Perplexity's monthly active users in India amounted to around *** million as of the second quarter of 2025. The AI startup saw a whopping *** percent year-on-year increase in MAUs in the country. In fact, India is the platform's leading market in terms of MAUs.

perplexity.ai is ranked #54 in IN with 276.5M Traffic. Categories: AI. Learn more about website traffic, market share, and more!

The number of downloads of the search-focused AI platform, Perplexity, in India amounted to about *** million, as of the second quarter of 2025. The AI startup saw a whopping *** percent year-on-year growth in downloads in the country. Perplexity seeks to leverage the Indian market and its sizable user base in a bid to surpass its rival OpenAI, which has already secured the lead in the United States.

Google and Perplexity started offering their tools for free to some Indian users in July 2025.

In 2024, OpenAI's ChatGPT was by far the most widely used AI-powered tool among developers over the past year, with 82 percent of developers reporting regular usage. GitHub Copilot ranked second at 44 percent, while Google Gemini came in third at 22 percent. Other notable tools included Bing AI and Visual Studio Intellicode, both of which are owned by Microsoft. Tools such as Claude and Perplexity AI saw lower but still notable usage rates. Traditional tools like WolframAlpha maintained a steady user base at four percent, overtaking newer tools such as Meta AI and Amazon Q.

This dataset contains the predicted prices of the asset Perplexity tokenized stock (PreStocks) over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Word-level valid and test perplexity on WikiText-2.

Human Preferences Alignment KTO Dataset of AI Service User Reviews of ChatGPT Gemini Claude Perplexity

  Introduction to Human Preferences Alignment

There are many methods of applying Human Preference Alignment techniques to help model align in the supervised finetuning stage, including RLHF Reinforcement Learning from Human Feedback(paper), PPO Proximal policy optimization(paper/equation), DPO Direct Preference Optimization (paper/equation), KTO Kahneman-Tversky… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/Human-Preferences-Alignment-KTO-Dataset-AI-Services-Genuine-User-Reviews.

AI in Consumer Decision-Making: Global Zero-Party Dataset

This dataset captures how consumers around the world are using AI tools like ChatGPT, Perplexity, Gemini, Claude, and Copilot to guide their purchase decisions. It spans multiple product categories, demographics, and geographies, mapping the emerging role of AI as a decision-making companion across the consumer journey.

What Makes This Dataset Unique

Unlike datasets inferred from digital traces or modeled from third-party assumptions, this collection is built entirely on zero-party data: direct responses from consumers who voluntarily share their habits and preferences. That means the insights come straight from the people making the purchases, ensuring unmatched accuracy and relevance.

For FMCG leaders, retailers, and financial services strategists, this dataset provides the missing piece: visibility into how often consumers are letting AI shape their decisions, and where that influence is strongest.

Dataset Structure

Each record is enriched with: Product Category – from high-consideration items like electronics to daily staples such as groceries and snacks. AI Tool Used – identifying whether consumers turn to ChatGPT, Gemini, Perplexity, Claude, or Copilot. Influence Level – the percentage of consumers in a given context who rely on AI to guide their choices. Demographics – generational breakdowns from Gen Z through Boomers. Geographic Detail – city- and country-level coverage across Africa, LATAM, Asia, Europe, and North America.

This structure allows filtering and comparison across categories, age groups, and markets, giving users a multidimensional view of AI’s impact on purchasing.

Why It Matters

AI has become a trusted voice in consumers’ daily lives. From meal planning to product comparisons, many people now consult AI before making a purchase—often without realizing how much it shapes the options they consider. For brands, this means that the path to purchase increasingly runs through an AI filter.

This dataset provides a comprehensive view of that hidden step in the consumer journey, enabling decision-makers to quantify: How much AI shapes consumer thinking before they even reach the shelf or checkout. Which product categories are most influenced by AI consultation. How adoption varies by geography and generation. Which AI platforms are most commonly trusted by consumers.

Opportunities for Business Leaders

FMCG & Retail Brands: Understand where AI-driven decision-making is already reshaping category competition. Marketers: Identify demographic segments most likely to consult AI, enabling targeted strategies. Retailers: Align assortments and promotions with the purchase patterns influenced by AI queries. Investors & Innovators: Gauge market readiness for AI-integrated commerce solutions.

The dataset doesn’t just describe what’s happening—it opens doors to the “so what” questions that define strategy. Which categories are becoming algorithm-driven? Which markets are shifting fastest? Where is the opportunity to get ahead of competitors in an AI-shaped funnel?

Why Now

Consumer AI adoption is no longer a forecast; it is a daily behavior. Just as search engines once rewrote the rules of marketing, conversational AI is quietly rewriting how consumers decide what to buy. This dataset offers an early, detailed view into that change, giving brands the ability to act while competitors are still guessing.

What You Get

Users gain: A global, city-level view of AI adoption in consumer decision-making. Cross-category comparability to see where AI influence is strongest and weakest. Generational breakdowns that show how adoption differs between younger and older cohorts. AI platform analysis, highlighting how tool preferences vary by region and category. Every row is powered by zero-party input, ensuring the insights reflect actual consumer behavior—not modeled assumptions.

How It’s Used

Leverage this data to:

Validate strategies before entering new markets or categories. Benchmark competitors on AI readiness and influence. Identify growth opportunities in categories where AI-driven recommendations are rapidly shaping decisions. Anticipate risks where brand visibility could be disrupted by algorithmic mediation.

Core Insights

The full dataset reveals: Surprising adoption curves across categories where AI wasn’t expected to play a role. Geographic pockets where AI has already become a standard step in purchase decisions. Demographic contrasts showing who trusts AI most—and where skepticism still holds. Clear differences between AI platforms and the consumer profiles most drawn to each.

These patterns are not visible in traditional retail data, sales reports, or survey summaries. They are only captured here, directly from the consumers themselves.

Summary

Winning in FMCG and retail today means more than getting on shelves, capturing price points, or running promotions. It means understanding the invisible algorithms consumers are ...

Introduction

In 2024, the Global AI Search Engine Market was valued at USD 17.3 billion and is projected to reach nearly USD 73.7 billion by 2034, expanding at a CAGR of 15.6% during 2025–2034. The growth is driven by the increasing integration of generative AI, natural language processing, and voice-enabled search technologies across industries. Organizations are investing heavily in AI-based platforms to enhance search relevance, improve personalization, and support multilingual accessibility, which is accelerating adoption worldwide.

One of the top driving factors behind this market’s growth is the explosion of digital data and the need for intelligent processing to sort through vast volumes efficiently. Users now want search results that are tailored to their unique preferences and context, which AI technologies like deep learning enable. Another important driver is the increased adoption of cloud computing, providing the infrastructure needed to support scalable AI search solutions. The rise of voice assistants and visual search methods also expands how users interact with these search engines, contributing further to demand.

Demand for AI search engines shows significant growth not only among consumers but also enterprises. Consumers rely on AI search for everyday needs such as finding local services or translating languages, while enterprises use AI-powered search tools to quickly access complex data across departments, improving operational efficiency and decision-making. Large enterprises lead adoption due to their need for handling immense data, but smaller companies are catching up through affordable, cloud-based options that streamline customer support and research.

https://market.us/wp-content/uploads/2025/09/AI-Search-Engine-Market.png" alt="AI Search Engine Market" width="1216" height="706">

In 2024, about 45% of U.S. adults used AI-powered search engines at least once a month, showing that mainstream adoption has already begun. Perplexity.ai emerged as a strong example, growing from 10 million monthly active users in mid-2023 to over 30 million by Q1 2025, reflecting the pace of adoption. Globally, around 27% of enterprises integrated AI search tools into their internal systems, while 51% of Gen Z turned to AI platforms for academic queries. In India, more than 60% of AI search traffic came from mobile, and 38% of users trusted AI search results over traditional engines.

The marketing landscape has also been reshaped by AI-driven search adoption. According to Gitnux, 61% of marketers saw increases in organic traffic due to AI, while 72% considered it central to future strategies. Nearly 95% of customer interactions are expected to be managed without humans by 2025, and by 2024 about 80% of search queries were projected to be handled by AI-powered agents. This signals a structural shift in how companies approach engagement, customer support, and visibility online.

Among smaller businesses, adoption momentum is accelerating, with 55% planning to expand AI investment in search marketing. AI-powered personalization is playing a key role in this trend, helping improve customer targeting and increasing conversion rates by up to 20%. Together, these figures illustrate how AI-driven search is not only transforming consumer behavior but also redefining enterprise strategies, marking a rapid global shift toward automated and intelligent engagement.

Perplexity of different initializations and improvement strategies.

ChatGPT's monthly active users in India grossed just under ** million as of the second quarter of 2025. This was a growth of *** percent over the same period in 2024. While ChatGPT was the leading AI platform used in India in terms of downloads and MAUs, Perplexity was the fastest growing of the two.

Description

Introducing dataset consisting of gpt4 answers to users requests. Queries were taken from allenai/WildChat-1M and causal-lm/instructions. Texts (requests and responses) were deleted in 3 cases:

either has non-english letters and special symbols either has http-links either has html blocks either has perplexity more than 1.5*IQR + third quantile ( in some cases average perplexity value of sentences or maximum value was used )

ChatGPT dominated the U.S. text generation AI market in 2024 with ** percent of users, far surpassing competitors like Google Bard (Gemini) at **** percent. QuillBot and DeepL each held **** percent, while tools such as Claude and Perplexity had minimal adoption below *** percent.

Word-level valid and test perplexity on PTB.

Geometriqs Global80 Prompt–Response Dataset

  Overview

This dataset contains the complete set of prompt–response pairs used in the GenAI Positioning Study: Global80 (November 2025).It captures how three leading generative-AI platforms — OpenAI ChatGPT (GPT-4), Google Gemini, and Perplexity AI — respond to a controlled set of neutral, comparative questions about 80 of the world’s largest companies.
The purpose is to measure model behaviour, not user behaviour: how these… See the full description on the dataset page: https://huggingface.co/datasets/geometriqs/global80_prompt-response-pairs.

The dataset contains the output of experiments on a research project on Vulnerability of LLMs in Educational Assessment. The Dataset contains: -the students assignments data in normal form and the injected form -the output produced by the experimented LLMs: ChatGPT, Gemini, DeepSeek, Grok, Perplexity and Copilot for the experiments evaluation the assignments, as a single document and collectively as a group of documents, denominated: -User Legitimate LLMs Prompts -Normal (no injection) providing the reference base evaluation -Prompt Injection Pass, one type of injection experiments, called Fail-To-Top, to move an assignment evailuated FAIL by reference base evaluation to PASS, i.e. above 35% of total points. -Prompt Injection to Top25 , a type of injection experiments to move to top 25% an assignment with lowe reference base evaluation . This latter type of experiment come in 3 versions, Fail-To-Top, Sat-To-Top, Good-To-Top where assignment with reference base evaluation respectively: Fail (below 35%), Satisfactory (greater than 25% and belo 50%) and Good (above 50% and below 75%) are considered for injection. The name of the folders and output results files are accordingly self-explanatory .

The AI office software market is experiencing explosive growth, driven by increasing demand for automation, enhanced productivity, and intelligent data analysis within businesses of all sizes. The market, currently valued at approximately $15 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $75 billion by 2033. This surge is fueled by several key trends, including the rising adoption of cloud-based solutions, the integration of advanced AI capabilities like natural language processing (NLP) and machine learning (ML) into existing office suites, and the growing need for intelligent automation to streamline workflows and reduce operational costs. Key drivers include the increasing availability of affordable AI solutions, improved user experience, and the demonstrated ROI from increased efficiency and reduced human error. While data security concerns and the need for skilled professionals to implement and manage these systems present challenges, the overall market outlook remains strongly positive. The segment landscape is diverse, encompassing various applications such as AI-powered writing assistants (e.g., Anyword, Jasper, ChatGPT), search and research tools (e.g., Arc Search, Perplexity), video editing software (e.g., Descript, Runway, Wondershare Filmora), and AI-driven automation platforms (e.g., Zapier). Competition is fierce, with established tech giants like Google and Meta alongside innovative startups vying for market share. North America currently holds the largest regional market share due to early adoption and technological advancements, but the Asia-Pacific region is anticipated to exhibit significant growth in the coming years, driven by rapid digital transformation and increasing investment in AI technologies. Strategic partnerships, acquisitions, and continuous innovation in AI algorithms and user interfaces will be critical for companies to maintain their competitive edge in this dynamic and rapidly evolving market.

The AI-based Recommendation Engine market is projected to reach USD 3,226 million by 2033, growing at a CAGR of XX% from 2025 to 2033. This growth is attributed to the increasing adoption of AI technologies by businesses to enhance customer engagement and drive sales. AI-based recommendation engines analyze user data to provide personalized product or content recommendations, leading to increased user satisfaction and conversion rates. Key market drivers include the rise of e-commerce, the growing use of social media, and the need for businesses to deliver personalized experiences to customers. The market is segmented into types (collaborative filtering, content-based filtering, hybrid recommendation), applications (e-commerce platforms, finance, social media, others), and regions. Major players in the market include Microsoft, Google, Andi Search, Metaphor AI, Brave, Phind, Perplexity AI, NeevaAI, Qubit, and Dynamic Yield. North America is the largest regional market, followed by Europe and Asia Pacific. Key trends in the market include the integration of AI-based recommendation engines with machine learning and natural language processing, the adoption of real-time recommendations, and the use of recommendation engines to drive cross-selling and upselling.

Readability scores for Chatgpt-4o, Gemini, and Perplexity responses to the most frequently asked Ankylosing spondylitis -related questions, and a statistical comparison of the text content to a 6th-grade reading level [Median, 95% Confidence Interval (CI) (Lower limit of confidence interval- Upper limit of confidence interval)].