity is ripe for model providers to offer their take on embedding models.French AI company Mistral threw its hat into the ring with Codestral Embed, its first embedding model, which it said outperforms existing embedding models on benchmarks like SWE-Bench.The model specializes in code and âperforms especially well for retrieval use cases on real-world code data.â The model is available to developers for $0.15 per million tokens.The company said the Codestral Embed âsignificantly outperforms leading code embeddersâ like Voyage Code 3, Cohere Embed v4.0 and OpenAIâs embedding model, Text Embedding 3 Large.Super excited to announce @MistralAI Codestral Embed, our first embedding model specialized for code.It performs especially well for retrieval use cases on real-world code data. pic.twitter.com/ET321cRNli â Sophia Yang, Ph.D. (@sophiamyang) May 28, 2025Codestral Embed, part of Mistralâs Codestral family of coding models, can make embeddings that transform code and data into numerical representations for RAG.âCodestral Embed can output embeddings with different dimensions and precisions, and the figure below illustrates the trade-offs between retrieval quality and storage costs,â Mistral said in a blog post. âCodestral Embed with dimension 256 and int8 precision still performs better than any model from our competitors. The dimensions of our embeddings are ordered by relevance
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Industries
AI & Machine Learning
Company Size
501-1,000
Company Stage
Series D
Total Funding
$940M
Headquarters
Toronto, Canada
Founded
2019
Find jobs on Simplify and start your career today