Price × stock
A competitor's price move read against their inventory position, so you can tell a clearance from a repricing.
Services — actionable signals
One signal beats five dashboards.
A price change is data. A price change on a competitor who just went out of stock, days before your biggest sales week, is a signal. Crawlify joins multiple verified pipelines into a single composite signal, delivered into the systems your team already uses, with the source and timestamp on every record behind it. Not another dashboard to check. A signal that arrives where you work.
Competitor repricing detected
Competitor X
Price
$1,299
12.4%vs 7 days ago
Inventory
184 units
31%vs 7 days ago
Promotion
Detected
NEWFirst seen 10:44 AM
Evidence Timeline
- Price changed10:41 AMSource: pricing.crawler
- Inventory increased10:42 AMSource: inventory.tracker
- Listing updated10:41 AMSource: listing.monitor
- Promotion detected10:44 AMSource: promo.scanner
- Market activity high10:46 AMSource: market.intel
Correlation Overview
Price droppedPrice decreased 14% in the last 7 days.
Inventory increasedInventory increased 28% in the last 7 days.
Correlated Signal
Aggressive repositioning detected
Competitor is lowering price while increasing inventory and launching a promotion.
98% ConfidencePromotion launchedNew -20% promotion detected.
Listing updatedProduct details or media updated.
The thesis
Verification catches wrong values. Correlation catches wrong conclusions.
A verified record tells you a number is true. It doesn’t tell you the number matters. Most teams have plenty of accurate data and still miss the thing that mattered, because the signal wasn’t in any single source. It was in the relationship between them.
Correlation is the layer where data stops being reference material and starts being a reason to do something differently on Monday.
A competitor's price drop is unremarkable on its own. Joined to their stock position, their ad spend, and the timing of your own sales calendar, it reads as one thing: a clearance push before a product change. That's a decision you can make. No individual pipeline contains it.
How it’s built
A signal is engineered, not guessed.
Correlation isn’t an AI deciding on its own what’s important. It’s your definition of what matters, applied consistently across verified pipelines, every run. You tell us the conditions that constitute a signal in your business. We build it, verify the inputs, and deliver the result.
01
Define
What combination of conditions actually matters to your team? That's a conversation, not a setting. It's usually the thing your best analyst checks manually.
02
Verify the inputs
Every pipeline feeding the signal is verified at the field level first. A signal built on unverified inputs is a confident wrong answer.
03
Join
Records matched across pipelines on identity, so the price, the stock level, and the ad spend genuinely belong to the same product at the same moment.
04
Deliver
The composite signal lands in your CRM, warehouse, or Slack, with every contributing source attached.
Signal types
The joins that turn data into decisions.
Officeholder × legislation
Who holds the seat, joined to what they're moving and which districts it touches.
Supplier × disruption
A supplier's litigation, facility news, and corporate status read together as one escalating-risk signal rather than three unrelated alerts.
Posting × ATS
A job posting joined to the applicant-tracking system behind it and the hiring velocity around it, so a role reads as a team build-out rather than a backfill.
Filing × enforcement
A regulatory filing joined to the enforcement history of the entity behind it.
Your data × ours
The most valuable join of all. Your CRM, member list, or supplier roster, matched against verified external pipelines, so a signal arrives already scoped to accounts you actually care about.
Provenance
Three verified records, one signal.
Every contributing record keeps its own source and timestamp, so the conclusion stays auditable back to the facts underneath it.
Talk to uscomposite_signal.json
{
"signal_id": "SIG-33071",
"signal_type": "competitor_clearance_push",
"entity": "Example Competitor — Wireless Headphones",
"confidence": 0.981,
"contributing_records": [
{ "pipeline": "price_monitoring", "field": "price", "before": "8999.00", "after": "7499.00", "source_url": "https://example-competitor.com/p/wireless-headphones", "captured_at": "2026-08-24T04:02:19Z", "verifier_id": "hv-6612" },
{ "pipeline": "stock_monitoring", "field": "stock_status", "value": "low_stock", "source_url": "https://example-competitor.com/p/wireless-headphones", "captured_at": "2026-08-24T04:02:22Z", "verifier_id": "hv-6612" },
{ "pipeline": "ad_monitoring", "field": "active_campaigns", "value": 0, "source_url": "https://example-adlibrary.com/example-competitor", "captured_at": "2026-08-24T04:11:40Z", "verifier_id": "hv-6699" }
],
"interpretation": "Price cut plus low stock plus ad spend stopped reads as clearance ahead of a product change, not a competitive reprice.",
"delivered_to": ["salesforce", "slack:#pricing"]
}Destinations
A signal that arrives where you already work.
A signal nobody sees isn't a signal. We deliver into the tools your team already has open, so acting on it doesn't require anyone to log into one more system.
Salesforce
Hubspot
Snowflake
Slack
Webhooks
Rest APIHow It Works
A managed service, not a tool you rent by the request.
- 01
Scope
You bring the sources and the target schema. We confirm what's public, what's feasible, and the cadence.
- 02
Deliver
Project-based verified pulls into your stack.
- 03
Graduate to feeds
As volume and cadence grow, one-time pulls become continuous feeds. Projects first, feeds as you scale.
Case Study
Academic event data pipeline — ScholarMeet
Crawlify powers the data pipeline behind ScholarMeet. What would have taken our team weeks of manual work now runs continuously with verified accuracy.
www.scholarmeet.com
Read Full Case Study
Fit
Where correlated signals fit versus alerts and dashboards.
BI tools are excellent at visualizing data you already have. Crawlify joins verified external pipelines into a signal and pushes it to you, rather than waiting for someone to open a dashboard and notice.
| Capability | Separate data feeds | BI dashboardsLooker, Tableau, Power BI | Crawlify |
|---|---|---|---|
| Provides the underlying data | visualizes what you already have | ||
| Verified at field level with provenance | Rarely | Inherits whatever you feed it | |
| Joins multiple external pipelines | Partial — if you've already modeled it | ||
| Produces a composite signal, not just a chart | |||
| Pushes to you rather than waiting to be checked | Partial — raw alerts | Pull, not push | |
| Traceable back to source evidence | Varies | To your warehouse, not the origin | Every contributing record |
| Model | Per-feed subscriptions | Software plus your analysts | Managed service, projects → feeds |
Use cases
What teams use managed extraction for.
Compliance
Public data, collected responsibly.
We work from public sources, at respectful request rates, in line with the rules that apply. Compliance isn’t an afterthought bolted on at the end — it’s part of how the data is collected and verified, which is also why it holds up when someone asks where it came from.
Frequently asked questions
A conclusion supported by more than one verified pipeline. Not a data point and not an alert that something changed, but a join: this price move, on this stock position, with this ad activity, reads as this. You define what combination matters in your business; we build and run it.
Have a signal in mind?
Tell us the pipelines and the conditions that matter to your team, and we'll tell you what's feasible, what's verifiable, and how we'd deliver it. Scoping call, not a sales pitch.
Talk to us
Become a data partner
Working with us on a vertical where correlated signals become a shared dataset? That's the data-partner track.
Become Data Partner