Recurring change feed
A structured recurring feed that captures what changed across competitors, catalogs, public listings, and location surfaces.
Public web data feeds and dashboards
Public source intelligence packages.
Recurring feeds, dashboard views, and analyst-ready dataset handoffs for markets your team cannot monitor manually.
Send one source, market, category, competitor, or business question. Popas replies with the proposed package shape, dashboard angle, delivery path, and scoping assumptions. On-premises operation, without corporate cloud tax.
Our founders worked with:
Market Intelligence Package
One package for feed delivery and dashboard visibility.
The default Popas offer is a mixed-source market intelligence package: a recurring feed plus a dashboard layer, scoped around the public sources and commercial questions the buyer actually needs to track.
Package example
Monitor competitors, catalogs, listings, and locations in one operating view.
This is not a one-off scrape. Popas scopes the source set, keeps the feed running, normalizes the output across sources, and delivers a dashboard layer that lets teams see what changed without rebuilding the data operation internally.
recurring feeddashboard panelscross-source matchingsample-first scoping
collect Track competitor catalogs, public listings, store footprints, and source changes in one recurring feed live match Normalize entities across sources so teams can compare like-for-like instead of raw row noise clean view Push the recurring feed into dashboard panels built around the commercial questions that matter visible alert Surface meaningful changes without forcing the buyer to manually monitor every source watched deliver Send the same package out as feed outputs, dashboard views, or dataset handoffs ready Dashboard panels
Buyer-facing dashboard views for monitoring coverage shifts, assortment movement, and source changes without manual tracking.
Scoped source coverage
Package scope is defined around the buyer’s market, source set, cadence, and workflow instead of a generic vendor template.
Dataset handoff
When needed, the same intelligence package can land as analyst-ready files, warehouse tables, or a ready-made dataset surface.
What You Can Buy
One intelligence operation, sold in the format that closes.
The package is the default. Feeds, dashboards, and ready-made datasets remain available when a buyer needs a narrower entry point.
01 / Market Intelligence Package
Recurring feed plus dashboard layer for buyers who need one place to monitor competitors, catalogs, listings, and locations.
Included outcomes
- Mixed-source monitoring
- Dashboard visibility
- Recurring feed delivery
Commercial posture
- primary offer
- custom scope
- buyer-ready package
02 / Recurring data feeds
For teams that want the recurring feed without the dashboard layer, delivered directly into their existing workflow.
Included outcomes
- Files or API delivery
- Warehouse-ready outputs
- Ongoing source monitoring
Commercial posture
- delivery-first
- source-specific cadence
- structured outputs
03 / Dashboards
For buyers who want monitored views and decision surfaces built on top of recurring public-source coverage.
Included outcomes
- Operational visibility
- Commercial review
- Shared internal reporting
Commercial posture
- dashboard-first view
- matched entities
- change visibility
04 / Ready-made datasets
For sources already packaged into productized dataset coverage when the buyer wants the fastest path to useful data.
Included outcomes
- Immediate starting point
- Analyst-ready files
- Optional expansion into custom work
Commercial posture
- catalog surface
- productized coverage
- secondary entry point
Why Popas
Lean infrastructure, cleaner data, more useful coverage.
The point is not scraping for its own sake. The point is an intelligence operation that stays commercially useful when the source set gets messy.
Typical alternative
Cloud-heavy data vendors burn margin on infrastructure layers buyers never asked for.
Why Popas
Popas runs on-prem, so more of the budget can go into useful coverage, refresh cadence, and delivery instead of corporate cloud tax.
Typical alternative
Most vendors either sell raw rows or polished BI, but not a package that can flex between them.
Why Popas
Popas can sell the full package, the recurring feed, the dashboard layer, or the ready-made dataset without changing the underlying operation.
Typical alternative
Mixed public sources create duplicate entities, mismatched records, and noisy comparisons.
Why Popas
Cross-source matching and normalization make the output cleaner before it lands in the dashboard or downstream data workflow.
Typical alternative
Buyers waste weeks describing a project before they can judge whether a vendor really understands the market.
Why Popas
Sample-first scoping turns one source list or market question into a concrete package proposal before recurring production begins.
How The Package Runs
From messy public sources to a usable operating view.
The package keeps working because Popas handles discovery, recurring collection, recovery, validation, and delivery behind the buyer-facing feed and dashboard surfaces.
Find the sources and competitors worth watching.
Popas maps public retailers, marketplaces, directories, store locators, category pages, review surfaces, brand pages, and competitor sources before a feed is built.
competitor discoverysource mappingcoverage plan
Collect catalog, listing, review, location, and market signals.
Request, browser, and hybrid crawlers collect the signals needed for recurring public-web intelligence across datasets, dashboards, and custom feeds.
product pagesprice changesavailability
Recover when source pages drift or break.
Self-healing scrapers handle selector drift, empty responses, pagination changes, JavaScript-heavy pages, and delivery failures before they become client work.
selector driftsource changeretry path
Use AI analysis before the data reaches the client.
Outputs are checked for missing fields, duplicate entities, suspicious price moves, stock anomalies, stale runs, and schema changes.
AI anomaly reviewentity matchingfreshness
Send business-ready feeds into the workflow already in place.
Clean data lands as files, APIs, warehouse tables, BI-ready datasets, recurring reports, Dashboards, or marketplace packages.
ParquetAPIdashboard-ready
Marketplace
Ready-made datasets are one entry point, not the whole offer.
Browse productized location datasets as a starting point. When you need broader public-source coverage, recurring product feeds, review collection, or a dashboard layer, Popas scopes the work around the exact source.
Canada liquor boards package
Multi-board location coverage for LCBO, SAQ, BCLIQUOR, NSLC, ANBL, Manitoba Liquor Marts, and PEI Liquor with one delivery contract.
LCBO locations
Store identity, address, coordinates, hours, phone, status, source URL, and sample rows for Ontario coverage.
Beer Store locations
Location data prepared for analysts, sales teams, mapping workflows, and territory planning across Ontario stores.
SAQ locations
Quebec liquor-board location coverage with store identity, coordinates, address, status, and sample rows for review.
Need something beyond the marketplace? Popas scopes custom monitored feeds, dashboards, and scoped extraction work by source complexity, refresh cadence, QA depth, and delivery requirements.
Start with a source
Send one source. Get a sample intelligence plan.
Share a source, competitor list, category, marketplace, or business question. Popas will reply with the proposed package shape, feed structure, dashboard angle, QA path, cadence, delivery format, and next step. The operation stays lean and on-prem to avoid unnecessary cloud overhead.