With our AI consulting services , you now have the ability to choose from an entire gamut of AI tools and solutions to deliver quick results.
Choose the ideal AI tools for your business with expert guidance from Random Walk, where we collaborate to implement custom AI solutions.
Our engineers specialize in seamless AI integration, accelerating connections between AI tools and existing software.
From business functions like marketing, HR and finance to different industries like retail and pharma, find the right AI tool.
If you are a client looking for AI capabilities,
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 to various industry-specific AI tools to aid operations.
Random Walk’s AI learning endeavors to unravel the mysteries surrounding AI, ensuring it’s not a black box. We offer enhanced learning through industry-oriented sessions, employing a seamless mix of learning models. Explore our AI workshops and corporate AI training to empower your employees with valuable skills. Our AI Training and AI consulting services for executives ensures a straightforward and effective learning experience.
Benefit from our experienced AI professionals, flexible on-demand resources, minimal upfront investment along with rapid development and integration capabilities.
Identify gaps between your AI aspirations and current digital capabilities. Get actionable insights with a prioritized roadmap to bridge those gaps. Empower leaders to make data-driven decisions on AI adoption and digital transformation.
Discover your AI readiness in just 15 minutes with our AI Readiness & Digital Maturity Assessment tool.
Brand Sponsorship Analytics Platform powered by AI, to measure the value you are getting from your sponsorship spend. Analyze relevant metrics of brand logo visibility and strategize your marketing spend in three simple steps in 5 minutes.
Fortune Cookie is a cutting-edge Secure Knowledge Model reshaping organizational data handling. By seamlessly integrating unstructured and structured data, it empowers efficient data organization, retrieval, and insightful decision-making.
Identify gaps between your AI aspirations and current digital capabilities. Get actionable insights with a prioritized roadmap to bridge those gaps. Empower leaders to make data-driven decisions on AI adoption and digital transformation.
Discover your AI readiness in just 15 minutes with our AI Readiness & Digital Maturity Assessment tool.
Brand Sponsorship Analytics Platform powered by AI, to measure the value you are getting from your sponsorship spend. Analyze relevant metrics of brand logo visibility and strategize your marketing spend in three simple steps in 5 minutes.
Fortune Cookies is a cutting-edge Secure Knowledge Model reshaping organizational data handling. By seamlessly integrating unstructured and structured data, it empowers efficient data organization, retrieval, and insightful decision-making.
Meet our two star detectives at the YOLO Detective Agency: the seasoned veteran Detective YOLOv8 (68M neural connections) and the efficient rookie Detective YOLOv11 (60M neural pathways). Today, they're facing their ultimate challenge: finding Waldo in a series of increasingly complex scenes.
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.
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.
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.
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.
Meet our two star detectives at the YOLO Detective Agency: the seasoned veteran Detective YOLOv8 (68M neural connections) and the efficient rookie Detective YOLOv11 (60M neural pathways). Today, they're facing their ultimate challenge: finding Waldo in a series of increasingly complex scenes.
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.
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.
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.
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.
