ARGUS Sales Battlecard¶

Competitive Intelligence for Sales Teams¶

Last Updated: January 2026 Classification: Internal Sales Use Only

Quick Reference Card¶

Our Elevator Pitch (30 seconds)¶

"Argus is the only AI testing platform that actually understands your codebase. While competitors just look at the DOM to fix broken tests, we read your git history and source code—giving us 99.9% healing accuracy versus the industry's 95%. Plus, our multi-model AI routing cuts testing costs by 60-80%. It's like having a senior QA engineer who knows every line of your code."

Three Things to Always Mention¶

1. Git-aware self-healing (99.9% accuracy) - Nobody else has this
2. 60-80% cost savings with multi-model AI routing
3. Full-stack testing (UI + API + DB) from plain English

When We Win¶

• Customer values test maintenance reduction
• Engineering team is frustrated with flaky tests
• Cost control and predictability matter
• They want codebase-aware intelligence
• Modern stack (React, FastAPI, etc.)

When We Lose¶

• Need 3000+ browser/device combinations today
• Legacy systems requiring specific integrations
• Already deeply invested in competitor ecosystem

Deployment Options - Addressing Customer Concerns¶

"What if the customer doesn't want to share their code?"¶

Key Message: Git-aware healing is a PREMIUM feature, not a requirement. Argus works great with ZERO code access.

Operating Modes¶

Mode 1: DOM-Only (No Code Access)¶

What we analyze:
✓ Live DOM structure of running app
✓ Visual screenshots (AI vision)
✓ Element attributes, text content, positions
✓ Historical test runs (our database)

What we DON'T need:
✗ Source code repository
✗ Git history
✗ Build artifacts

Still get:
✓ AI test generation from plain English
✓ Visual regression testing
✓ Self-healing at 90-95% (industry standard)
✓ Multi-model cost savings (60-80%)
✓ Full UI/API/DB testing capabilities

Talk Track (DOM-Only):

"You don't need to share any code with Argus. Our DOM-only mode analyzes your running app and delivers 90-95% healing accuracy—matching or exceeding Mabl, testRigor, and Katalon. Git-aware healing is an optional upgrade for teams who want to push from 95% to 99.9%."

Mode 2: Git-Aware (Read-Only Access)¶

Security guarantees:
• READ-ONLY access (never writes to repo)
• Code analyzed in memory, never stored permanently
• SOC2 compliant, encrypted in transit
• Revoke access anytime via GitHub/GitLab settings

What it enables:
• 99.9% healing accuracy
• Knows exactly which commit broke a test
• Understands component renames during refactors
• Provides developer attribution ("Fixed by Sarah in commit abc123")

Talk Track (Git-Aware):

"If you want the full 99.9% healing accuracy, we just need read-only access to your repo—same as any CI/CD tool like GitHub Actions or CircleCI. We never write code, never store it permanently. It's analyzed in memory and discarded. You can revoke access anytime."

Mode 3: Self-Hosted / On-Premise (Enterprise)¶

For customers with strict security requirements:
• Runs entirely in customer's infrastructure
• Code NEVER leaves their network
• Full git-aware features
• Air-gapped deployment available
• Customer controls all data

Talk Track (Self-Hosted):

"For customers with strict data residency or security requirements, we offer a self-hosted deployment. Argus runs entirely in your infrastructure—your code never leaves your network. You get full git-aware healing with complete data control."

Feature Availability by Mode¶

Objection Handling - Code Access Concerns¶

Competitor Battlecards¶

VS APPLITOOLS¶

Applitools Profile¶

Head-to-Head Comparison¶

Why We Win Against Applitools¶

Key Differentiators: 1. Full-stack testing - Applitools is visual-only; we test UI, API, and database 2. 10x cheaper - $99/mo vs $969/mo for comparable features 3. NLP test creation - Write tests in plain English 4. Self-healing - Our git-aware healing is far superior

Talk Track:

"Applitools is excellent for visual regression, but it's only one piece of the puzzle. You still need separate tools for functional, API, and database testing. With Argus, you get visual AI PLUS full-stack testing PLUS intelligent self-healing—all at 1/10^th the price."

Objection Handling¶

Killer Questions to Ask Prospect¶

• "How much are you spending on Applitools annually?"
• "What tools do you use for API and database testing alongside Applitools?"
• "How do you handle functional test maintenance today?"
• "When a test breaks, how long does it take to diagnose if it's visual vs functional?"

VS TESTRIGOR¶

testRigor Profile¶

Head-to-Head Comparison¶

Why We Win Against testRigor¶

Key Differentiators: 1. 99.9% vs 90% healing accuracy - Our git-aware approach beats DOM-only 2. Codebase understanding - We know WHY things changed, not just THAT they changed 3. Multi-model cost savings - 60-80% lower AI costs 4. Explanation of fixes - We show the git commit, author, and reason

Talk Track:

"testRigor pioneered plain English testing, and we offer the same capability. But when tests break, testRigor guesses based on DOM structure—90% accuracy. We read your git history to understand exactly what changed and why—99.9% accuracy. That 10% difference means the difference between a maintenance headache and a maintenance-free experience."

Objection Handling¶

Killer Questions to Ask Prospect¶

• "When a test breaks, how do you know if it's a real bug or just a selector change?"
• "How often do you have to manually investigate 'healed' tests to verify they're correct?"
• "What's your false positive rate on test failures?"
• "Do you know which developer's commit broke a test?"

VS MABL¶

Mabl Profile¶

Head-to-Head Comparison¶

Why We Win Against Mabl¶

Key Differentiators: 1. Git-aware healing - Mabl uses ML on DOM; we use source code + git 2. Full-stack testing - Mabl is weak on API, nonexistent on DB 3. Production integration - We learn from Datadog/Sentry; they don't 4. 60% cheaper - $99/mo vs $249/mo

Talk Track:

"Mabl has a great UI and decent self-healing—about 95% accuracy. But they're limited to what they can see in the DOM. When your team refactors a component name, Mabl's healing fails because it doesn't understand code. We read your git history and know 'LoginButton was renamed to AuthButton in commit abc123 by Sarah.' That's why we hit 99.9% accuracy."

Objection Handling¶

Killer Questions to Ask Prospect¶

• "What happens when your team refactors component names? How does Mabl handle that?"
• "How do you test your API endpoints alongside UI tests?"
• "Do your tests know which features real users actually use most?"
• "What's included in your $249/month tier vs what's add-on?"

VS KATALON¶

Katalon Profile¶

Head-to-Head Comparison¶

Why We Win Against Katalon¶

Key Differentiators: 1. AI-native vs AI-bolted - Katalon added AI to traditional automation; we built AI-first 2. Zero to tests in minutes - Katalon requires significant setup and training 3. True autonomy - We auto-discover and generate tests; Katalon requires manual creation 4. Multi-model cost optimization - They use single model; we optimize across providers

Talk Track:

"Katalon is a comprehensive platform with a long history. But their AI is bolted onto traditional test automation—it's not architected for autonomous testing. We're AI-native. Give us your URL and we'll auto-discover and generate a test suite. With Katalon, you're still manually building tests, just with some AI assistance."

Objection Handling¶

Killer Questions to Ask Prospect¶

• "How long did it take to get your team productive on Katalon?"
• "What percentage of your tests are auto-generated vs manually created?"
• "How does Katalon handle test creation for new features?"
• "When a test fails, how quickly can you determine root cause?"

VS BROWSERSTACK¶

BrowserStack Profile¶

Head-to-Head Comparison¶

Why We Win Against BrowserStack¶

Key Differentiators: 1. Intelligence vs Infrastructure - They provide browsers; we provide brains 2. Test generation - They run your tests; we create your tests 3. Self-healing - We fix broken tests; they just report failures 4. Complementary positioning - We can use BrowserStack for device coverage

Talk Track:

"BrowserStack is excellent infrastructure—20K+ real devices is impressive. But they're a 'dumb pipe'—you write tests, they run them. We're the intelligence layer: we generate tests, heal them when they break, and learn from production. Many customers use both: Argus for AI intelligence, BrowserStack for device coverage when needed."

Objection Handling¶

Killer Questions to Ask Prospect¶

• "How much time does your team spend writing and maintaining tests vs running them?"
• "If BrowserStack finds a failure, how long does root cause analysis take?"
• "How do you handle test maintenance when your UI changes?"
• "Do you have tests covering all the devices you can run on?"

VS LAMBDATEST / KANEAI¶

LambdaTest/KaneAI Profile¶

Head-to-Head Comparison¶

Why We Win Against LambdaTest/KaneAI¶

Key Differentiators: 1. 2x more AI agents - 23 specialized agents vs their 10 2. Git-aware healing - They use DOM-only; we use source code 3. Multi-model cost optimization - They use single model; we route intelligently 4. Deeper codebase analysis - We understand your full stack

Talk Track:

"KaneAI is a strong competitor—they've built 10 AI agents and have good NLP. We have 23 specialized agents and the same NLP capability. The key difference is healing: they use DOM analysis like everyone else. We're the only platform that reads git history to understand why selectors changed. That's why our healing accuracy is 99.9% vs their ~95%."

Objection Handling¶

Killer Questions to Ask Prospect¶

• "How many of those 3000 browsers do you actually use?"
• "When tests break, does KaneAI tell you which commit caused it?"
• "How do they handle component renames during refactoring?"
• "What's the false positive rate on their self-healing?"

Pricing Comparison Matrix¶

Price Positioning¶

• vs Applitools: 90% cheaper for more capabilities
• vs Mabl: 60% cheaper with better healing
• vs Katalon: Similar price, 10x easier to use
• vs BrowserStack: Complementary (intelligence + infrastructure)

Feature Comparison Matrix¶

Feature                    Argus  Appli  testR  Mabl  Katal  BrwSt  KaneAI
─────────────────────────  ─────  ─────  ─────  ────  ─────  ─────  ──────
NLP Test Creation          ●●●●●  ○○○○○  ●●●●●  ●●○○○ ●●○○○  ●○○○○  ●●●●○
Visual AI Testing          ●●●●○  ●●●●●  ●●○○○  ●●●●○ ●●●○○  ●●●●○  ●●●○○
Self-Healing               ●●●●●  ●●○○○  ●●●○○  ●●●●○ ●●●○○  ●●○○○  ●●●●○
Healing Accuracy           99.9%  ~85%   ~90%   ~95%  ~90%   ~80%   ~95%
Git-Aware Healing          ●●●●●  ○○○○○  ○○○○○  ○○○○○ ○○○○○  ○○○○○  ○○○○○
Codebase Analysis          ●●●●●  ○○○○○  ○○○○○  ○○○○○ ●●○○○  ○○○○○  ●●●○○
API Testing                ●●●●○  ○○○○○  ●●●○○  ●●○○○ ●●●●○  ●●○○○  ●●●○○
Database Testing           ●●●●○  ○○○○○  ○○○○○  ○○○○○ ●●●○○  ○○○○○  ●●●○○
Multi-Model AI             ●●●●●  ○○○○○  ○○○○○  ○○○○○ ○○○○○  ○○○○○  ○○○○○
Cost Optimization          ●●●●●  ●●○○○  ●●●○○  ●●●○○ ●●●●○  ●●●○○  ●●●○○
Production Learning        ●●●●●  ○○○○○  ○○○○○  ●●○○○ ○○○○○  ○○○○○  ○○○○○
IDE Integration (MCP)      ●●●●●  ○○○○○  ○○○○○  ○○○○○ ○○○○○  ○○○○○  ○○○○○
Device Coverage            ●●○○○  ●●●○○  ●●●●○  ●●●○○ ●●●●○  ●●●●●  ●●●●●
Enterprise Features        ●●●○○  ●●●●○  ●●●○○  ●●●○○ ●●●●●  ●●●●●  ●●●●○

● = Strong   ○ = Weak/None

Quick Reference: When to Lead With What¶

Lead with Git-Aware Healing when:¶

• Prospect mentions test flakiness
• They've tried self-healing tools before and were disappointed
• Engineering team does frequent refactoring
• They ask "how is your healing different?"

Lead with Multi-Model Cost Savings when:¶

• Budget is a primary concern
• They've had bad experiences with AI cost overruns
• CFO or finance is involved in decision
• They ask about pricing model

Lead with NLP Test Creation when:¶

• Non-technical stakeholders will write tests
• They want to reduce QA bottleneck
• Current test creation is slow
• They mention testRigor as alternative

Lead with Full-Stack Testing when:¶

• They currently use multiple testing tools
• API and database testing are pain points
• They want to consolidate vendors
• Microservices architecture

Lead with Production Learning when:¶

• They use Datadog/Sentry/similar
• Test prioritization is a challenge
• They mention "testing what matters"
• Release velocity is a concern

Competitive Landmines to Set¶

Against Applitools¶

"When you talk to Applitools, ask them how they handle functional test failures—not just visual differences. And ask about their API testing capabilities."

Against testRigor¶

"When evaluating testRigor, ask what happens when your team refactors a component name. Ask them to show you the git commit that caused a selector change."

Against Mabl¶

"When you demo Mabl, ask them to show you a healed test and explain WHY the selector changed—not just that it did. Ask about their API testing story."

Against Katalon¶

"When evaluating Katalon, time how long it takes to create your first test. Ask about auto-discovery—can it generate tests from just a URL?"

Against BrowserStack¶

"BrowserStack is great infrastructure. Ask them: once they find a failure, how do they help you fix it? Do they generate tests or just run them?"

Handling "Why Haven't I Heard of You?"¶

Response Framework:

"Fair question. We're a newer entrant focused on getting the technology right before scaling go-to-market. The established players like Applitools and Mabl have been around 7-8 years, but their architectures are fundamentally limited—they can't do git-aware healing or multi-model optimization because they weren't built for it. We built from scratch with AI-native architecture. The tradeoff is brand awareness, but the technology advantages are significant. Our early customers chose us specifically because we solve problems the established players can't."

Closing Checklist¶

Before ending any competitive deal, ensure you've:

• Demonstrated git-aware healing with their actual codebase
• Shown multi-model cost comparison
• Addressed their #1 pain point directly
• Set landmines against competitors they're evaluating
• Established clear next steps and timeline
• Identified economic buyer and technical champion
• Offered pilot/POC with success criteria

Contact for Competitive Support¶

For deal-specific competitive intelligence or custom battlecard requests: - Sales Engineering: [Contact] - Product Marketing: [Contact] - Competitive Intel Slack: #competitive-intel

Document Version 1.0 | Updated January 2026 | Internal Use Only