Fireraven

Fireraven is presenting at PyData Copenhagen alongside Vincent Laulagnet from micromove.com on October 24th at the Microsoft offices in Denmark! 🚀 We'll be doing a deep dive into how to implement safe and reliable AI systems that comply with the latest AI regulations in critical sectors like medical, pharmacology, and the military. 🏥⚕️💼 We'll showcase our solution and how it can be used to mitigate risks and implement robust validation and testing strategies. 🔒✅ If you're interested in building AI that’s both innovative and responsible, you won’t want to miss this! Come join us and explore the future of compliant AI. 🌍💡 #PyDataCopenhagen #Fireraven #AIRegulation #SafeAI #ReliableAI

🌟 I’m thrilled to share that Fireraven has been featured in an article by Nordea Invest! 🎉 This recognition means so much to us, and I couldn't be prouder of what our team is achieving. We’re passionate about making AI safer and more ethical, and it feels amazing to see our efforts highlighted. A heartfelt thank you to Sanne Opstrup Wedel for capturing our story, and to Nordea, and of course, DTU Skylab, NEXT AI for their unwavering support. Together, we’re tackling the challenges of AI and striving to create technology that truly serves people. I invite you to check out the article (in Danish) and see how we’re making a difference! 🔗 https://lnkd.in/d6GhUgtr #Fireraven #Grateful #AI #NordeaInvest #AIDenmark

Fireraven will be showcasing at ALL IN!! 🚀 If you're struggling to keep your Chatbots and LLMs on track 100% of the time, or gain insights into client usage (and how to improve), we have the perfect solution for you! 😉 Plus, we're excited to share that we'll soon be kicking off our Seed round to fuel our next phase of growth and scale our impact!📈🔥 If you're attending the event in Montreal, come see us and learn more about how we're making AI and LLMs safer and more reliable!!🌐✨ #AISecurity #ReliableAI #ReliableChatbots #LLMAnalytics #SeedRound #Investment #StartupRaise Next AI DTU Skylab Front Row Ventures

Awesome to see large corporations starting to take this initiative to make sure they can use Generative AI in a safe and secure way!! This is exactly why we built Fireraven! To help businesses implement AI safely and reliably for real-world usage. Et merci Jean-Luc SansCartier pour avoir partagé ça!!

Title: Why Your Business Isn't Ready for LLMs (And Why It Should Be) - Part 4 Yesterday, we explored the architecture of hybrid chatbots. Today, let's dive into Fireraven's hybrid solution that combines the flexibility of LLMs with the reliability of traditional systems. Fireraven offers a hybrid chatbot solution that merges the flexibility of LLMs with the dependability of traditional chatbots. But Fireraven goes further by enabling businesses to create a custom database of safe questions tailored to their specific needs and context. And the best part? You don’t need to know how to code to manage it. With Fireraven, you can easily define which topics you want to address, which questions are safe, and which should be blocked, all through our intuitive interface. Thanks to our robust fact checker and RAGs (retrieval-augmented generation), there's almost no risk of hallucinations. If your chatbot ever produces an incorrect response, we can explain why it happened and show you how to edit your database to prevent future errors, making it the safest chatbot you’ve ever tried. Building your question database is straightforward with Fireraven’s technology, which guides businesses through our generative models, advanced testing, and red-teaming techniques. This ensures the LLM only generates responses to questions rigorously vetted for accuracy and relevance within your specific business context. For instance, a bank using Fireraven’s solution can populate its database with thousands of banking-related queries, such as "How do I reset my online banking password?" or "What are the current mortgage rates?" The chatbot will never respond to questions unrelated to banking. Once the database is established, Fireraven’s chatbots continually learn from user interactions, refining and expanding their capabilities. This process ensures that the chatbot becomes more accurate and efficient over time, providing a seamless and reliable user experience. Ready to explore Fireraven? Visit our website to learn more and see how our solution can revolutionize your customer service. For the full version now, go to our website's blog section : https://lnkd.in/dPZ53TWQ To schedule a free 30-minute consultation on any topic related to safe AI, please connect with us on Calendly: https://lnkd.in/dTsGupiu

Title: Why Your Business Isn't Ready for LLMs (And Why It Should Be) - Part 3 In our last discussion, we highlighted the limitations of traditional chatbots. Today, let's explore a balanced solution: hybrid chatbots that merge the strengths of LLMs and traditional systems. Hybrid chatbots leverage the power of LLMs to generate natural language responses while being guided by a structured framework to ensure accuracy and relevance. This architecture comprises four integral layers: the Rule-Based Layer, the LLM Layer, the Retrieval-Augmented Generation (RAG) system, and the Verification Layer. Each layer, distinct yet interconnected, contributes to a chatbot that is articulate, accurate, and dependable. Let's dive into the anatomy of these layers and explore how they work together to create this sophisticated system. Rule-Based Layer In the grand architecture of hybrid chatbots, the Rule-Based Layer serves as the vigilant gatekeeper. This layer ensures that the chatbot operates within predefined boundaries and adheres to strict protocols. It starts by assessing whether the chatbot is even permitted to answer the query at hand. Is the question relevant? Does the user have the necessary access rights to the requested information? Do we already have the answer within our existing knowledge base? Retrieval-Augmented Generation (RAG) Next, we enter the dynamic and resourceful RAG layer. This is where the chatbot’s true prowess in information retrieval shines. When a user query arrives, the RAG system dives into a contextual database, fetching all pertinent information needed to construct a comprehensive and accurate response. LLM Layer At the heart of the hybrid chatbot lies the LLM Layer, the poet and the thinker. This layer takes the structured data provided by the RAG system and crafts it into natural, flowing language that feels almost human. The LLM (Large Language Model) generates responses that are not only contextually accurate but also engaging and easy to understand. Verification Layer Finally, we have the Verification Layer, the meticulous editor and fact-checker. This layer is crucial in maintaining the integrity and professionalism of the chatbot’s responses. It performs rigorous fact-checking, applying filters and classifiers to ensure the generated content is accurate, polite, and aligned with company guidelines. Together, these four layers form a robust and sophisticated architecture that empowers hybrid chatbots to deliver exceptional service. Tomorrow, we'll introduce Fireraven’s solution, a cutting-edge hybrid chatbot that sets a new standard in customer service. For the full version now, go on our website in the blog section : https://lnkd.in/dZtUk2te Got questions? The Fireraven team is here to help! Schedule a free 30-minute call with us today: https://lnkd.in/d_iJHxWu

Why Your Business Isn't Ready for LLMs (And Why It Should Be) - Part 2 In the first part of this series, we discussed why LLMs aren't yet suited for client-facing chatbots. Today, let's explore why traditional chatbots often lead to frustration and why businesses need to rethink their approach. Traditional Chatbots: An Exercise in Frustration Most companies still rely on traditional chatbots for their client-facing applications. These chatbots operate on predefined scripts and rules, providing responses based on specific keywords or phrases. Their strength lies in their predictability and reliability, making them ideal for straightforward tasks such as answering frequently asked questions or guiding users through simple processes. However, traditional chatbots have their limitations. They lack the ability to understand context or manage complex conversations. If a user asks a question that falls outside the chatbot's programmed responses, the bot may fail to provide a satisfactory answer, leading to frustration. They are also not capable of learning from new interactions; they can only do what they've been explicitly programmed to do. This inflexibility can make them less effective in dynamic environments where user queries vary widely and require nuanced understanding. A Real-Life Example Take my recent experience with my bank’s website. I needed a specimen check but had no idea where to find it on their labyrinthine interface. Desperately, I clicked the help button, which led me through a series of predefined options that ultimately couldn’t assist me. Frustrated, I called customer service, waited on hold for 30 minutes, and finally got the answer in three clicks. Imagine if an LLM-powered chatbot had been there. It could’ve understood my query, navigated the bank’s database, and provided the document in seconds. This would’ve saved me time, spared the customer service trainee the hassle, and been more efficient for the company. This experience highlights the gap between traditional chatbots and the potential of LLMs. Traditional systems are like maze runners—they follow a set path, and any deviation leaves them confused. In contrast, LLMs can think and adapt on the fly, providing a much more intuitive and satisfying user experience. Looking Ahead Tomorrow, we'll discuss hybrid chatbot solutions that can enhance both reliability and flexibility. For the full blog post, now, visit our blog. https://lnkd.in/dPZ53TWQ Got questions? The Fireraven team is here to help! Schedule a free 30-minute call with us today: https://lnkd.in/dTsGupiu

Why Your Business Isn't Ready for LLMs (And Why It Should Be) - Part 1 In a world where business interactions are personal and seamless, Large Language Models (LLMs) promise to transform customer service. Imagine a digital assistant that understands your needs, remembers past interactions, and responds with empathy and precision. Despite their extraordinary capabilities, why do customer interactions still feel more robotic than revolutionary? In this part, we'll dive into why LLMs, despite their sophistication, face hurdles in customer service. The issue isn't their intelligence but their ability to handle real-world interactions efficiently. Rewind to 2010: You're on a website, seeking support from a chatbot that only understands a limited set of commands. “Press 1 for billing, press 2 for technical support.” Frustrating, right? These early chatbots operated on rigid decision trees. Fast forward to today: Enter LLMs like OpenAI’s GPT-4. These cutting-edge AIs are designed to understand and generate human-like text, making conversations more fluid and natural. They can explain complex topics, navigate legal questions, and even score high on challenging exams. Think of them as the chatbots’ equivalent of Iron Man’s Jarvis—intelligent, flexible, and incredibly capable. Yet, here’s the conundrum: despite mastering complex tasks, LLMs struggle with straightforward customer service. Why? It's not about intelligence but implementation, reliability, and trust. The Hallucination Problem: Imagine resolving a billing issue with a bot, and it starts giving you random financial advice. This “hallucination” happens because LLMs generate responses based on patterns, not actual understanding. These unpredictable responses make hallucinations a significant problem, requiring extensive oversight and adjustments. Data Privacy Concerns: Consider an unpredictable AI accessing your sensitive data. While it offers valuable insights, there's a risk of accidental exposure of confidential information. These models remember everything of their training data, posing a significant privacy issue. Despite these challenges, companies are investing in LLMs tailored to specific fields, balancing advanced AI benefits with data privacy and security. Tomorrow, we'll explore traditional chatbots, their strengths, and limitations. For the full version today with all 4 parts, visit our blog : https://lnkd.in/dPZ53TWQ Got questions? The Fireraven team is here to help! Schedule a free 30-minute call with us today: https://lnkd.in/dTsGupiu

🚀 Fireraven is now backed by Front Row Ventures to fuel our growth and scale the reach of our product! 🚀 We're thrilled to leverage this early investment and partnership to help us grow and make AI and LLMs more reliable and safe for responsible use. 🌟🔒 This is a major step forward in our mission to create safe, ethical and inclusive AI solutions that benefit everyone. Huge thanks to the FRV team during this whole process 🔥 Abderraouf Nechadi Amira Igouzoul Martin Chaperot-Merino Christian Levan And thanks to Next AI, DTU Skylab and CurHumBra Consulting ApS for all the support! 🙏 Stay tuned for more updates as we continue our journey! 💡✨ #ReliabileAI #LLM #Fundraising #SafeAI #EthicalAI #Growth

🚀 Fireraven is at the Collision Conf! 🚀 We're thrilled to announce that we're preparing a waitlist for the second version of our product, designed to make LLMs more reliable and safe. 🔒🤖 If you're attending the conference in Toronto, we'd love to connect! Reach out to us and let's chat about the future of AI. 🌐✨ #CollisionConf #Fireraven #AI #LLM #TechInnovation #SafeAI #ReliableAI