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Watermarking and Forensics for AI Models, Data, and Deep Neural Networks

In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source code, covering problems such as noise filtering, high-dimensional curve fitting, and predictive analytics. One of the

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What if you could build a secure, scalable RAG+LLM system – no GPU, no latency, no hallucinations?

In this session, Vincent Granville shares how to engineer high-performance, agentic multi-LLMs from scratch using Python. Learn how to rethink everything from token chunking to sub-LLM selection to create AI systems that are explainable, efficient, and designed for enterprise-scale applications.

What you’ll learn:

🔹How to build LLM systems without deep neural nets or GPUs
🔹 Real-time fine-tuning, self-tuning, and context-aware retrieval
🔹 Best practices in chunking, crawling, and UI design
🔹 A case study using financial reports from Nvidia

With Vincent Granville, Co-founder & AI Lead at BondingAI.io.

A serial founder, author, and former post-doc at Cambridge, Vincent’s work spans open-source tools, Fortune 100 deployments, and millions of downloads.

Watch the video, here. See also Vincent’s YouTube video on “Scaling, Optimization & Cost Reduction for LLM/RAG & Enterprise AI”, here. For other podcasts and webinars by Vincent Granville, visit our podcasts section, here. Books on the topic are available here

Related articles:

🔹 How to Get AI to Deliver Superior ROI, Faster – link.
🔹 Benchmarking xLLM and Specialized Language Models – link.
🔹 Doing Better with Less: LLM 2.0 for Enterprise – link.
🔹 How to Design LLMs that Don’t Need Prompt Engineering – link.
🔹 From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution – link.
🔹 10 Must-Read Articles and Books About Next-Gen AI in 2025 – link.

Live session with Vincent Granville, Chief AI Architect and Co-founder at BondingAI.

Scaling databases is a tricky balance. Teams need speed and reliability, but costs keep rising. From runaway infrastructure bills to overprovisioned clusters and slow queries, companies often spend more without seeing better performance. Join for a practical session on how to reduce database total cost of ownership (TCO) without sacrificing performance. Vincent will share strategies that leading organizations are using to control costs, optimize systems, and scale efficiently in the context of Enterprise AI.

You will learn where most teams overspend, how to optimize resources, and how to scale smarter. Along the way, you will hear examples of businesses that consolidated systems, reduced overhead, and improved performance. The presentation will feature efficient optimization techniques including best kept secrets in the context of no-Blackbox specialized language models (LLMs, SLMs, RAG) for enterprise.

What You Will Learn:

  • The biggest drivers behind high database TCO and how to address them
  • Approaches to cut infrastructure, licensing, and operational costs
  • Speeding up deployment in the context of trustworthy & secure AI for enterprise
  • Performance tuning techniques that prevent overprovisioning
  • Examples of teams that achieved more scalability with fewer resources

Audience

CTOs, engineering leaders, database Admins, finance and IT decision-makers. If you are responsible for technical strategy or budget alignment, this webinar will give you insights you can put into action right away. Recording will be available to participants who cannot attend the live event due to schedule conflicts.

PowerPoint presentation available here.

Recent Articles

Watermarking and Forensics for AI Models, Data, and Deep Neural Networks

In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source

Video: the LLM 2.0 Revolution

What if you could build a secure, scalable RAG+LLM system – no GPU, no latency, no hallucinations? In this session, Vincent Granville shares how to engineer high-performance, agentic multi-LLMs from

Scaling, Optimization & Cost Reduction for LLM/RAG & Enterprise AI

Live session with Vincent Granville, Chief AI Architect and Co-founder at BondingAI. Scaling databases is a tricky balance. Teams need speed and reliability, but costs keep rising. From

Benchmarking xLLM and Specialized Language Models: New Approach & Results

Standard benchmarking techniques using LLM as a judge have strong limitations. First it creates a circular loop and reflects the flaws present in the AI judges. Then, the

BondingAI Joining Forces with Top Law Firm to Secure Game-Changing AI Technology

BondingAI.io, the leading company for hallucination-free and secure Enterprise AI, is proud to announce our partnership with law firm SankerIP to protect and secure our unique AI technology.

Stay Ahead of AI Risks – Free Live Session for Tech Leaders

Exclusive working session about trustworthy AI, for senior tech leaders. View PowerPoint presentation, here. ​AI isn’t slowing down, but poorly planned AI adoption will slow you down. Hallucinations,

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