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TinyFish

The Enterprise Web Agent company. Our AI agents run complex business workflows at web scale millions of times to deliver measurable outcomes.

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Founded

2024

Location

Palo Alto, CA

Employees

51

Funding

$47M Series A

TinyFish: Enterprise Web Automation Agents

**TinyFish** builds production-grade AI agents that execute complete web workflows across thousands of public websites—discovering, extracting, and acting on data with enterprise reliability. The company positions its stack as an alternative to brittle scripts and legacy RPA, emphasizing outcomes, SLAs, and measurable KPIs over demo-ware. TinyFish emerged publicly in 2025 with a $47M round led by [ICONIQ Growth](https://www.iconiqcapital.com/growth/insights/powering-the-future-of-enterprise-web-agents-our-partnership-with-tinyfish).

  • Website: [tinyfish.ai](https://www.tinyfish.ai)
  • Tagline: The Enterprise Web Agent Company
  • Focus: Enterprise web automation agents for large-scale, high-stakes workflows
  • HQ: Palo Alto, CA
  • Team size: ~52 (LinkedIn)
  • Funding: $47M (Aug 2025) led by ICONIQ Growth; cited participants include USVP and MongoDB Ventures per [Built In SF](https://www.builtinsf.com/articles/tinyfish-launches-raises-47m-series-a-20250822) and [Economic Times](https://m.economictimes.com/tech/funding/ai-agent-startup-tinyfish-raises-47-million-in-iconiq-led-round/articleshow/123409891.cms)
  • ---

    What TinyFish Does

    TinyFish deploys end-to-end “enterprise web agents” that:

  • Discover and navigate complex sites at scale
  • Extract clean, structured data despite changing UIs
  • Execute actions (e.g., form submissions, updates, checks) reliably
  • Feed downstream systems and workflows with validated data and KPIs
  • The engineering philosophy centers on reliability: **codified learning**, deterministic transforms, schema validation, rules/guardrails, and feedback loops to mitigate drift—outlined in TinyFish’s post on [codified learning](https://blog.tinyfish.ai/codified-learning-the-backbone-of-reliable-scalable-enterprise-web-agents-3cc9ba7fd2b3).

    ---

    Key Capabilities

  • End-to-end web workflow automation across thousands of sites
  • Structured data extraction that stays clean under UI and site changes
  • High-throughput, resilient operation with production-grade reliability
  • Outcome-oriented delivery tied to KPIs, SLAs, and enterprise standards
  • ---

    Representative Use Cases

  • Price and promotion tracking across retailer networks
  • Inventory aggregation and availability sync for hospitality and marketplaces
  • Store locator, menu, and content monitoring for multi-location brands
  • Competitor assortment and content change intelligence
  • Lead sourcing and enrichment from public directories and profiles
  • Claims intake and form submissions across third-party portals
  • Compliance and data collection from government or industry sites
  • Real-world anecdotes include hospitality inventory aggregation for small ryokans in Japan to improve Google Travel discoverability, large-scale pricing and inventory monitoring, and brand competitive intelligence—referenced across company and investor posts, including [ICONIQ Growth’s note](https://www.iconiqcapital.com/growth/insights/powering-the-future-of-enterprise-web-agents-our-partnership-with-tinyfish) and a founder post on LinkedIn .

    ---

    Who It’s For

  • Enterprise data, operations, and product teams needing web-scale collection and actions with SLAs
  • Verticals such as marketplaces, e-commerce, travel, local services, recruiting, and financial intelligence
  • Organizations that outgrew DIY scripts, scrapers, or RPA and require resilience when websites change
  • ---

    Why Enterprises Choose TinyFish

  • Reliability as a core differentiator: engineering for drift resistance and deterministic quality controls
  • Outcome-first approach: agents tied to measurable business KPIs, not single-page automations
  • Enterprise motion: discovery, scoping, deployment, and support aligned to SLAs and compliance
  • Credibility signals: investor backing and early customer stories in launch coverage
  • See [ICONIQ Growth](https://www.iconiqcapital.com/growth/insights/powering-the-future-of-enterprise-web-agents-our-partnership-with-tinyfish), [Built In SF](https://www.builtinsf.com/articles/tinyfish-launches-raises-47m-series-a-20250822), and [Economic Times](https://m.economictimes.com/tech/funding/ai-agent-startup-tinyfish-raises-47-million-in-iconiq-led-round/articleshow/123409891.cms)
  • ---

    Market Feedback

    Pros

  • Emphasis on real outcomes and production reliability (highlighted in [launch posts](https://www.linkedin.com/posts/tinyfish-ai_enterprisewebagents-aiagents-activity-7363926403513245700-EES-) and investor write-ups like [ICONIQ Growth](https://www.iconiqcapital.com/growth/insights/powering-the-future-of-enterprise-web-agents-our-partnership-with-tinyfish))
  • Clear enterprise fit with SLAs, compliance posture, and deployment rigor
  • Transparent engineering philosophy (see [codified learning blog](https://blog.tinyfish.ai/codified-learning-the-backbone-of-reliable-scalable-enterprise-web-agents-3cc9ba7fd2b3))
  • Cons

  • Limited public hands-on reviews; no G2 or Capterra listings at time of research
  • No public pricing or self-serve trial, adding friction for quick evaluations
  • Vendor-managed deployments may feel heavy for smaller, build-first teams
  • Additional community chatter: a Reddit thread referencing production agents .

    ---

    Go-To-Market and Pricing

  • Sales-led, enterprise-oriented engagement: discovery → scoping → deployment
  • Outcomes, KPIs, and SLAs are emphasized throughout the process
  • No public pricing page or free trial at this time
  • Learn more via the [TinyFish website](https://www.tinyfish.ai) and [LinkedIn](https://www.linkedin.com/company/tinyfish-ai)
  • ---

    Integrations and Architecture Notes

  • Public materials focus on robust agent operation across the public web rather than named integrations
  • No published integration catalog or SDK list
  • Expect enterprise hookups to data warehouses, queues, and downstream APIs during deployments (details not publicly enumerated)
  • For the engineering approach to reliability and maintenance under change, see TinyFish’s write-up on [codified learning](https://blog.tinyfish.ai/codified-learning-the-backbone-of-reliable-scalable-enterprise-web-agents-3cc9ba7fd2b3).

    ---

    Quick Facts

  • Company: TinyFish
  • Domain: [tinyfish.ai](https://www.tinyfish.ai)
  • Category: Enterprise web automation agents (AI agents for public web data and actions)
  • HQ: Palo Alto, CA
  • Team: ~52 employees (LinkedIn)
  • Funding: $47M (Aug 2025), led by [ICONIQ Growth](https://www.iconiqcapital.com/growth/insights/powering-the-future-of-enterprise-web-agents-our-partnership-with-tinyfish)
  • Go-to-market: Enterprise sales; outcomes and SLAs emphasized
  • Tech pillars: Codified learning, deterministic transforms, schema validation, rules/guardrails, feedback loops
  • ---

    Sources and Further Reading

  • Company site: [TinyFish](https://www.tinyfish.ai)
  • Reliability approach: [Codified Learning blog](https://blog.tinyfish.ai/codified-learning-the-backbone-of-reliable-scalable-enterprise-web-agents-3cc9ba7fd2b3)
  • Investor perspective: [ICONIQ Growth on TinyFish](https://www.iconiqcapital.com/growth/insights/powering-the-future-of-enterprise-web-agents-our-partnership-with-tinyfish)
  • Launch coverage: [Built In SF](https://www.builtinsf.com/articles/tinyfish-launches-raises-47m-series-a-20250822), [Economic Times](https://m.economictimes.com/tech/funding/ai-agent-startup-tinyfish-raises-47-million-in-iconiq-led-round/articleshow/123409891.cms)
  • Company updates: [TinyFish on LinkedIn](https://www.linkedin.com/company/tinyfish-ai)
  • Community discussion: [Reddit thread](https://www.reddit.com/r/AI_Agents/comments/1n0uxxn/real_ai_agents_at_work_not_just_scifi_anymore/)
  • ---

    Tip: If you’re evaluating AI agents for web-scale workflows, compare TinyFish against RPA and scraper stacks on reliability under UI change, schema validation practices, and outcome SLAs—areas where TinyFish’s “codified learning” approach is designed to stand out.

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