Research SaaS and AI-tool competitors in a real browser. Visit competitor sites, pricing pages, feature pages, and review platforms to extract pricing, features, positioning, and customer sentiment, then return a structured comparison report. Use when the user wants competitor analysis, market landscape research, pricing comparisons, feature comparisons, or review synthesis.
Research SaaS and AI-tool competitors in a real browser. Visit competitor sites, pricing pages, feature pages, and review platforms to extract pricing, features, positioning, and customer sentiment, then return a structured comparison report. Use when the user wants competitor analysis, market landscape research, pricing comparisons, feature comparisons, or review synthesis.
category
marketing
Competitor Researcher
You research competitors and turn messy product pages into a structured market comparison. This skill is read-only: observe, extract, compare, and report. Do not sign in, submit forms, or mutate any site state.
Tool Selection Rule
Prefer existing tools first: If a competitor page is public and renders well without a browser, use normal web fetches or other available tools first.
Use Hanzi only when the browser is actually needed: JavaScript-rendered pricing tables, tabbed feature sections, lazy-loaded reviews, anti-bot protections, or other pages that do not work reliably with plain HTTP tools.
Stay read-only: Do not create accounts, start trials, submit lead forms, or click any CTA that would change external state.
Before Starting — Preflight Check
Try calling browser_status to verify the browser extension is reachable. If the tool doesn't exist or returns an error:
Hanzi isn't set up yet. This skill needs the hanzi browser extension running in Chrome.
home page, pricing, features, integrations, enterprise, docs
Review platforms
G2, Capterra, Product Hunt
Supporting evidence
blog, changelog, docs, comparison pages
If the competitor list is not provided, discover a short list first by reading public comparison pages and review listings, then confirm with the user before continuing.
Phase 2: Gather Official Product Data
For each competitor, collect the following from public product pages:
Pricing — plan names, list prices, usage limits, free tier, free trial, enterprise/contact-sales positioning
Features — core features, standout capabilities, integrations, AI features, compliance/security claims
Social proof — customer logos, testimonials, usage numbers, case studies, badges
Prefer plain fetches for simple pages. Use browser_start when pricing tables or feature pages require a real browser.
Browser extraction prompt pattern
When Hanzi is needed, use a task like:
Visit this competitor's public site and extract structured product information. Read the home page, pricing page, and feature page if available. Return: company name, target customer, headline, subheadline, plan names, prices, billing details, key features, integrations, AI-specific claims, social proof, and any enterprise/contact-sales positioning. Expand tabs or accordions if needed, but do not sign up or submit forms.
If a site has multiple pricing toggles or tabs:
Read monthly and annual pricing when available
Note which values are hidden behind "contact sales"
Call out usage-based pricing separately from seat-based pricing
If browser_start times out:
Call browser_screenshot to see where it got stuck
Retry once with a tighter task focused on just the missing page
If it still fails, record the limitation and move on
Phase 3: Gather Review Sentiment
Review sites are often the reason a real browser helps. For each competitor, check whichever of these are available:
G2
Capterra
Product Hunt
Extract:
Average rating if visible
Review count if visible
Repeated positives
Repeated complaints
Notable buyer segments or use cases
Do not try to summarize every review. Instead, synthesize recurring themes.
Review synthesis rules
Use at least 3 review signals per competitor when available
Separate strengths from complaints
Prefer recent or clearly visible feedback over old buried content
If review data is sparse, say so explicitly instead of guessing
Phase 4: Compare and Normalize
Once extraction is complete, normalize competitors into the same categories so the output is easy to compare.
indie, SMB, mid-market, enterprise, developer teams
Core strength
what they emphasize most
Differentiators
what appears unique or especially strong
Weaknesses / gaps
what is absent, unclear, or criticized in reviews
Review sentiment
recurring praise and recurring complaints
If the user asked for custom dimensions, include those too.
Phase 5: Output the Research Report
Always produce two parts:
1. Structured comparison table
Use a table like this:
Competitor
Entry Price
Pricing Model
Best For
Core Strength
Key Gaps
Review Sentiment
ExampleCo
$29/mo
seat-based
SMB teams
strong workflow automation
weak reporting
praised for ease of use, criticized for pricing
2. Positioning and market summary
After the table, summarize:
How each competitor positions itself
Which competitors compete most directly with the target product
Where pricing clusters or diverges
Which features are becoming table stakes
What review themes repeat across the market
What whitespace or differentiation opportunities appear
Output template
Competitor Research Report
Target product: {product}
Competitors researched: {N}
Sources used: official sites, pricing pages, feature pages, {review sites}
[comparison table]
Positioning differences
- Competitor A positions around ...
- Competitor B positions around ...
Market insights
- Pricing trend:
- Feature trend:
- Review pattern:
- Opportunity:
Limitations
- Competitor C blocked browser access on its pricing page
- Competitor D had no public review profile on G2/Capterra
If the user asked for a short answer, compress the summary but keep the table.
Example Output
The following example shows how to transform the raw browser findings above into the final report format described in Phase 5.
Sources used: official sites, pricing pages or plans pages, Product Hunt review pages
Competitor
Entry Price
Pricing Model
Best For
Core Strength
Key Gaps
Review Sentiment
Browser Use
$75/mo
free + credits + usage-based + enterprise
teams that want cost-efficient browser-agent automation with stealth and high concurrency
browser-agent automation with detailed usage pricing and strong concurrency
pricing page is complex and mixes plan, credits, and per-step or per-token costs
praised for automation capabilities and dependable agent support; no repeated public complaints were visible
Skyvern
$29/mo
free + credits + seat-like tiers + enterprise
developers, ops teams, and regulated enterprise workflows
browser-workflow automation with clear concurrency and compliance-oriented tiers
fewer repeated public complaints were visible because Product Hunt sentiment is still sparse
praised for browser automation and complex workflow handling; only visible criticism was that the product is still early stage
Browserbase
$20/mo
free + monthly tiers + enterprise
solo builders, startups, and enterprise teams running cloud browsers for AI
cloud browser infrastructure that scales cleanly from builder to enterprise use
public feedback is strongly positive but still light on repeated negatives
praised for easy integration, scalable infrastructure, and simple AI-browser workflows; only isolated requests for more tutorials and customization were visible
browserless
$25/mo
free + annual tiers + usage overages + enterprise
teams running browser automation at scale with Playwright or Puppeteer
managed browser automation infrastructure with transparent unit-based pricing and compliance options
heavier plans get expensive quickly and public review volume is low
praised for reliability, Chrome compatibility, and rendering automation; no repeated public complaints were visible
Browser Cash
$0.09/hour
pure usage-based
AI builders and enterprises needing real-browser nodes and async automation
real-browser network for AI systems with usage-based pricing and low boot times
no public Product Hunt review sentiment was visible yet
no public review score or sentiment visible on Product Hunt yet
Positioning differences
Browser Use positions around making web automation easy and cost-efficient for browser agents.
Skyvern positions around replacing brittle scripts and manual browser workflows with an AI agent platform.
Browserbase positions around being the cloud browser layer for AI products and teams.
browserless positions around transparent, scalable browser automation infrastructure for developers and teams.
Browser Cash positions around giving AI systems internet intelligence through a network of real browser nodes.
Market insights
Pricing trend: this market mixes flat monthly plans with strongly usage-based pricing, and several products make concurrency, credits, proxies, or token costs part of the core commercial model.
Feature trend: core differentiation clusters around stealth or anti-bot reliability, concurrency, enterprise security controls, human-in-the-loop workflows, and browser infrastructure that AI agents can use without brittle custom scripting.
Review pattern: visible public sentiment consistently rewards reliability, ease of integration, and strong automation outcomes; repeated public complaints are still sparse for some newer products, which itself is a signal that review coverage is immature in this category.
Opportunity: a product that combines real-user-browser access, clearer pricing, reliable agent workflows, and stronger publicly visible user trust signals would stand out in this market.
Limitations
This example report uses browser-agent-adjacent competitors that were validated through public pages and public review surfaces visible at the time of testing.
Some products in this category have sparse public review coverage, so absence of repeated complaints may reflect limited review volume rather than universally positive sentiment.
Example Validation
The following real-world validation focuses on a browser-agent-adjacent set that is closer to Hanzi's market.
Browser-agent-adjacent validation set
Validation BA1 — Browser Use pricing and positioning
Source:https://browser-use.com/pricing
Observed results:
Positioning: Easiest way to automate the web and Cheapest browser agent
Target customer: teams that need browser automation capacity and high concurrency
Visible pricing:
Free: $0/month
Subscription: $75/month visible public plan, with additional usage-based costs and credits ranges shown in the pricing table
Enterprise: custom / contact sales
Additional notes:
The page mixes flat plans, credit ranges, session pricing, token pricing, and other usage-based charges
The page repeatedly emphasizes concurrency, stealth mode, and browser-agent economics
