AppliedAI
@AppliedAICo
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The makers of Opus: the new standard for human-AI collaboration in the enterprise. We handle the process; you handle the purpose.
United Arab Emirates
Joined August 2022
Why do so many enterprises struggle to move from AI hype to real-world impact? Find out here
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What if you could conduct emergency medical triage from a smartphone? The winner of the @lablabAI AI Genesis Hackathon, @FuyutoMiyake, did just this with the help of our #Opus platform — turning a phone into a lightweight multimodal sensor as the central decision engine for
lablab.ai
It's your turn to build something that matters. If you're serious about AI, you're in the right place - the community where developers and founders ship AI products through world-class hackathons.
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Huge congratulations to everyone who participated in the @ADNOCADmarathon last weekend! Our #Opus team came out in force, with runners across both the 10km and 42km distances. What a great day to challenge ourselves, be inspired, and share some fantastic finish line moments with
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Congrats to all the winners – and to every builder who showed up, shipped, and proved what this community can do. This one was for the books. The momentum is real. And the next one is already loading. Full Winner Ceremony footage drops soon. Stay tuned. AI Genesis was the
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“The most boring AI company in the world” – that’s how Founder & CEO @ahbolur describes @AppliedAICo. For the latest installment of our #IntelligentFuture series, exploring how advanced tech is shaping our lives, he and I discussed how the company is embedding Artificial
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Awesome to have you onboard @wyldinvestments 🚀
Excited to announce Wyld’s investment in @AppliedAI's Series A alongside @G42ai, @BessemerVP,@PalantirTech+others! @AppliedAICo is transforming enterprise automation in regulated sectors with human-centric #AI. Proud to back 3-time founder @ahbolur & team! https://t.co/WkAtowsB4I
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Middle East Venture Partners @MEVPcapital backs @AppliedAICo in $55M Series A AppliedAl just landed a $55M Series A, co-led by @G42ai and @BessemerVP, with Middle East Venture Partners (@MEVPcapital) joining the round to fuel global expansion from Abu Dhabi.
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Traditional, time-based model of work is obsolete and inefficient in today's AI age. There is a new way to this. https://t.co/CsVLuR3af9
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Thank you for the early support @C_Angermayer !
Love the #UAE - not just because it’s the capital of capital but also for its thriving startup ecosystem & unmatched entrepreneurial spirit. Proud to be an early investor in @AppliedAICo, which just raised $55M from G42, @PalantirTech, Bessemer & others. https://t.co/aLIWB45ihT
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AppliedAI is grateful for Palantir Technologies support as an investor and operational partner and we look forward to a number of major announcements in 2025.
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The keynote highlighted how Palantir AIP infrastructure can be leveraged by external platforms for fast and secure model inference on highly sensitive data.
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AppliedAI presented a technical keynote on Opus’s integration with Palantir Technologies AI Platform (AIP) and Foundry.
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@AppliedAICo had the privilege of sharing Opus State of the Art Knowledge Workflow Generation and Execution architecture at @PalantirTech DevCon 2024 in Palo Alto, California. https://t.co/naS67cwWyw
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That’s a wrap on Day 1 of DevCon, which featured: • A Fireside Chat on deprecating backend dev with Palantir CTO @ssankar • Keynotes from @DTNMarkets, @Lennar, @anduriltech, @AppliedAICo, @the7bridges and Palantir Chief Architect @hyperindexed • Three new product launches:
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75% of consumers are concerned about AI misinformation. How can policymakers and experts collaborate to create ethical AI frameworks addressing transparency, bias, and misinformation? Watch the #FII8 livestream for an expert discussion: https://t.co/8c7jwJiwlz
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"We believe the human element of AI will reside in two areas: intent and liability. We’re seeing that many people using workflows need to assign liability somewhere, with a human signing off and assuming liability and responsibility for that role." Great contribution by Founder
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MEDIC’s multifaceted evaluation reveals these performance trade-offs, bridging the gap between theoretical capabilities and practical implementation in healthcare settings, ensuring that the most promising models are identified and adapted for diverse healthcare applications.
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and other tasks. Our results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths, such as low hallucination or lower cost of inference.
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MEDIC features a novel cross-examination framework quantifying LLM performance across areas like coverage and hallucination detection, without requiring reference outputs. We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, ...
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