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RandomWalk

Leading The World’s AI Integration

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Leading The World’s AI Integration

AI tools to choose with expert guidance from Random Walk. We collaborate and implement custom solutions.

We provide AI integration. Our engineers are experts at fast tracking integration between any software and AI tools.

From business functions like marketing, HR and finance to different industries like retail and pharma, find the right AI tool.

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Let us work with you to integrate niche AI tools into your day-to-day business functions like HR, marketing, and finance. We also cater various industry-specific AI tools to aid operations.

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1-bit LLMs: The Future of Efficient and Accessible Enterprise AI

As data grows, enterprises face challenges in managing their knowledge systems. While Large Language Models (LLMs) like GPT-4 excel in understanding and generating text, they require substantial computational resources, often needing hundreds of gigabytes of memory and costly GPU hardware. This poses a significant barrier for many organizations, alongside concerns about data privacy and operational costs. As a result, many enterprises find it difficult to utilize the AI capabilities essential for staying competitive, as current LLMs are often technically and financially out of reach.

1-bit LLMs: The Future of Efficient and Accessible Enterprise AI

AI-Powered vs. Traditional Sponsorship Monitoring: Which is Better?

Picture this: You, a brand manager, are at a packed stadium, the crowd's roaring, and suddenly you spot your brand's logo flashing across the giant screen. Your heart races, but then a nagging question hits you: "How do I know if this sponsorship is actually worth the investment?" As brands invest millions in sponsorships, the need for accurate, timely, and insightful monitoring has never been greater. But here's the million-dollar question: Is the traditional approach to sponsorship monitoring still cutting it, or is AI-powered monitoring the new MVP? Let's see how these two methods stack up against each other for brand detection in the high-stakes arena of sports sponsorship.

AI-Powered vs. Traditional Sponsorship Monitoring: Which is Better?

GuideLine: RAG-Enhanced HRMS for Smarter Workflows

Human Resources Management Systems (HRMS) often struggle with efficiently managing and retrieving valuable information from unstructured data, such as policy documents, emails, and PDFs, while ensuring the integration of structured data like employee records. This challenge limits the ability to provide contextually relevant, accurate, and easily accessible information to employees, hindering overall efficiency and knowledge management within organizations.

GuideLine: RAG-Enhanced HRMS for Smarter Workflows

Exploring Different Text-to-Speech (TTS) Models: From Robotic to Natural Voices

Text-to-speech (TTS) technology has evolved significantly in the past few years, enabling one to convert simple text to spoken words with remarkable accuracy and naturalness. From simple robotic voices to sophisticated, human-like speech synthesis, models offer specialized capabilities applicable to different use cases. In this blog, we will explore how different TTS models generate speech from text as well as compare their capabilities, models explored include MARS-5, Parler-TTS, Tortoise-TTS, MetaVoice-1B, Coqui TTS among others. The TTS process generally involves several key steps discussed later in detail: input text and reference audio, text processing, voice synthesis and then the final audio is outputted. Some models enhance this process by supporting few-shot or zero-shot learning, where a new voice can be generated based on minimal reference audio. Let's delve into how some of the leading TTS models perform these tasks.

Exploring Different Text-to-Speech (TTS) Models: From Robotic to Natural Voices

Is Your Job Next? The AI Takeover Is Here, but Don't Panic... Yet.

Let's face it - we're all a bit on edge about this whole AI thing, aren't we? It feels like every other day there's a new headline about robots taking over jobs or AI outsmarting humans. And you've probably caught yourself wondering, "Is my job next on the chopping block?" Well, let's figure out what's really going on in this brave new world of AI. Trust me, it's not all doom and gloom - but it's definitely time to pay attention.

Is Your Job Next? The AI Takeover Is Here, but Don't Panic... Yet.
1-bit LLMs: The Future of Efficient and Accessible Enterprise AI

1-bit LLMs: The Future of Efficient and Accessible Enterprise AI

As data grows, enterprises face challenges in managing their knowledge systems. While Large Language Models (LLMs) like GPT-4 excel in understanding and generating text, they require substantial computational resources, often needing hundreds of gigabytes of memory and costly GPU hardware. This poses a significant barrier for many organizations, alongside concerns about data privacy and operational costs. As a result, many enterprises find it difficult to utilize the AI capabilities essential for staying competitive, as current LLMs are often technically and financially out of reach.

AI-Powered vs. Traditional Sponsorship Monitoring: Which is Better?

AI-Powered vs. Traditional Sponsorship Monitoring: Which is Better?

Picture this: You, a brand manager, are at a packed stadium, the crowd's roaring, and suddenly you spot your brand's logo flashing across the giant screen. Your heart races, but then a nagging question hits you: "How do I know if this sponsorship is actually worth the investment?" As brands invest millions in sponsorships, the need for accurate, timely, and insightful monitoring has never been greater. But here's the million-dollar question: Is the traditional approach to sponsorship monitoring still cutting it, or is AI-powered monitoring the new MVP? Let's see how these two methods stack up against each other for brand detection in the high-stakes arena of sports sponsorship.

GuideLine: RAG-Enhanced HRMS for Smarter Workflows

GuideLine: RAG-Enhanced HRMS for Smarter Workflows

Human Resources Management Systems (HRMS) often struggle with efficiently managing and retrieving valuable information from unstructured data, such as policy documents, emails, and PDFs, while ensuring the integration of structured data like employee records. This challenge limits the ability to provide contextually relevant, accurate, and easily accessible information to employees, hindering overall efficiency and knowledge management within organizations.

Exploring Different Text-to-Speech (TTS) Models: From Robotic to Natural Voices

Exploring Different Text-to-Speech (TTS) Models: From Robotic to Natural Voices

Text-to-speech (TTS) technology has evolved significantly in the past few years, enabling one to convert simple text to spoken words with remarkable accuracy and naturalness. From simple robotic voices to sophisticated, human-like speech synthesis, models offer specialized capabilities applicable to different use cases. In this blog, we will explore how different TTS models generate speech from text as well as compare their capabilities, models explored include MARS-5, Parler-TTS, Tortoise-TTS, MetaVoice-1B, Coqui TTS among others. The TTS process generally involves several key steps discussed later in detail: input text and reference audio, text processing, voice synthesis and then the final audio is outputted. Some models enhance this process by supporting few-shot or zero-shot learning, where a new voice can be generated based on minimal reference audio. Let's delve into how some of the leading TTS models perform these tasks.

Is Your Job Next? The AI Takeover Is Here, but Don't Panic... Yet.

Is Your Job Next? The AI Takeover Is Here, but Don't Panic... Yet.

Let's face it - we're all a bit on edge about this whole AI thing, aren't we? It feels like every other day there's a new headline about robots taking over jobs or AI outsmarting humans. And you've probably caught yourself wondering, "Is my job next on the chopping block?" Well, let's figure out what's really going on in this brave new world of AI. Trust me, it's not all doom and gloom - but it's definitely time to pay attention.

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