A cybersecurity industry expert approached Eventum with an ambitious concept for an innovative AI-powered cybersecurity solution. Facing a tight deadline to prepare a critical document for submission to federal funding agencies, the client required immediate expert validation and refinement.
The client’s detailed project document needed rapid technical review to identify potential inaccuracies or naive assumptions related to complex AI methodologies and implementations. The timeline was pressing—the client needed the revised document within 24 hours.
An Eventum engineer with deep expertise in large language models (LLMs) and cybersecurity applications quickly stepped in. Within less than 24 hours, our engineer meticulously reviewed the extensive document, identified areas needing technical adjustment, and provided clear, actionable revisions and recommendations.
The client received a refined, technically accurate document within the stringent timeframe. This expedited turnaround enabled timely submission for critical funding opportunities. The client praised Eventum’s exceptional speed and precision, underscoring our ability to deliver expert-level results on tight deadlines.
This engagement highlights Eventum’s agility, precision, and unmatched responsiveness, making us the preferred partner for innovators seeking to rapidly advance ambitious AI-driven projects.
See how Eventum helped launch an app into the App Store in 4 months, currently going viral Generative AI next gen social media.
Read the Case Study on Plai Labs
Learn how Eventum helped DeepCell reduce manual oversight by 90%, provided R&D roadmapping & mentorship of ML team
Read the Case Study on DeepCell
Learn how to hire your AI team, ranging from role types & expectations, matching positions to project requirements, and interview structures.
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At Eventum, we've spent countless hours optimizing Retrieval-Augmented Generation (RAG) systems, and we've learned one critical thing: chunking is absolutely essential to getting it right. It might sound trivial—just breaking documents into smaller bits—but chunking fundamentally shapes the quality, accuracy, and reliability of your AI’s responses. Here's what we've found matters most when tackling chunking for RAG.
Building out an ML product can feel like a race to experiment, train models, and iterate quickly. Often, startups allocate GPUs and spin up cloud infrastructure without much thought to optimization—until sky-high bills spark a scramble for cost savings. Below are ten practical ways to keep your machine learning systems lean, efficient, and scalable from the start.
Perhaps the most surprising aspect of the evolution of the Transformer architecture for language modeling is how consistent it has remained over the past near-decade
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