Competitive teardowns rot two ways: they take a week nobody has, or they get written from stale memory and screenshots of screenshots. This playbook pairs AI deep research with your own multimodal walkthroughs to produce a claims table — every claim carrying a source URL, a freshness date, and hand-verification on the ones that matter.
Workflow area: Ideation & Critique · Tier: Core · Hands-on time: ~40 minutes
The deep-research modes in Claude, Perplexity and Gemini will happily assemble a competitor analysis in minutes — and that analysis will confidently describe features that shipped, features that were announced and cancelled, and features that exist only in a 2023 blog post. Competitor products change monthly; training data and indexed articles lag by quarters. So the workflow is a pincer: AI research for breadth and market context, your own screenshots and walkthroughs for ground truth, and a claims table that refuses to let the two blur together.
Define the comparison spine first. Pick the five to eight dimensions that matter for your decision — onboarding flow, pricing model, collaboration features, AI capabilities, accessibility posture, platform coverage. A teardown without a spine becomes a feature-list swamp.
Run the deep-research pass for breadth. In Perplexity, Gemini Deep Research, or Claude with web search:
Research [COMPETITOR A, B, C] for a competitive UX teardown.
For each, along these dimensions: [YOUR SPINE]
Rules:
- Every claim must carry a source URL and the date of the source.
- Distinguish SHIPPED (in product today, per source) from ANNOUNCED
(marketing/roadmap) from REPORTED (third-party claims). Label each.
- Flag any claim where sources conflict or are older than 12 months
as LOW CONFIDENCE.
- Include pricing pages, changelogs, release notes and recent
reviews as preferred sources over blog commentary.
- Say "no reliable source found" rather than inferring.
Do your own multimodal walkthrough for ground truth. Sign up for trials of the top two competitors. Screenshot the flows on your spine — onboarding, the core task, settings, empty states. Then run each set through Claude or Gemini: "Here are screenshots of Competitor A's onboarding, captured today. Describe the flow structure, required steps, and moments of friction. What patterns are notable versus common practice?" Your capture date becomes the freshness stamp no article can beat.
Merge into the claims table — the deliverable. One row per claim:
DIM CLAIM COMP STATUS SOURCE DATE VERIFIED
Onboarding No signup required for A SHIPPED own trial 2026-08 HAND ✓
first project
AI "AI layout suggestions" B ANNOUNCED changelog URL 2026-05 —
Pricing Free tier capped at 3 A SHIPPED pricing URL 2026-08 HAND ✓
projects
Collab Real-time multiplayer C REPORTED review URL 2025-11 LOW CONF
Hand-verify the claims your decisions will lean on. Rank claims by decision weight and verify the top ones yourself — open the pricing page, run the flow, click the feature. Anything you could not verify keeps its LOW CONFIDENCE label in every downstream deck. Then write the one-page synthesis: where competitors converge (table stakes), where they diverge (open territory), and the two or three verified gaps your design could own.
Use trial accounts under your own company identity and respect competitors' terms of service — no scraping behind logins, no misrepresenting yourself in sales demos, and no pasting a competitor's paywalled or NDA-covered material into research prompts.
Deep research gives you breadth in minutes and errors at unknown rates — the claims table with source URLs, freshness dates and hand-verified decision-bearing rows is what turns AI-speed competitive intel into something a strategy can safely stand on.