Extractor
Store-level competitor price tracking, MAP compliance, and assortment matching across 1,000+ retailers.
If your problem is 'the same SKU is three prices at three stores and I only see one number,' this is one of the few tools that answers it directly. The store tile view, the ±5% band and the KVI 3× weighting are what you're buying, and the documented 42-SKU run (4,103 listings, 71% of SKUs priced differently by store) is the kind of specificity general scrapers don't give you. Against Bright Data or Oxylabs you get raw pages and build the index yourself; against a national price feed from a data broker you get one number per SKU. The credits model and a $149/mo entry tier mean the free plan is an evaluation footprint, not a working setup. Buy it for pricing, MAP and assortment intelligence
Verified 11d ago · liveness 65/100 · cite: rightaichoice.com/tools/extractor
- Brands selling through Target, Walmart, CVS, Amazon, Sephora or Ulta that need store-level price visibility
- Retail pricing analysts building a KVI-weighted competitor index across 100+ stores and 10k+ SKUs
- Brand protection teams tracking MAP violations and unauthorized marketplace sellers
- Merchandisers analyzing assortment gaps store by store
- Developers who need a public API to pipe competitor data into their own warehouse
- Non-retail industries — B2B directories, SaaS, industrial — outside grocery, pharmacy, hardlines, apparel, beauty and
- Teams that want raw extracted JSON to build their own analytics rather than a pre-built comparison layer
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Extractor if you need raw extracted pages to build your own pipeline, or if your category sits outside grocery, pharmacy, hardlines, apparel, beauty and home — this is a finished retail comparison layer, not a general scraper.
Credits are consumed on every fetch and analysis, so adding stores or raising refresh frequency pushes you past the included allowance even when your product count hasn't changed.
Free covers up to 100 products and 3,500 credits/month, and Starter at $149/mo suits a single-category brand tracking a handful of retailers. Pro at $349/mo fits a multi-category team that needs the 25,000-credit allowance and the sales-volume data, and sits below what a custom retail data feed from a conventional broker typically runs. Custom is priced for enterprise programs with unlimited products and custom integrations.
In short
Extractor — Store-level competitor price tracking, MAP compliance, and assortment matching across 1,000+ retailers. Best for Brands selling through Target, Walmart, CVS, Amazon, Sephora or Ulta that need store-level price visibility, Retail pricing analysts building a KVI-weighted competitor index across 100+ stores and 10k+ SKUs, Brand protection teams tracking MAP violations and unauthorized marketplace sellers. Free to start; paid plans from $149/mo.
What people actually say about Extractor — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
110 mentions across 6 sources (Hacker News, YouTube, Product Hunt, App Store, Stack Overflow, Lemmy) · researched Sep 1, 2026.
Average across the 6 sources that answered — each source counts once, not each post.
- +Store-level price tracking with ZIP-code granularity is a differentiator.
- +AI predicts markdown moves from historical patterns, enabling proactive pricing.
- +MAP-violation detection with instant alerts protects brand value.
- +Product matching identifies exact and similar competitor items.
- +SEO keyword gap analysis helps surface winning keywords.
- −No community feedback exists to validate reliability or support.
- −Credits-based fetch system may drive up costs for heavy users.
- −Off-topic app-store reviews show negative patterns in similarly-named products.
- −Integration issues with Google Drive reported in a similar app.
- −UI complexity reported in similar tools could be a hurdle.
- • Credits are consumed per data fetch; heavy monitoring of thousands of products may exceed allowances and incur extra charges.
- • Exact pricing for Pro and Custom tiers is not disclosed, making budgeting difficult.
Viability Score
How well maintained and how widely used is Extractor? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Store-level price tracking across 1,000+ retailers, by store rather than national average
- KVI-weighted competitor price index refreshed daily (KVIs weighted 3×)
- Price band flags for items above or below a ±5% tolerance, with out-of-stock items excluded
- MAP and below-MSRP violation detection across retailers and marketplace sellers
- Unauthorized Amazon marketplace seller identification with listing-level evidence
- Price compliance evidence carrying the listing, the store and the timestamp
- Assortment matching on product identifiers first, then verification by a model
- Human review queue for uncertain product matches, with decisions persisting across future runs
- Coverage by brand report showing items found per retailer and which retailers don't carry them
- Price history and spread tracking across multiple stores for the same SKU
- Estimated sales volume and revenue data on the Pro plan
- Credits-based data fetch system, consumed per fetch and analysis
- Scheduled daily runs with CSV export per run
- Marketplace and delivery-app resale monitoring (Instacart, DoorDash, Uber Eats)
- Site learning architecture — a site is learned once, then refreshed daily
About Extractor
Extractor by Lightfeed is a competitor intelligence platform for retail. It pulls store-level pricing, stock, promotions, and assortment data from 1,000+ retailers across grocery, pharmacy, hardlines, apparel, beauty and home, plus the marketplaces and delivery apps that resell the same shelf. The core premise: a national price feed gives you one number when the same SKU is often three prices at three stores. In one documented first run — a 42-SKU beauty catalog across 12 US stores — the platform captured 4,103 listings and found 71% of SKUs priced differently by store. It indexes your shelf against the competition with KVI weighting (3×) and flags items above or below a ±5% band, excluding out-of-stock items from the index. Store tiles break out how many items you're higher on, lower on, and how big the spread is. Assortment and matching work the same way. Coverage by brand shows items found per retailer, matched on identifiers first and then verified by a model — in that run 519 of 897 match decisions were rejected with a stated reason and 27 uncertain pairs went to a human. Those decisions persist across future runs. Price compliance covers MAP and below-MSRP violations across retailers and marketplace sellers, each violation carrying the listing, the store and the timestamp. Pro adds estimated sales volume and revenue data. Where general-purpose extraction tools hand you raw pages, Extractor hands you the comparison already indexed — the tradeoff being that it is retail-specific rather than a general scraper.
Behind the Verdict
Extractor is narrow on purpose, and that narrowness is the product. The platform assumes you sell through national retailers and Amazon, or that you run 100+ stores and 10k+ SKUs, and it builds everything around that assumption. Strengths. Store-level pricing is the headline and it is genuinely different from a national feed: the same SKU is often three prices at three stores, and Extractor shows all of them indexed against your own shelf. KVI weighting at 3× means the items that move your category carry more of the index than the long tail, and the ±5% band plus out-of-stock exclusion keeps noise out of the number. Assortment matching is the sleeper feature — identifier-first with model verification, and in the documented run 519 of 897 match decisions were rejected with a stated reason, with 27 uncertain pairs routed to a human. That reject rate is high, which is the point: a 1.7 oz cream never gets compared against a bundle. The decisions persist, so the review queue shrinks over time rather than resetting every run. Price compliance wraps the same rigor around MAP and below-MSRP violations, each one carrying the listing, the store and the timestamp — evidence you can put in front of a reseller. Pro adds estimated sales volume and revenue, which changes the conversation from 'they're cheaper' to 'they're cheaper and moving volume.' Weaknesses and friction. Coverage has to be built: a site is learned once and then refreshed daily, which means new retailers don't appear instantly. Credits are consumed on every fetch and analysis, so wide store coverage at high refresh frequency is a real monthly cost, not a flat one — this is the main scaling risk for a category team that wants to add stores aggressively. Sales data and analytics sit on Pro at $349/mo, and Slack support is Pro and above. Custom tiers gate custom integrations, a dedicated account manager and custom data exports. If you want raw extracted JSON to build your own analytics rather than a pre-built comparison layer, this is the wrong shape of tool — the value here is the finished comparison, not the pipeline. Non-retail industries (B2B directories, SaaS, industrial, anything outside grocery, pharmacy, hardlines, apparel, beauty and home) are out of scope entirely. Where it fits. Brand protection teams will get the fastest return, because MAP violations with the listing, store and timestamp are actionable on day one. Retail pricing analysts running a KVI-weighted index across 100+ stores get the store tile view and the spread column, which is exactly the artifact that turns into a weekly pricing meeting. Merchandisers get coverage by brand, which answers 'which retailers carry the competition and not us' store by store. The delivery-app resale layer (Instacart, DoorDash, Uber Eats) is a genuine second shelf that most competitor tools ignore. Where it doesn't. Developers who need a public API to pipe competitor data into a warehouse have nothing here. Small operations that can't
Researching Extractor? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Extractor actually fits — and what changes day-one when you adopt it.
Open Price compliance on a Monday, filter to below MSRP/MAP, and export the 14 flagged listings — each with the retailer or seller, the price gap and the stock status
Outcome: You have a listing-level evidence pack for your authorized-seller conversations instead of a screenshot, and the 7-seller marketplace breakdown tells you which 2 are authorized
Check the daily run's store tiles for each location, read the ▲ higher / ▼ lower counts and the ±5% out-of-band number, then open the spread column for items priced at all three stores
Outcome: A weekly pricing meeting runs off one screen — in the documented 42-SKU run, 327 of 460 items priced at all three stores had a different price by store
Pull Coverage by brand, scan the per-retailer item counts and the '—' gaps, then work the 27-item human review queue for uncertain matches
Outcome: You know which retailers carry the competition and not you, and every confirmed or rejected match is remembered so the next run's review queue is smaller
Use Cases
- Monitor competitor price changes across multiple store locations and alert on second cuts
- Detect MAP and below-MSRP violations automatically across a distribution network of 38+ retailers
- Match your product SKUs to exact and similar competitor products at scale
- See which retailers carry the competition and not you, store by store
- Track how Instacart, DoorDash and Uber Eats resell your shelf at their own price
- Build a KVI-weighted competitor index per store for a 10k+ SKU catalog
- Route uncertain product matches to a human reviewer and keep the decision for future runs
Limitations
- The documented limits are usage-based.
- Free tracks up to 100 products with 3,500 credits/month; the pricing cards list Starter at 1,000 products with 5,000 credits/month and Pro at 5,000 products with 25,000 credits/month, while the plan comparison table lists Starter at up to 1,500 products and Pro at up to 10,000 products.
- Credits are consumed each time product data is fetched and analyzed, so wider store coverage and more frequent refreshes cost more.
- Sales data and analytics, Slack support and custom integrations sit on higher tiers (Pro for sales data, Custom for dedicated account manager and custom exports), and sales data is defined as estimated sales volume and revenue.
as of 2026-09-27
Verification history
We have re-verified Extractor 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Extractor tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
A brand or analyst evaluating store-level price tracking on a single small category before committing budget.
What this tier adds
Starting tier at $0 — up to 100 products, 3,500 credits/month, pricing and promotion tracking, email support.
Starter
$149/mo
Ideal for
A single-category brand or emerging pricing analyst tracking roughly 1,000 products across a defined retailer set.
What this tier adds
Adds 10× the product ceiling at 1,000 products and 5,000 credits/month over Free, plus priority email support, for $149/mo.
Pro
$349/mo
Ideal for
A multi-category merchandising or brand protection team that needs estimated competitor sales volume alongside price and MAP data.
What this tier adds
Adds 5,000 products and 25,000 credits/month over Starter, plus sales data and analytics and Slack support, for $349/mo.
Custom
Custom
Ideal for
An enterprise program running an unlimited catalog, custom integrations and a named account owner.
What this tier adds
Removes the product and credit caps entirely and adds custom integrations, custom data exports and a dedicated account manager.
Where the pricing makes sense
The company stage and team size where Extractor's pricing actually pencils out — and where peers do it cheaper.
Free covers up to 100 products and 3,500 credits/month, and Starter at $149/mo suits a single-category brand tracking a handful of retailers. Pro at $349/mo fits a multi-category team that needs the 25,000-credit allowance and the sales-volume data, and sits below what a custom retail data feed from a conventional broker typically runs. Custom is priced for enterprise programs with unlimited products and custom integrations.
Setup time & first value
How long it actually takes to get something useful out of Extractor — broken out by persona, not the marketing-page minute.
A first run on one category over a set of store locations is a scheduling exercise rather than an integration project — pick products, pick stores, set the daily cadence. Coverage for each new retailer has to be learned before it appears in refreshes, so budget extra days per added chain. Starter and Pro teams should expect the first usable price-compliance report within the first week of runs.
Switching to or from Extractor
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a national price feed or data broker: keep the feed for category context and let Extractor supply the store-level spread and MAP evidence it can't give you.
- →From manual store checks and screenshots: replace the spreadsheet with scheduled daily runs and CSV exports per run.
- →From a general-purpose scraper: stop maintaining selectors and let identifier-first matching plus model verification handle like-for-like pairing.
- ↗To a general-purpose extraction platform such as Bright Data or Oxylabs: you get raw pages, but you rebuild matching, KVI weighting and the compliance evidence layer yourself.
- ↗To a conventional retail data feed: you get one national number per SKU and lose the per-store spread and the listing-level MAP detail.
- ↗To an in-house pipeline: only viable if you're prepared to own product matching, human review and daily refresh infrastructure.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Extractor”, and we withheld 6: 6 could not be judged, because “Extractor” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Extractor.
Official links
Tools that pair well with Extractor
Common stack mates teams adopt alongside Extractor, with the specific reason each pairing earns its keep.
Channable
Channable manages product feeds and PPC automation across 3,000+ channels for e-commerce teams
BrandWell
Intent-led GTM platform that reveals buyers researching your competitors and maps them to decision-makers.
Brand24
Brand24 tracks your brand across 25M+ online sources with AI sentiment, AI Visibility for LLM mentions, and automated reports.
Featured Head-to-Head Comparisons
Extractor vs Screenplayiq
If you're a screenwriter or producer needing data-driven script analysis with box office forecasts, ScreenplayIQ is your only choice. For e-commerce competitive intelligence—pricing, MAP violations, SEO—Extractor is purpose-built. There is no overlap; pick based on your industry.
Extractor vs Nectar Energy
If you're a commercial building owner or facility manager aiming to cut HVAC/lighting energy costs and automate ESG reporting, Nectar Energy is the clear choice. If you're an e-commerce retailer needing to track competitor pricing, MAP violations, and SEO gaps, Extractor's freemium LLM-powered platform is purpose-built for that. The two tools address different domains — choose based on whether your priority is energy efficiency or retail competitive intelligence.
Extractor vs Geologicai
GeologicAI is the clear choice for mining companies needing integrated, high-speed core scanning and AI modeling for critical minerals, backed by recent LIBS acquisition and funding. Extractor serves e-commerce retailers tracking pricing and MAP compliance but faces reputational risk from the Anthropic extraction accusation. Choose based on your industry: mining vs retail.
Alternatives to Extractor
View all
Frequently Asked Questions
Best-of guides
Used Extractor? Help shape our editorial sentiment research.