TrueFoundry

A big one for the team....Finally, our RAG framework built while working with various enterprises is now open-source. We have native integrations with Qdrant and SingleStore as of now. Over the last one week, a lot of you have tried this out & given great inputs! We appreciate the feedback & critique! (Leave your thoughts in the comments - let’s keep this ‘open source’ too) PS - Repo & UI link are in comments

The relationship between enterprises and their service providers is going through a revolution as firms figure out new ways of delivering more for less, and a new wave of disruptors emerges to drive the switch from labor arbitrage to #technologyarbitrage. 💲Services firms must find ways of delivering value to enterprises with generative AI (#GenAI) and machine learning (ML). This drive will result in services provided to enterprises by software (technology arbitrage) rather than people (labor arbitrage). 🌊 A new wave of tech firms has arrived to put the idea into practice, offering enterprises an opportunity to have bots do much of the work traditionally performed by offshore centers and onshore consultants. Their arrival pressures service providers to respond with similar technology arbitrage offerings. 🔗 Follow this link for examples, what will drive the scale and pace of impact, and how enterprise leaders should respond: https://lnkd.in/emSYudGh Saurabh Gupta Phil Fersht Thomas Reuner Joel M. Tony Filippone Hridika Biswas Rhino.ai Eclypses TrueFoundry Globality, Inc. C3 AI Customer.io Builder.ai 10Web.io Moveworks Inflection AI (Pi), Pryon DataRobot Alteryx Akkio QuantPi Adept Knorket.AI, rabbit inc.

I'm excited to share about this session that is happening at "The Fifth Elephant 2024 Annual Conference" Building and Deploying LLM Applications: From Concept to Production - AMA with Mixture-of-Experts Session 🧠 (https://lnkd.in/gfuHrS8M) *Motivation:* Building LLM applications is about getting a dataset, indexing it in a Vector db, slapping an OpenAI wrapper, and packaging it in Gradio. And LangChain can do the majority of this work. Boom! You are ready to deliver a product in 10 minutes. This happy workflow is the popular narrative. But real life throws many curveballs at you and it is not as simple as it was made to believe. We have seen it all with run-of-the-mill chat-with-your-PDF apps - only to be left frustrated, the moment we want to scale to more varied documents! Many difficult design & build choices have to be made that require asking general but foundational questions such as: What to solve - problem formulation? Why LLMs? How to solve - problem breakup, task prioritization, timelines & deliverables, team composition, etc. What to optimize and trade-off: cost, performance, correctness,  What does an MVP look like for my problem? Buy vs Build? When to switch from commercial models to local models? Wrappers or custom models?  How to handle private data? Is using the open-source LLMs the only way to handle PII data? Can the proprietary LLMs handle it? How to evaluate - the system and its parts, A/B testing, and evaluation? Should one start with RAG, fine-tuning right away or use frameworks like DSPY or prompt engineering? How does one solve the cold start problem and generate synthetic datasets? How does one incorporate Agentic workflows and design patterns? Is it feasible to leverage the existing frameworks or just roll out one? How does one monitor the costs, latencies and relevant metrics with LLMs ? What are the SLAs and how to achieve them? How are LLMs being applied in verticals such as Health, Education, Healthcare, Deep tech, FinTech, and Agriculture, among others, and in both for-profit and not-for-profit settings? Or they can be very specific such as “How do I reduce cost from INR 1/- per conversation to INR 0.1/- per conversation?” You are not alone. Our MoEs have gone through it all. Engage with Mixture-of-Experts to go over the entire dev cycle in different verticals. *The Mixture of Experts (MOE)* Chintan Donda, Senior ML Engineer, Wadhwani AI Sai Nikhilesh Reddy, Associate ML Scientist, Wadhwani AI Pulapakura Sravan, Software Associate, Data Science & Programming, JP Morgan Chase Prathamesh Saraf, Gen AI Backend Engineer, TrueFoundry Rajaswa Patil, Applied AI, Postman Praveen Pankajakshan, Chief Scientist, CropIn AI Lab, CropIn *Host* Soma Dhavala, Founder ML Square, ex-Wadhwani AI. Come over for an enriching discussion spanning various facets of the LLM app life cycle in production. Zainab Harshad Vikram

Since we started Surge in 2019, we've been early believers & partnered with innovative AI companies across the value chain. Every Surge cohort features AI companies, including invideo, a rapidly growing text-to-video in minutes platform & Atlan, which recently raised $105M Series C. 🚀 If you're an AI founder looking for a global community of fellow innovators, apply to join us for Surge 10, kicking off this October in India & wrapping in the US. Apply now ➡️ https://lnkd.in/gw9jJKTD. Applications close on August 15.