Bear

Bear is a San Francisco-based technology company that develops a specialized marketing platform designed to help businesses capture leads and optimize their digital visibility directly within artificial intelligence search engines. The enterprise software manages the entire customer acquisition funnel, enabling business-to-business organizations to systematically track, analyze, and influence how their specific products are recommended by generative systems like ChatGPT and Perplexity. Operating with a core workforce of four employees, the early-stage startup currently supports an active user base of more than 60 different companies utilizing its proprietary platform for lead generation. Bear is financially backed by the startup accelerator Y Combinator, working closely with partner Pete Koomen, and provides its artificial intelligence optimization services to notable technology customers including Browserbase, Wispr Flow, and Cal.com. The company was founded in 2025 by Siddhant Paliwal and Janak Sunil.

# Bear: Marketing Stack for AI Agents

High-Level Overview

Bear is a marketing intelligence platform designed specifically for the AI agent era. The company helps B2B SaaS and technology firms capture and convert high-intent leads generated through AI systems like ChatGPT, Perplexity, and Claude Code.[1] Rather than building another traditional marketing tool, Bear addresses a critical gap: marketers lack visibility into how often their products get recommended by AI agents versus competitors, and they have no systematic way to optimize for AI-driven discovery.

The platform serves as a bridge between the emerging AI agent economy and traditional go-to-market strategies. With 60+ companies already using the platform—including YC portfolio companies like Browserbase, Wispr Flow, and Cal.com—Bear is establishing itself as essential infrastructure for companies seeking to thrive in a world where AI systems increasingly mediate customer discovery.[1] The company operates on a freemium model with a 7-day free trial, democratizing access to AI agent marketing data that was previously unavailable to most organizations.

Origin Story

Bear was founded by Janak and Siddhant, two founders with complementary technical and business backgrounds.[1] Janak brings enterprise experience from his time at Coinbase, combined with entrepreneurial credibility from founding UCLA's largest apartment listing platform, which was acquired in 2024. Siddhant comes from a deep technical pedigree: he was an early engineer at Third Chair (YC X25) and Intel, and he founded his first company—a parking application—at age 15, an achievement that earned him an invitation to the UN.[1]

The idea emerged from a fundamental observation: as AI agents became mainstream tools for research and decision-making, companies suddenly faced a new discovery channel they couldn't measure or optimize for. Unlike traditional search engine optimization or paid advertising, there was no playbook for getting recommended by ChatGPT or Claude. The founders recognized that this represented both a massive blind spot for marketers and an opportunity to build the first platform that could systematize AI agent marketing. Bear launched through Y Combinator, positioning itself at the intersection of two powerful trends: the explosion of AI agent adoption and the persistent need for B2B SaaS companies to generate qualified leads.

Core Differentiators

Data Collection and Competitive Intelligence

Bear's primary differentiator is its ability to aggregate and analyze AI agent recommendation patterns at scale.[1] The platform tracks when a company's product appears in AI-generated answers, when competitors show up, which sources contribute to those answers, and the exact prompts that triggered the recommendations. This creates a real-time competitive intelligence layer that didn't exist before—marketers can now see which companies are winning the AI recommendation game and why.

Content Cloning and Optimization

Rather than forcing marketers to guess what AI systems prefer, Bear enables them to identify high-performing content from competitors and replicate it.[1] For example, if a competitor's blog post consistently appears as a source for ChatGPT recommendations on a particular topic, Bear makes it straightforward for your team to create similar content optimized for the same queries. This transforms content strategy from intuition-based to data-driven.

Automated Outreach to Influential Sources

Bear identifies which sources (journalists, bloggers, researchers) are most frequently cited by AI agents for specific topics, then automates outreach on behalf of the user.[1] If CNET is a frequently cited source for "best laptops 2025," Bear finds the author and initiates contact—removing friction from the relationship-building process that drives media coverage and AI recommendations.

Accessibility and Freemium Model

By offering free access with a 7-day trial, Bear lowers the barrier to entry for startups and smaller SaaS companies that might not have large marketing budgets.[1] This approach accelerates adoption and builds network effects, as more companies using the platform generate more data that makes the platform more valuable for all users.

Role in the Broader Tech Landscape

Bear operates at a critical inflection point in how software discovery works. The rise of AI agents represents a fundamental shift in how users find and evaluate products—moving from search engines and app stores to conversational AI systems that synthesize information and make recommendations.[2] This transition is not speculative; it's already happening. McKinsey's 2025 Global Survey on AI found that 62% of organizations are experimenting with AI agents, and 23% are already scaling agentic systems in at least one business function.[4]

For B2B SaaS companies, this creates an urgent problem: traditional marketing playbooks—SEO, paid search, content marketing—were optimized for a world where humans directly searched for solutions. In the AI agent era, the customer journey is mediated by autonomous systems that operate on different logic. Bear's platform essentially translates between these two worlds, helping companies understand and optimize for AI-driven discovery while maintaining their existing marketing infrastructure.

The timing is particularly favorable because the market is still in early innings. Most organizations are experimenting rather than scaling, which means there's a window for platforms like Bear to establish themselves as the standard infrastructure before the market consolidates.[4] Additionally, as software vendors like Workday integrate AI agents into their own platforms, the demand for specialized AI marketing tools will likely increase—companies will need both the enterprise suite and the specialized tools that help them win in specific channels.[3]

Bear also influences the broader ecosystem by making AI agent marketing accessible to startups and mid-market companies, not just enterprises with dedicated marketing operations teams. This democratization effect accelerates the adoption of AI-driven go-to-market strategies across the startup ecosystem, which in turn creates more data and more sophisticated use cases for the platform.

Quick Take & Future Outlook

Bear is well-positioned to become the essential marketing infrastructure for the AI agent era. The company has identified a genuine market gap—one that will only grow more acute as AI agents become the primary discovery mechanism for software—and has built a product that directly addresses it. The founding team's combination of enterprise experience (Janak at Coinbase), technical depth (Siddhant at Intel and as an early-stage founder), and YC backing provides credibility and resources to execute at scale.

Looking ahead, several trends will shape Bear's trajectory. First, as AI agents mature and become more widely deployed across enterprises, the volume and sophistication of AI-driven recommendations will increase exponentially, making Bear's data more valuable.[4] Second, the competitive intensity around AI agent optimization will likely drive consolidation—companies that don't optimize for AI discovery will lose market share to those that do, creating urgency for Bear's platform. Third, as the market matures, Bear may expand beyond content and outreach optimization into predictive analytics, helping companies anticipate which topics and formats will trend with AI systems before competitors do.

The broader question is whether Bear remains a specialized point solution or becomes a foundational layer in the marketing stack. Given that AI agents are reshaping how software gets discovered—a shift as significant as the rise of search engines or social media—there's a plausible path for Bear to evolve into a category-defining platform. For now, the company is executing on a clear thesis: in a world where AI agents mediate discovery, marketers need new tools to compete. Bear is building those tools, and the market is just beginning to recognize the urgency.[5]