DTC Brand Teardown — Ecommerce Battlecard (AI)

Give it a DTC brand's domain, get back a one-page competitive teardown. This Actor is an ecommerce competitive-intelligence super-actor: for every brand domain you hand it, it orchestrates five research organs — store platform, martech stack, catalog sample, firmographics and recent news — and uses OpenAI to write a sharp ecommerce battlecard. Store platform and installed apps, martech and pixels, catalog breadth and price tiers, growth signals, strengths, weaknesses, recent moves, and exactly how to compete with them.

You bring the domain. It hands back a finished battlecard you can paste straight into a deck, a sales call sheet, or a pitch.

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The flow

┌─────────────────────────────┐
                      │  noniner/shopify-store-analyzer │ → platform · apps · maturity
                      ├─────────────────────────────┤
                      │ noniner/b2b-tech-stack-detector │ → martech · analytics · pixels
  brand domain  ──────┼─────────────────────────────┼──────▶  AI teardown
  (gymshark.com)      │ noniner/product-catalog-extract.│ → price range · breadth       (one-page
                      ├─────────────────────────────┤                                 battlecard +
                      │  noniner/ai-company-enrichment  │ → brand · who they target       markdown)
                      ├─────────────────────────────┤
                      │   noniner/google-news-scraper   │ → recent moves
                      └─────────────────────────────┘

1. Paste one or more brand domains (e.g. gymshark.com, allbirds.com).
2. The Actor fans out to five organs — each organ is run once across all your domains, so adding brands stays cheap.
3. OpenAI synthesizes one grounded teardown per brand — strictly from what the organs returned. If an organ has no data, that section is simply left out; nothing is invented.

Organs degrade gracefully: if a sub-actor is unavailable or returns nothing, the teardown is still written from whatever organs did return, and sourcesUsed tells you exactly what was used.

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What you get per brand

For every brand domain, the Actor returns:

1. brandOverview — who the brand is and what it sells, 1–2 factual sentences.
2. category + positioning — the vertical and how they position themselves.
3. productStrategy — catalog breadth and price tiers, inferred from a product sample.
4. pricingStrategy — one sentence summarising their price strategy from the sampled price range.
5. martechStack — the store apps plus martech/analytics/pixels, summarised.
6. growthSignals — concrete signals of scale (growth apps, retargeting pixels, broad catalog, recent moves).
7. strengths / weaknesses — evidence-based bullets about them as an ecommerce operator.
8. recentMoves — recent headlines from the news organ ([] if none).
9. howToCompete — concrete angles to beat or sell against them (framed to your offering when you provide one).
10. opportunities — gaps a competitor or vendor could exploit (a missing app category, a weak price tier, a stack they lack).
11. oneLineSummary — one punchy sentence capturing the brand's ecommerce angle.
12. teardownMarkdown — the whole teardown as a clean one-page markdown doc, ready to paste.

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Three ways people use it

• DTC competitive analysis — tear down the brands you compete with: store platform, installed apps, catalog breadth, price tiers and growth signals, so you know where you out-execute them and where they out-execute you.
• Ecommerce agency pitches — walk into a pitch with a finished battlecard on the prospect and their rivals: "here's your competitor's stack, here's the gap, here's how we close it."
• Shopify app & tool vendor battlecards — see which apps a brand already runs, spot the missing category, and build a "how to compete with <brand>" / market-positioning one-pager for every target account.

Whether you're doing DTC competitive analysis, pulling together ecommerce agency pitches, building Shopify app vendor battlecards, or just need to know how to compete with a specific brand, this Actor turns a bare domain into market positioning you can act on.

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How the orchestration works

Each organ is a proven public Actor on the No Niner account. This super-actor calls them and fuses the result:

Organ	Actor	What it contributes
Store platform	noniner/shopify-store-analyzer	Ecommerce platform, installed apps, catalog size, store maturity.
Technographics	noniner/b2b-tech-stack-detector	Martech, analytics and tracking pixels on the storefront.
Catalog sample	noniner/product-catalog-extractor	A small product sample → price range and breadth signal (kept low to bound cost).
Firmographics	noniner/ai-company-enrichment	Brand description, who they target, size signal.
Recent moves	noniner/google-news-scraper	Recent brand headlines and snippets.

Organs can return a redirected domain (for example brand.myshopify.com instead of brand.com); results are matched back on the registrable root, not the exact string, so the right data always lands on the right brand.

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Input

Field	Type	Notes
domains	array	Required. Brand domains, store URLs or emails — the domain is extracted. One per line.
yourOffering	string	What YOU sell. If set, howToCompete and opportunities are framed to beat each brand relative to your offering.
maxResults	integer	Max brands to tear down. Default 25, max 300. One charge per brand.
maxConcurrency	integer	How many brands to synthesize in parallel. Default 3, max 8.
model	string	OpenAI model. Default gpt-4o-mini.
useStore	boolean	Call the store-platform organ. Default true.
useTech	boolean	Call the technographic organ. Default true.
useCatalog	boolean	Call the catalog-sample organ. Default true.
useEnrichment	boolean	Call the firmographic enrichment organ. Default true.
useNews	boolean	Call the recent-news organ. Default true.

Minimal input example

{
  "domains": ["gymshark.com", "allbirds.com"],
  "yourOffering": "We sell a Shopify retention and subscriptions app"
}

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Output example

{
  "domain": "gymshark.com",
  "brandName": "Gymshark",
  "oneLineSummary": "A fitness-apparel juggernaut that turned influencer-led community into a global activewear brand.",
  "category": "activewear / fitness apparel",
  "positioning": "Positions itself as the community-first training-apparel brand built with and for a young fitness audience.",
  "productStrategy": "Broad mid-market activewear range spanning leggings, tops and accessories across a wide price band.",
  "pricingStrategy": "Mid-market pricing that undercuts premium athleisure while staying above fast-fashion.",
  "martechStack": "Runs a mature storefront with analytics and retargeting pixels plus conversion and email/retention apps.",
  "growthSignals": ["Retargeting pixels installed", "Broad multi-category catalog", "Active product-launch cadence"],
  "strengths": ["Strong brand community", "Broad, frequently refreshed catalog"],
  "weaknesses": ["Heavy reliance on discounting", "Crowded activewear category"],
  "recentMoves": ["Expanded into new retail and market segments"],
  "howToCompete": ["Lead with superior retention economics", "Target the post-purchase experience they underinvest in"],
  "opportunities": ["No dedicated subscription/replenishment motion detected — an opening for a retention app"],
  "teardownMarkdown": "# DTC Brand Teardown — Gymshark\n**Domain:** gymshark.com\n**Category:** activewear\n\n> A fitness-apparel juggernaut...\n\n## Overview\n...",
  "sourcesUsed": ["shopify-store-analyzer", "b2b-tech-stack-detector", "product-catalog-extractor", "ai-company-enrichment", "google-news-scraper"]
}

sourcesUsed always reflects reality — if an organ was unavailable, it simply won't appear, and the teardown is written from the rest.

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Pay-per-event pricing

This Actor uses pay-per-event billing with one headline event:

Event	Price	Charged when
brand-teardown	$0.30	Once per brand successfully torn down. Bundles up to five orchestrated sub-actor runs (store, technographic, catalog, firmographic, news) plus the OpenAI teardown synthesis into one finished battlecard.

The Actor logs its OpenAI token usage and the estimated AI cost at the end of every run, so the event price always stays safely above the model cost.

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Setup

The AI synthesis calls the OpenAI API. Set the environment variable / secret OPENAI_API_KEY on the Actor. Without it, the Actor cannot write the teardown.

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Use it from an AI agent (Apify MCP server)

This Actor is available through the Apify MCP server, so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it a list of brand domains and read back the finished teardowns — with no custom integration code.

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Run it locally

npm install
# put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...   # required for the AI teardown
apify run -p

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Related actors

• Shopify Store Analyzer — Detect an ecommerce store's platform, installed apps, catalog size and maturity.
• Product Catalog Extractor — Pull the product catalog (titles, prices, variants) from an online store.
• B2B Competitor Teardown — The B2B/SaaS sibling: positioning, tech stack, pricing and how to win.
• B2B Tech Stack Detector — Detect the technologies, martech and pixels a website runs on.

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Built by the No Niner team.