Graft reposted this
The future of work is the intelligence layer, not assistants. That’s why we built Graft! If you want to know what that is and why it’s inevitable, read on… https://lnkd.in/gncjcbAg #graft #ai #intelligencelayer
Graft's Intelligence Layer is the bridge between company information and business value. Now your teams can quickly access the collective intelligence of your organization - just connect your data, enrich it with expertise, and Graft delivers instant, relevant answers through a simple chat interface.
External link for Graft
San Francisco, US
Graft reposted this
The future of work is the intelligence layer, not assistants. That’s why we built Graft! If you want to know what that is and why it’s inevitable, read on… https://lnkd.in/gncjcbAg #graft #ai #intelligencelayer
Graft reposted this
Recently, a flurry of people have complained that they aren't seeing the ROI from AI. There are two possibilities: 1. The technology cannot produce the desired outcomes. 2. They are using it wrong. We know the first option is incorrect, because there are countless examples of companies using AI to improve operational efficiency, generate new revenue, and build innovative products. So, how are people using it wrong? Here are some of the most egregious errors I've seen: 1. Slapping a simple RAG pipeline on an LLM without any other supporting infrastructure or integrations; 2. Fine-tuning a model as a first step rather than an optimization step, performed after they've seen ROI; 3. Extremely narrow use cases that don't account for how they interact with broader tasks or workflows; 4. Building interfaces that fail to produce necessary behavioral changes because they were designed to be marketed, not used; and 5. Reinforcing data silos with intelligence silos, producing AI tools that are just as myopic as the people they are meant to assist. Not seeing ROI from a bad application of AI doesn't disprove its value any more than a poorly designed website disproves the value of the internet. At Graft, we built the Intelligence Layer based on our collective experience driving real business outcomes. If you want to see ROI from AI, we can help.
Graft reposted this
Graft has been recognized as one of the Top Artificial Intelligence Solutions Provider By CIO Applications. Graft's Intelligence Layer sits above your company’s information layer to help you build better products, run a more efficient business, and innovate faster. Every team member can engage with the collective intelligence of the organization, custom-tailored to your enterprise’s specific needs, ultimately accelerating your organization's growth and competitive advantage. Under the covers, the intelligence layer is a comprehensive modern AI platform, where search, predictive, and generative AI converge to drive real business outcomes. “You can focus on outcomes instead of navigating the complexity of choosing and integrating the infrastructure components; we have curated and combined premier solutions for you. There’s no longer a need to weigh the merits of various tools or question their performance— with Graft, you have the assurance of a thoroughly vetted, cohesive system,” says Adam Oliner, founder and CEO. The article will also be featured in the forthcoming special print edition on Artificial Intelligence 2024. Read More : https://lnkd.in/gSSDxTxF #DigitalTransformation #AIIntegration #BusinessInnovation #TechSolutions #ArtificialIntelligence
Graft reposted this
Under the hood, Graft is a ‘foundation model-based,’ ‘agentic,’ ‘compound AI’ system with ‘advanced RAG.’ And it had *all* of that before those terms were coined. Anyone trying to build production-grade solutions using AI, rather than toys or demos, likely did something similar. It’s just good systems engineering. I wonder which of the many capabilities that make Graft so powerful and simple to use will be next to get a shiny new buzzword from the community. 😉
Our CEO, Adam Oliner, talking about how to navigate AI buzzwords. "A positive experience with AI happens when the solutions are simple and trustworthy. Simple because most people aren’t AI experts and won’t alter their behavior just to use a new tool. Trustworthy because there’s typically already a way to solve the problem without AI, so if people are not getting better results quickly with AI, they will default to the old ways. Tools that improve existing workflows and automatically perform useful tasks that a person simply wouldn’t do otherwise, tend to be more successful."
Thank you to Shane O'neill @ CIO Online for letting me rant about AI buzzwords and marketing BS. It was a fun conversation! "In terms of shoddy implementations, chatbots can be a challenge. Lots of companies think you can fine-tune an LLM on your data and magically get a personalized AI chatbot. They spent spectacular amounts of money building bots only to see spectacular failures. Some bots have given customers crazy discounts, sold them a competitor’s product, given financial and legal advice, and so on. The fact is, real AI solutions require real engineering. Any vendor promising quick, easy answers should be treated as suspect." — https://lnkd.in/g9ZaNsqT
PDF parsers are tools designed to extract and process data from PDF files. Their significance lies in the ability to automate data extraction, streamline document workflows, and enable advanced data analysis. In this guide, Eugene Druzhynin explores 10 prominent PDF parsers, each with unique capabilities, helping you understand their functionalities and choose the right one for your needs. https://lnkd.in/grZD-PBM
Thanks Sapphire Ventures for including us in your GenAI App Infrastructure Stack!
The rapid surge in GenAI adoption has spawned an entire ecosystem of new tools and emerging startups to enable the development of next-gen applications. 🚀 Building on our market map shared last month, our latest blog dives deeper into the GenAI App Infrastructure Stack to help builders and backers navigate the landscape, spotlighting the startups we’re keeping a close eye on across the following categories: 🔹 Infrastructure 🔹 Foundation Models 🔹 Data Management & Processing 🔹 Model Training & Deployment 🔹 App Development & Orchestration 🔹 Validation & Security 🔹 Monitoring & Observability 🔹 End-to-End Training & Fine-Tuning Platforms 👉 Explore our market map and the full blog post here: https://lnkd.in/g5j9jwyw 📩 If you’re building in the space, reach out to us at [email protected], [email protected], or [email protected]. 🙏 A huge thanks to the contributors who provided insights for this piece: Debanjan Saha, Inbal Budowski-Tal, Julian Wiffen, Phil Gates-Idem, Guhan Venguswamy, Liam DeCoste, Nikita Solilov, Greg Hamel, and Josh Barclay. CC: Casber Wang, Anders Ranum, Kevin Burke, David Carter, Meghann Shacklette, Alex Lehman, Adam Liu, and Jake DellaPasqua. #GenAI #Infra #Startups
As LLMs proliferate in enterprise search, the issue of search results reliability becomes particularly critical. One potential solution to mitigate against hallucinations is RAG (Retrieval Augmented Generation), an AI framework that enhances the quality and relevance of generated text. Hear about the latest developments in this webinar!
The knowledge management space is evolving. 🐒 Join me tomorrow for a webinar to learn about some of the recent technologies that are contributing to this evolution! #graft #knowledgemanagement #ai #llm #rag
The problem of hallucinations, false information, and fabricated data is well known to searchers. It puts people off from trusting search results when generative AI is involved. As LLMs proliferate, not only in the open web search world but also in enterprise search, the issue of search results reliability becomes particularly critical. One potential solution to mitigate against hallucinations is RAG (Retrieval Augmented Generation), an AI framework that enhances the quality and relevance of generated text. Join Adam Oliner to hear about the latest developments in this webinar. https://lnkd.in/gTwaUFs4
We just published our State of AI for Internal Operations where we surveyed 300 executives about the future of AI in internal operations. 5 key findings: 👉 The majority of organizations view knowledge management as central to their AI strategy, with 59% of respondents indicating this. 👉 Improved efficiency and productivity is the top priority in implementing AI in internal operations, as indicated by 61% of respondents. 👉The biggest challenge in adopting AI for internal operations is concerns about data privacy and security, with 55% of respondents expressing this. 👉 The majority of organizations are in the early stages of exploration and experimentation in terms of AI maturity, with 28% of respondents indicating this. 👉The key application area for AI technologies in internal employee operations is in Knowledge Management and Search, and Learning and Development, with 52% of respondents indicating each of these areas. View the full report here: https://lnkd.in/eq9aEi-y