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For RetailersWhich solution fits?
Talk to ExpertTalabat, Careem Quik and Noon Minutes across 18 GCC cities.
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Food Delivery Intelligence NEW
OTT & Streaming NEW
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Pricing Intelligence
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Quick Commerce — Zepto · Blinkit
Pincode Price Tracker
GCC Q-Commerce — Talabat · Noon NEW
UK Grocery — Tesco, Sainsbury's, Asda
AU Grocery — Coles & Woolworths NEW
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Trust & Company
Scalable web, app and AI-powered collection across 40+ countries.
All 58 services →Repricing driven by competitor moves rather than a weekly review.
Explore → Pricing & Promotions
Brand & Intelligence
Digital Shelf & Search
Assortment For RetailersWhich solution fits?
Talk to ExpertSources we already run pipelines against. Download a real sample from any of them.
Browse coverage →Talabat, Careem Quik and Noon Minutes across 18 GCC cities.
Launch Demo →Tesco, Sainsbury's, Asda, Morrisons and Aldi, daily.
Get Early Access → E-Commerce Dashboard
Flipkart Insights (Live) #1
Grocery Intelligence
Grocery Price (U.S.)
Quick Commerce (India) HOT
Food & Restaurant
Fashion Intelligence
Automotive
Travel & Hospitality
Real Estate
Food Delivery Intelligence NEW
OTT & Streaming NEW
By Use Case
Pricing Intelligence
Digital Shelf Analytics
MAP Monitoring HOT
Cross-Border Price Parity NEW
Share of Search
Review Sentiment
Kitchen Market Gaps NEW
Dynamic Pricing / AI Repricing NEW
Promotions & Deals Alerts NEW
B2B / POI & Lead Data NEW
By Region Flipkart Real-Time Insights
Quick Commerce — Zepto · Blinkit
Pincode Price Tracker
GCC Q-Commerce — Talabat · Noon NEW
UK Grocery — Tesco, Sainsbury's, Asda
AU Grocery — Coles & Woolworths NEW
Marketplace Scrapers
Developer APIs Universal APIs Delivery & SDKsFixed monthly retainer, named engineer, no per-request metering.
Managed Data API → Knowledge Center
Guides & Playbooks
Downloads & Tools
Trust & CompanyThis is not grocery with bigger packs. The buyer is a chef or a purchasing manager, the unit is a case, and the price a restaurant actually pays is usually not on the page at all.
This is not grocery with bigger packs. The buyer is a chef or a purchasing manager, the unit is a case, and the price a restaurant actually pays is usually not on the page at all.
Free pilot on your own sources, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.
Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.
Retail grocery shows a shelf price to anyone. B2B foodservice frequently does not.
Distributors operate three patterns, sometimes on the same site:
The mix varies enormously by distributor and by category, and it decides whether a price-monitoring programme is viable at all.
Before quoting, we report visible_share_category across your catalogue: the proportion of products
where a price is obtainable without an account. In some categories that is most of it. In others it is under a
third, and a price index built on the visible remainder needs saying so loudly.
Create a trade account. Use a client's credentials. Submit a quote request form.
All three would raise the visible share and all three are actions on someone else's system rather than collection. The last one is worth naming specifically because it is the one clients most often suggest: submitting an RFQ to extract a price puts a fabricated enquiry into a distributor's sales pipeline, in someone's name.
Where a price is gated, the field is null with a reason. We never substitute list price for a gated trade price — the gap between them is the entire point of the analysis.
Every one of them changes what a price means.
The unit of trade, and where most feeds break.
Three states, reported as a share.
Beyond images, which is where this differs.
Both change what a price is worth.
Suggested by the distributor, never asserted by us.
The fields a chef or buyer actually filters on.
A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.
One record per distributor SKU. The case fields are the ones that matter most.
| Field | Type | What it captures | Refresh |
|---|---|---|---|
distributor / distributor_sku |
string | Which distributor and their own code | Every record |
product_name / brand / category |
string | As published | Every record |
case_qty / unit_size / unit_uom |
number / string | The case architecture, parsed | Where parseable |
list_price_case / currency |
number / string | Case price where visible | list_public records |
price_per_unit / price_per_uom / uom_basis |
number / string | Derived, with the basis named | Where case parses |
case_parse_confidence |
number | Below threshold, per-unit is null rather than guessed | Every record |
price_visibility |
string | list_public, account_required or quote_only | Every record |
visible_share_category |
number | Share of the category where price is obtainable anonymously | Per batch |
moq_cases / lead_time_days |
number | Where published. Both change what a price means | Where published |
assets |
object | Image count, spec PDF, nutrition PDF, allergen doc, 3D, video | Every record |
substitutes / equivalence_asserted_by_actowiz |
array / constant | Distributor suggestions. The constant is always false | Where published |
Every batch ships with the visible-price share by category and distributor, because that number moves as distributors change their gating and a programme built on last quarter's share can quietly stop working.
Price visibility is the variable, and it differs more by distributor than by category.
Fresh produce, meat and seafood are the honest weak spots. Prices move daily and are frequently quoted rather than published, which is a commercial reality rather than a collection problem. A programme covering those categories needs a different design and we will say so. Request a source we don't list →
We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.
| Market | Why demand concentrates here |
|---|
We run production collection across 40+ countries. Coverage depth varies by market and by source, so we confirm what is actually available for your specific markets during scoping rather than claiming uniform global coverage. Ask about a market we don't list →
Two sides of the same market, wanting opposite things.
Needs to know whether their negotiated pricing is competitive against list and against other distributors.
Case-level pricing with per-unit derived, plus the visible share so the gaps are known.
Cost position
Wants to see competitor list pricing and range without an account.
Cross-distributor catalogue with case architecture normalised for comparison.
Range competitiveness
Needs to know how their products are listed, priced and described across distributors.
Presence, list price and asset completeness per distributor.
Channel visibility
MOQ and lead time change what a price is worth across sites.
Both captured where published, alongside stock and discontinuation state.
Availability planning
Catalogue quality is a competitive lever and gaps are hard to see.
Asset completeness per SKU — images, spec sheets, allergen docs — against competitors.
Catalogue completeness
Wants list-price movement as a proxy for input cost trends.
Consistent case-normalised series with the visible share stated, so the basis is defensible.
Series integrity
Four patterns, and the last one is about your own catalogue rather than competitors'.
Your contracted pricing against visible list pricing across distributors, case-normalised so comparison is per unit rather than per pack format. The visible share tells you how much of the picture you have.
Outcome: A negotiation opened from evidence rather than a claim.
Which distributors carry what, with case architecture normalised so a 12x2.5L and a 6x5L are comparable on a per-litre basis.
Outcome: Range gaps and duplication visible across a supply base.
How your products appear across distributor catalogues — listed, priced, described, and with which assets present or missing.
Outcome: Distribution and content gaps you cannot see from your own systems.
Asset presence per SKU against competitors: images, spec sheets, nutrition and allergen documents. Foodservice buyers filter on allergens, so a missing declaration is a lost sale.
Outcome: A content gap that is cheap to close once identified.
Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.
Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →
Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.
Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.
We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.
Best fit: Teams who need the data, not the infrastructure.
The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.
Best fit: Product and engineering teams building on live data.
A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.
Best fit: Research, strategy and diligence work with a deadline.
Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.
They look adjacent and behave differently. Picking the wrong one produces a dataset that answers nothing.
| Consideration | In-house scraping team | Generic proxy / DIY tool | Actowiz managed feed |
|---|---|---|---|
| Time to first usable data | 6–12 weeks of engineering before anything is trustworthy | Days, but output needs manual cleanup before use | Free pilot in 24 hours, production in 5–10 business days |
| Who fixes it when a source changes | Your engineers, at the cost of their roadmap | You do — tools report failures, they don't resolve them | We do, same business day, inside the retainer |
| Data quality assurance | Whatever your team has time to build | None beyond HTTP success | Schema validation plus sampled human QA on every run |
| Compliance documentation | Rarely produced, then requested urgently by legal | Not provided; terms risk sits with you | Sources, method and lawful basis documented for review |
| Accountability | Distributed across a team with other priorities | A support ticket queue | A named engineer and an account owner |
| True annual cost | Engineer salaries, proxies, hosting, ongoing maintenance | Low licence fee plus significant hidden analyst time | One fixed monthly retainer, quoted after scoping |
A retail feed parses "500g" from a title. A foodservice feed has to parse 12 x 2.5L, 6/5 LB, CS/24-16OZ and a dozen other conventions that differ by distributor and sometimes by category within one distributor.
Get it wrong and every derived figure is wrong in a way that looks plausible.
We parse case_qty, unit_size and unit_uom separately, carry
case_parse_confidence, and where confidence is below the agreed threshold we leave
price_per_unit null with a reason rather than publishing a number derived from a guess.
Split-case availability is captured separately where a distributor offers it, because a per-unit price on a case-only line is a theoretical figure a buyer cannot act on.
In retail, imagery drives conversion. In foodservice, the document that decides a purchase is frequently the allergen declaration or the specification sheet.
We record asset presence per SKU: image count, spec PDF, nutrition PDF, allergen document, certification, and 3D or video where published. For a distributor, that is a competitive gap analysis. For a manufacturer, it shows where your product is listed without the documents that let a buyer choose it.
Certifications and origin claims are captured as published and never verified. Whether a certification is
current is a matter for the issuing body, not for extraction. verified_by_actowiz is false on every claim
field, as it is everywhere else on this site.
Where you want the assets themselves rather than a presence flag, that is our image enrichment service, and the same rights position applies: we record the source of every file and do not grant usage rights.
The visible-price share comes first, because it decides whether the rest is worth doing.
Distributors matter more than SKUs here, because gating differs by distributor far more than by category.
Before quoting, per distributor and per category. If it is under a third in your categories, we will tell you that a price index is not the right programme.
Real records with case architecture parsed, per-unit derived where safe, and nulls where parsing was not, so you can see the parse quality rather than a claim about it.
How conservative to be. A higher threshold means more nulls and fewer wrong numbers, and the right setting depends on what you will do with the data.
Weekly is usually right for trade catalogues. The visible share ships in every batch, since distributors change their gating and a programme can quietly stop working.
JSON, JSONL, CSV, Parquet or XLSX to Amazon S3, Google Cloud Storage, Azure Blob, Snowflake, BigQuery, SFTP or a REST endpoint. Asset files, where in scope, delivered to your own bucket.
We collect only publicly accessible catalogue information. We never create a trade account, use client credentials, or submit a quote or RFQ form. Certification and origin claims are captured as published with verified_by_actowiz set false.
These are contractual, not marketing copy. They appear in the engagement document.
| Commitment | What we hold ourselves to |
|---|---|
| Pilot turnaround | A real sample from your own sources within 24 hours of scoping, at no cost. |
| Go-live | Production collection running within 5–10 business days of sign-off. |
| Delivery punctuality | 99.5% on-schedule delivery, measured monthly and reported to you. |
| Breakage response | Source layout changes triaged same business day; critical sources inside 4 hours. |
| Data quality | Schema validation on every run plus sampled human QA before any delivery leaves us. |
| Escalation | A named engineer and an account owner, not a shared ticket queue. |
| Change requests | Field additions and source changes handled inside the retainer, not re-quoted. |
| Exit | Your historical data exported in full on request. No lock-in, no export fee. |
Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.
Starting with the one that decides whether a programme is viable.
Only where they are visible without an account, which is a minority on most distributors. We never create a trade account and never use client credentials.
What we do is report the visible-price share per distributor and category before you commit, so you know how much of the picture a programme can actually see. Where a price is gated the field is null with a reason, and we never substitute list price — the gap between list and trade is the entire point of the analysis.
No. That puts a fabricated enquiry into a distributor's sales pipeline, usually in a named person's name, and it is an action on someone else's system rather than collection.
It is the suggestion we receive most often on this service, which is why it is stated rather than left implied.
Because a per-unit price computed from a mis-parsed case is wrong by a factor rather than a rounding, and it looks plausible. Cross-distributor comparison inverts when one side's case size is misread.
We carry case_parse_confidence and leave per-unit null below the agreed threshold, rather than publishing a number derived from a guess.
Different unit, different buyer, different price visibility. Grocery shows a shelf price to anyone and the unit is a consumer pack. Here the unit is a case, the buyer is a chef or purchasing manager, and the price is frequently behind an account.
Running a grocery schema against a foodservice catalogue produces per-unit figures that are confidently wrong.
Fresh produce, meat and seafood. Prices move daily and are frequently quoted rather than published, which is a commercial reality rather than a collection failure.
A programme covering those needs a different design — usually indicative ranges and availability rather than a price series — and we will say so instead of quoting for something that will not work.
We record their presence per SKU as standard, which is the competitive gap analysis most clients want. Retrieving the files themselves is in scope on request.
Where files are retrieved the same rights position applies as on image enrichment: we record the source of every file and do not grant usage rights.
No. We capture them exactly as published with verified_by_actowiz false. Whether a certification is current is a matter for the issuing body.
Tracking how certification claims change across a distributor's range is genuinely useful; asserting they are valid is not ours to do.
Weekly is usually right. Trade catalogues move considerably slower than retail, and daily collection on a catalogue that changes weekly costs seven times as much for the same answer.
Fresh categories are the exception, and those are the ones where prices are least often published anyway.
We quote individually. The main driver is distributor count rather than SKU count, because each distributor's case conventions and gating need their own handling.
One scoping call, a free sample within 24 hours with the visible-price share per distributor, then a fixed monthly quote. Request a quote.
Send your distributor list. We return the visible-price share per distributor and category before any quote.
If it is under a third in your categories, we will tell you a price index is the wrong programme rather than quoting for one.Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.
Watch how businesses like yours are using Actowiz data to drive growth.
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