Imran Chaudhri’s Post

Allie K. Miller, Tonic.ai: Revolutionizing data privacy with secure synthetic data generation! #DataPrivacy #SyntheticData #DataSecurity #Innovation #TechSolutions #PrivacyRevolution #SecureData #DataManagement #AI #MachineLearning

Data is the fuel of AI. So protecting your data is job number one. Whether it’s private emails, invoices, contracts, or customer info, you need to have a plan for sensitive information in AI workflows. Tonic.ai is jumping on this with the secure data lakehouse for LLMs. 🔹 Converts unstructured data to standardized, AI-ready data 🔹 Enhances data with high-quality metadata using advanced NER models 🔹 Detects and redacts private data automatically 🔹 Helps improve RAG and fine-tuning workflows in minutes As an AI advisor for Tonic, I’m unbelievably excited to see this launch. More info here but feel free to DM me or comment your email and I’ll connect you to the executive team: https://lnkd.in/eCipvW32

The rise of AI chips signifies a paradigm shift in data center operations. These specialized processors deliver superior processing power and efficiency for cloud computing and big data analysis, accelerating innovation across various sectors. However, high development costs remain a hurdle. #AIHardware #DataCenterTransformation #CloudOptimization #BigDataAnalytics #MachineLearningAcceleration #AIInnovation #TechChallenges For More Insights, visit: https://lnkd.in/enBGr7Ta 🌟 Seeking the Path to Market Success? 📈🔍 Connect with us today at 👉 https://lnkd.in/e9pisJmX 👈 and embark on a journey of informed decision-making.

🔍 Traditional LLM metrics are broken for AI agents. Why? Because Agents don't just generate text—they make meaningful decisions and take actions that change the environment around them. Think about it: When a AI customer support agent says "Your refund is processed!" how do you know if it actually: 1️⃣ Retrieved the correct order 2️⃣ Processed the refund properly 3️⃣ Collected customer feedback Drawing from Pasquale's PhD research on AI system reliability and our experience helping companies deploying agents in the real-world, we're introducing 𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁𝘀: a new framework to evaluate and verify AI agents in production. Similar to legal contracts, Agent Contracts outline obligations and boundaries for complex agentic applications. They let you define and verify at: ▪️ Module-Level: verify the input, action, and output of each agentic operation ▪️ Trace-Level: ensure agents performed all necessary steps in dynamic, multi-step sequences The key insight? We need to measure not just WHAT agents say, but HOW they operate. 🤖 Curious how it works? We break down the framework with real examples in our latest blog post. ➡️ Check it out here: https://lnkd.in/eqwEqRca We're launching a beta for select Agent use cases. If you're building agents with LangGraph (LangChain), CrewAI, AG2, or Swarms, ping us👇 to get early access! #AIAgents #MLOps #EnterpriseAI #LLM #AgentEvaluation

5 components we need to figure out before we deploy AI agents: - Role what's the AI agents job? What do you want it to do? - Trusted data what info does the AI need to do its job? (documents, public data, data from your CDP, ideally a giant secure library of all your company's data from various platforms/lakes/databases) - Actions all the things your AI agent can actually do (sending emails? updating records?) - Guard rails you control actions with guard rails, keep AI in check. What can it do and not do? - Channels meeting your customers and your team where they are, actually interacting wherever you choose to deploy it Once each of these is watertight, AI is ready to deliver on its promise 📸 : TB to San Fran, what a trip where AI inspiration was on tap

For the past six months, I've been deep in the world of AI, working closely with multiple enterprises to build intelligent AI agents. It’s been an exciting journey with huge potential – and some tough challenges. One recurring issue I’ve seen is that AI’s effectiveness often hits a wall because data isn’t stored cleanly or is scattered across different systems. This makes retrieval difficult and can lead to inaccurate answers – a common pain point across the industry. With my experience at Uber Customer Obsession Engineering, where we built a contact router and orchestrator that ensured tickets reached the right agent, I started to wonder: what if we could route the right context to the right AI agent in the same way? But to make that possible, the data itself needs to be in a structured, usable format. So, we’re building a Knowledge Processor – a solution designed to organize data across multiple formats, enabling cross-referencing and validation. Once the data is clean and organized, we can focus on "context routing" to make sure each AI agent gets the precise information it needs to respond accurately. But that’s just part of the equation. We’re also building continuous learning into the system, using user queries and feedback to help the AI adapt and improve over time. Each interaction refines its understanding, making it smarter, more accurate, and better aligned with user needs. Excited to finally be sharing this journey and looking forward to what’s next! https://www.qinch.ai/

Measuring AI Agent ROI can be nebulous. Equip yourself with the key learnings from this session before crafting your business case at you organization. Tahsim Ahmed and Denys Linkov get into best practices you should consider before measuring ROI as well as our key learnings from the creation and iteration of our own bot - Tico.

I've been having more and more conversations on GenAI recently (shocking, I know!). Most of these conversations are with C-Suite and Ops leaders looking to 'future proof' themselves or are feeling FOMO now that even their phone, headphones, watch, toothbrush (yes, toothbrush) comes with AI! It's particularly interesting to hear about why they want to adopt and what the business thinks they will be able to do with the technology. For the most part, the opinion is that a chatbot is the extent of the solution. News flash: this is probably the most basic use case (however definitely a step in the right direction). Generative AI is fast becoming an enterprise change maker and it won't be long before the team structure is totally unrecognisable. Dataiku released a feature set last year dubbed 'LLM Mesh' and I think it could be one of the most powerful tools on the market right now for organisations looking to deploy Generative AI across the business. https://lnkd.in/exiYWkwm

The intersection of AI and business processes is getting more exciting each day. DocuSign and Elastic are stepping up the game by enhancing their generative contract and search solutions. DocuSign's Insight leverages AI to analyze agreements, uncovering both risks and opportunities that may otherwise go unnoticed. Meanwhile, Elastic's Elasticsearch Relevance Engine integrates advanced AI and vector search technology to refine search relevance, supporting a range of generative AI initiatives. These innovations represent a significant leap forward in streamlining operations and mitigating risks, providing businesses with more intelligent tools to navigate complex data environments. #BusinessAutomation #ArtificialIntelligence #DocuSign #Elastic #DataAnalysis #SearchSolutions Visit https://workflo.agency for your edge in automation. Schedule a free consultation today to see how automation can transform your business processes—missing out could mean falling behind.

92% of businesses are using AI to drive growth, but even the best AI can't work with bad data. If your data is siloed, inconsistent, or outdated, your AI strategy will fall flat. A trusted data infrastructure is critical to your AI strategy.