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Service · News data

News Data Scraping Services & News API

With entities resolved and duplicates already collapsed.

News data services cover managed collection of published news content at scale, with entity resolution, deduplication and classification applied before delivery, available as a scheduled feed or an authenticated API for your own systems.

Raw news collection produces the same story forty times. The work worth paying for is everything that happens after collection.

Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.

80,000+ publishers, 40+ languages Near real-time, under 5 min median Syndication deduplicated
news_stream_2026-08-05.jsonl LIVE FEED
{"article_id":"aw-2026080512-9f3c", "canonical":true,"duplicate_count":17, "headline":"Retailer lifts guidance on grocery volume", "outlet":"Reuters","outlet_tier":1, "country":"GB","language":"en", "published_at":"2026-08-05T07:14:02Z", "ingested_at":"2026-08-05T07:16:41Z", "body_words":642,"paywalled":false, "entities":[{"name":"Tesco PLC", "type":"ORG","ticker":"TSCO.L", "salience":0.91}], "topics":["earnings","grocery_retail"], "sentiment":{"score":0.42,"label":"positive"}}
1 of 214,880 articles today · 04:00–08:00Zdedup ratio 3.4:1 · schema v5.0
Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall

Key facts at a glance

What it is
Structured article records with full text, metadata, resolved entities, topics and sentiment
Publisher coverage
80,000+ sources across 40+ languages and 190+ countries
Latency
Median under 5 minutes from publication to delivery on priority sources
Deduplication
Syndicated and wire-republished articles collapsed to one canonical record with a duplicate count
Entity resolution
Organisations linked to tickers and identifiers where available; people and locations typed
Delivery
Streaming API, webhooks, or scheduled batch in JSON, JSONL or Parquet
Historical archive
Multi-year archive available for backtesting and model training on selected sources
Who it's for
Quant and fundamental research, risk and compliance, PR and comms, AI teams building RAG systems
80,000+publishers monitored40+ languages
< 5 minmedian publish-to-deliverypriority sources
3.4:1typical dedup ratiosyndication collapsed
190+countries coveredglobal footprint

Key takeaways

  • What it is: Structured article records with full text, metadata, resolved entities, topics and sentiment
  • Publisher coverage: 80,000+ sources across 40+ languages and 190+ countries
  • Latency: Median under 5 minutes from publication to delivery on priority sources
  • Deduplication: Syndicated and wire-republished articles collapsed to one canonical record with a duplicate count
  • Entity resolution: Organisations linked to tickers and identifiers where available; people and locations typed
  • Delivery: Streaming API, webhooks, or scheduled batch in JSON, JSONL or Parquet

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is news data, and what makes a news feed usable rather than merely large?

News data is journalism converted into records a machine can process: each article decomposed into a headline, body text, publication timestamp, author, outlet identity and geography, plus derived annotations such as the entities it mentions, the topics it covers and its tone. The value is not in the volume of articles — anyone can crawl a lot of news — but in what has been resolved before the data reaches you.

Three problems separate a usable feed from a firehose:

1. Syndication makes raw counts meaningless

A single wire story is republished by dozens of outlets, often verbatim, sometimes with a changed headline. Uncollapsed, this produces phantom signal: a mention-volume spike that reflects one story, not forty. We compute content fingerprints across the corpus, designate a canonical record with the earliest reliable timestamp, and attach the duplicate count and republishing outlet list to it. Typical collapse ratio is around 3.4 to 1.

2. Entity mentions need resolution, not string matching

"Apple" is a company, a fruit and a record label. "Jaguar" is a car maker and an animal. String matching on a company name produces false positives that quietly corrupt any downstream signal. We resolve entities to identifiers — tickers, registry IDs, geographic codes — using surrounding context, and attach a salience score indicating whether the entity is central to the article or a passing reference.

3. Timestamps must be trustworthy

Publishers backdate, update and silently republish. We record both the publisher's stated publication time and our own ingestion time, and flag material body changes on subsequent observation. For any latency-sensitive use case, knowing which timestamp you are trusting is not a detail — it is the whole basis of the analysis.

What we extract

Six layers in every news record

Take full text with annotations, or metadata-only if licensing or storage constraints apply.

Article content

The article itself, cleanly separated from site furniture.

  • Headline, subheadline, standfirst
  • Full body text, boilerplate removed
  • Author byline and section
  • Word count and reading time

Outlet metadata

Who published it, and how much weight that outlet should carry.

  • Outlet name, domain and country
  • Tier classification by reach
  • Language and regional edition
  • Paywall and access status

Entity extraction

The organisations, people and places the article is actually about.

  • Organisations linked to tickers/IDs
  • People with role context
  • Locations with geographic codes
  • Salience score per entity

Topic classification

Consistent taxonomy so filtering doesn't rely on keyword guesswork.

  • Multi-label topic taxonomy
  • Industry and sector tags
  • Event-type classification
  • Custom taxonomy mapping

Sentiment & tone

Directional signal at both article and entity level.

  • Article-level sentiment score
  • Per-entity sentiment
  • Subjectivity indicator
  • Headline vs body tone divergence

Deduplication & lineage

The layer that turns volume into signal.

  • Canonical record designation
  • Duplicate count and outlet list
  • First-published timestamp resolution
  • Update and revision detection
Service scope

What the news data service includes

Collection is the easy part. Deduplication, entity resolution and classification are the service.

✓ Included in every engagement

  • Deduplication and syndication clustering across republishing chains
  • Entity resolution for companies, people and locations
  • Topic and sentiment classification with documented method
  • Latency tiers from near real time to daily digest
  • Source discovery, scoping and a written collection plan
  • Free pilot on your own sources before any commitment
  • Full pipeline build, hosting and proxy infrastructure
  • Schema design, validation and sampled human QA on every run
  • Ongoing maintenance when source layouts change — our cost, not yours
  • Delivery to your warehouse, bucket, SFTP or API endpoint
  • Documented methodology and compliance notes for your legal review

× Not included — stated upfront

  • Paywalled article bodies — we deliver metadata and permitted excerpts only
  • Redistribution rights to publisher content you don't hold
  • Full-text archives from before our collection began
  • Anything behind a login, paywall or credentialed session
  • Personal data beyond a documented lawful basis
  • Licensed third-party datasets we do not hold rights to
  • Guarantees about fields a source simply does not publish
Schema

News data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary is agreed during scoping.

Deliverable schema — news feed v5.0 — core fields shown; full dictionary has 60+ fields
Field Type What it captures Refresh
article_id string Stable identifier, persistent across updates to the same article Real time
canonical / duplicate_count boolean / int Whether this is the canonical version and how many republications exist Real time
headline / body_text string Headline and full article body with navigation and boilerplate stripped Real time
outlet / outlet_tier string / int Publisher name and reach-based tier classification Real time
country / language enum ISO country and language codes for the specific edition Real time
published_at / ingested_at timestamp Publisher-stated publication time and our ingestion time, kept separate Real time
entities array Resolved organisations, people and locations with type, identifier and salience Real time
topics array Multi-label classification against our taxonomy or your custom mapping Real time
sentiment object Article-level and per-entity sentiment scores with labels Real time
paywalled boolean Whether full text sits behind a paywall, in which case we deliver metadata only Real time
revision_of / revised_at string / timestamp Links a revised article to its earlier version with change timing Real time

Where an article is paywalled, we deliver headline and metadata only and never the protected body text. Publisher access restrictions are respected as a hard rule, not a configurable option.

Coverage

Publisher categories and regions we cover

Coverage is built by tier and region rather than by a fixed source list, so specialist trade press in your sector is usually addable.

Global wires (Reuters, AP, AFP, PA)Financial pressNational dailiesRegional and local pressTrade and industry pressTechnology mediaRetail and CPG tradeEnergy and commodities pressHealthcare and pharma mediaAgriculture trade pressAutomotive mediaLogistics and shipping pressCompany press releasesRegulatory and government announcementsBroadcast transcriptsAggregators and news portalsEnglish, Spanish, PortugueseFrench, German, Italian, DutchArabic, Hebrew, TurkishHindi, Tamil, Bengali, GujaratiMandarin, Japanese, KoreanIndonesian, Thai, VietnameseRussian, Polish, CzechNordic languages

If a specific trade publication matters to your sector and we don't already cover it, adding a source is routine work with a short lead time. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market Why demand concentrates here
United States & United Kingdom Highest publisher density and the fastest syndication chains to deduplicate.
Germany, France & Spain Multilingual monitoring with strong regional publisher ecosystems.
India Very high publisher volume across multiple languages and heavy republishing.
Singapore & UAE Regional financial and trade coverage hubs for Asia and the Middle East.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

South AfricaNigeriaKenyaEgypt

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 →

Who buys this data

Which teams buy news data

The same feed serves very different consumers — latency matters enormously to some and not at all to others.

Quantitative Researcher

Hedge funds, asset managers
The problem

News-based signals decay in minutes, and vendor feeds arrive with inconsistent timestamps and uncollapsed syndication that manufactures false volume spikes.

What we deliver

Streaming delivery with median sub-5-minute latency, separated publish and ingest timestamps, and deduplicated canonical records with entity salience.

Metric that moves

Signal decay window

Fundamental / Credit Analyst

Investment and credit teams
The problem

Covering hundreds of names means missing material developments in trade press that never reaches mainstream financial media.

What we deliver

Entity-filtered feeds across specialist trade press per name, with topic classification so operational news surfaces separately from market commentary.

Metric that moves

Coverage completeness

Risk & Compliance Lead

Banks, insurers, corporates
The problem

Adverse-media screening against name lists produces overwhelming false positives because matching is string-based rather than entity-resolved.

What we deliver

Entity-resolved screening with salience thresholds and negative-topic classification, cutting false positives without losing genuine hits.

Metric that moves

False positive rate

Comms & PR Director

Corporates and agencies
The problem

Coverage reports arrive next morning, after the news cycle has already formed, and count syndicated republication as separate coverage.

What we deliver

Real-time alerting on brand and executive mentions with deduplication, tier weighting and sentiment, routed to Slack or email within minutes.

Metric that moves

Response time

AI / ML Engineering Lead

AI labs and enterprise AI teams
The problem

Training or grounding a model on news requires clean, deduplicated, licence-clear text at volume — and web crawls deliver none of those properties.

What we deliver

Deduplicated full-text corpora in Parquet with provenance metadata, plus a documented archive suitable for training and RAG grounding.

Metric that moves

Corpus quality

Commodity / Energy Analyst

Trading and industrial firms
The problem

Supply disruptions surface first in regional and local press in the local language, days before international coverage.

What we deliver

Multilingual regional press monitoring with event-type classification for outages, strikes, weather and logistics disruption.

Metric that moves

Signal lead time

Use cases

How news data gets used

Four patterns, with measured outcomes.

Event-driven signal generation

Streaming entity-resolved news drives systematic strategies that react to earnings language, guidance changes, management departures and regulatory actions. Deduplication ensures a single wire story doesn't register as a volume spike, and salience scores filter passing mentions from articles genuinely about the entity.

Outcome: Signal pipelines that fire on genuine first publication rather than on the fifth republication of the same story.

Adverse media and sanctions-adjacent screening

Name lists are screened against entity-resolved news rather than string matches, with salience thresholds and negative-topic classification applied. This removes the bulk of false positives that make manual review queues unmanageable.

Outcome: Screening review queues reduced substantially while genuine adverse coverage still surfaces.

Supply chain and commodity disruption monitoring

Regional press in local languages is monitored for event types — port closures, plant outages, strikes, extreme weather, export restrictions — with geographic resolution, giving earlier warning than international coverage provides.

Outcome: Disruption awareness measured in hours rather than days on region-specific events.

News corpora for LLM training and RAG grounding

Deduplicated full-text archives with provenance metadata are delivered as partitioned Parquet for model training, or as continuously updated indexed feeds for retrieval-augmented generation where currency and citation traceability matter.

Outcome: Model grounding on a corpus where every passage traces to a dated, attributable source.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Investment firm · Singapore

Analysts were reading the same story from forty sources

Situation

A monitoring tool delivered high volume with no deduplication, so analysts spent significant time filtering republished versions of identical wire stories.

What we ran

Deduplicated, entity-resolved delivery with syndication chains collapsed to a single canonical record, filtered to the firm's watchlist entities.

Result

Article volume reaching analysts dropped substantially while genuine event coverage stayed intact.

Supply chain team · Germany

Disruption events were being learned about from suppliers, not from news

Situation

Port closures, strikes and plant outages reached the team through supplier emails, often days after the event was publicly reported.

What we ran

Sub-five-minute latency monitoring across logistics and industrial publishers, entity-resolved to the team's supplier and port watchlist.

Result

Disruption awareness moved ahead of supplier notification in most monitored events.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

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.

  • Real extraction from your actual sources
  • Returned inside two business days
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us for this work

Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

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.

Build vs buy

Should you build news collection in-house or hire it as a service?

Deduplication at scale is a genuinely hard engineering problem and it never stops needing tuning.

In-house build vs self-serve tool vs Actowiz managed service
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 48 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

Why deduplication is the single most important property of a news feed

If you evaluate one thing in a news data trial, evaluate deduplication. Everything downstream depends on it, and its absence is easy to miss until it has already corrupted months of analysis.

Consider mention volume as a signal. A company appears in 340 articles today versus a 40-article baseline. Without deduplication you might conclude a major event occurred. With deduplication you may find eleven genuine stories, republished across 329 outlets — a normal news day for a widely covered name. The uncollapsed version doesn't just add noise; it produces a confidently wrong conclusion.

How we handle it

  1. Content fingerprinting using shingled hashes that tolerate headline rewrites and minor edits while still matching substantially identical bodies.
  2. Canonical selection favouring the earliest reliable timestamp, with wire-service origination given precedence where detectable.
  3. Duplicate attachment rather than deletion — the canonical record carries the count and the republishing outlet list, because republication breadth is itself a signal of story importance.
  4. Revision tracking so a materially updated article links to its earlier version rather than appearing as a new story.

We deliberately keep duplicates accessible instead of discarding them. Collapsing to canonical is right for signal generation; the full republication set matters for PR measurement and reach analysis. You choose per use case.

News data for LLM training, RAG and AI search visibility

News has become one of the most sought-after training and grounding corpora, and also one of the most legally sensitive. Teams building retrieval-augmented systems need current, attributable, deduplicated text; teams training models need volume with clean provenance. Both need to know where the data came from.

What we provide for AI workloads

  • Deduplicated corpora. Training on forty copies of the same wire story overweights it forty times. Our canonical records prevent that, and the duplicate metadata is retained separately if you want to model republication.
  • Provenance on every record. Source URL, outlet, author, publication timestamp and capture timestamp, so any model output can be traced to an attributable source — increasingly a requirement rather than a nicety.
  • Paywall discipline. We never extract protected body text. For AI teams this matters more than for anyone else, because a training corpus containing improperly obtained content is a liability that surfaces long after ingestion.
  • Chunk-ready clean text. Boilerplate, navigation and encoding artefacts removed, so text goes straight to embedding without preprocessing.
  • Continuous updates for RAG. Streaming delivery keeps a retrieval index current, which is the entire point of grounding a model on news rather than relying on training-time knowledge.

On licensing: we can advise on the collection methodology and access status of every source, and we document it. We cannot grant you rights we do not hold, and any provider claiming blanket training rights over global news should be treated with suspicion. Our approach is to be precise about what each source permits so your legal team can make an informed decision.

How it works

How a news engagement goes live in 5 to 10 business days

Entity lists, topic filters and latency tier are configured during the pilot so the production stream matches your use case exactly.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

Streaming via webhook or WebSocket, REST/GraphQL API pull, or scheduled batch in JSON, JSONL or Parquet to S3, GCS, Azure Blob, SFTP, Snowflake, BigQuery or Databricks. Streaming and batch share one schema, so a batch pilot migrates to streaming without pipeline changes.

Compliance & data ethics

We extract only publicly accessible articles, never bypass paywalls or authentication, respect robots directives and publisher rate limits, and deliver metadata only where full text is protected. Each engagement includes a documented source list with access status per publisher so your legal team can assess licensing before signature.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment What we hold ourselves to
Pilot turnaround A real sample from your own sources within 48 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.

Why teams pick Actowiz for this work

  • Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
  • We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
  • QA is part of the service. Schema validation and sampled human review run before delivery, every run.
  • Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
  • Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
  • Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.
Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Entity resolution
Determining that a mention of a company, person or place refers to a specific known entity, so that monitoring a watchlist actually returns relevant coverage rather than name collisions.
Syndication cluster
The group of republished versions of a single original story across multiple publishers. Collapsing these to one canonical record is what turns raw news collection into usable monitoring.
Latency tier
How quickly an article reaches you after publication. Sub-five-minute delivery costs materially more than hourly because it requires continuous polling across the entire publisher set.
FAQ

News data: frequently asked questions

What buyers ask during evaluation.

We deliver headline, outlet, timestamp, author and any publicly visible summary, and we mark the record as paywalled. We do not extract protected body text, and we do not use subscription credentials, shared logins or paywall circumvention techniques of any kind.

This is a firm rule rather than a setting. A feed containing improperly obtained paywalled content creates liability for you as the recipient, not only for the provider, and that exposure typically surfaces long after ingestion when it is expensive to unwind.

Median under five minutes from publication to delivery on priority sources, which covers wires, major financial press and the outlets you designate as high-priority. Broader long-tail coverage runs on a longer polling cycle, typically 15 to 60 minutes, because polling 80,000 sources at wire frequency is neither economic nor welcome to publishers.

Every record carries both the publisher's stated publication time and our ingestion time, so you can measure our actual latency yourself rather than relying on our claim. We recommend doing exactly that during the pilot.

We resolve organisations to tickers and registry identifiers, and people and locations to typed entities with context. Accuracy is high on well-known entities and degrades on ambiguous names, small private companies and transliterated names in non-Latin scripts — which is true of every entity resolution system, ours included.

Each entity carries a salience score and a resolution confidence. For screening use cases we recommend filtering on both: it removes most false positives at a small cost in recall, and the tradeoff is tunable to your risk appetite.

Yes, on selected sources. Our archive extends multiple years for wires, major financial press and a substantial set of trade publications. Long-tail and regional sources have shallower history, and some have none before we began collecting them.

During scoping we give you exact date ranges per source rather than a headline claim, because a backtest built on a corpus with unacknowledged gaps produces results that look valid and are not. If your strategy needs uniform history across a specific source set, we will tell you plainly whether that exists.

It helps, provided you use both views. For measuring genuine story count, the canonical records are correct. For measuring reach and republication breadth, the duplicate set is what you want — a story picked up by 200 outlets has different value from one carried by three.

We deliver canonical records with the full republishing outlet list attached, so you can compute both story count and coverage breadth from the same feed. Suppressing duplicates entirely would destroy the reach measurement, which is why we attach rather than discard them.

Yes — 40+ languages, including full-text extraction and entity resolution in the major ones. Multilingual coverage is often the strongest argument for a managed feed, because regional developments surface in local-language press well before international outlets pick them up.

Optional machine translation of headline and body is available, delivered alongside the original text rather than replacing it. We keep the original because translation loses nuance that matters, particularly in regulatory and legal reporting.

Yes, and we recommend it strongly. Filters can be set on entities, entity salience thresholds, topics, outlet tier, country, language, sentiment range and keyword patterns, and combined with boolean logic.

Unfiltered global news is around 200,000 articles per four-hour window in our corpus. Almost no use case wants that. Most clients run a tightly filtered stream for alerting plus a broader scheduled batch for analysis, which keeps both cost and noise manageable.

We quote every news monitoring engagement individually, because a real number depends on scope: source count, record volume, refresh frequency and delivery method. Anyone quoting you a price before understanding those four things is guessing.

Publisher count, latency requirement and whether you need full-text or metadata-only delivery are the main drivers. Sub-five-minute latency costs meaningfully more than hourly.

The process is short: one scoping call, a free pilot on your own sources within 48 hours, then a fixed monthly quote. No per-request metering, no overage billing, and field or source additions are handled inside the retainer rather than re-quoted. Request a quote.

Technically, straightforwardly — we deliver deduplicated, clean, provenance-tagged text in Parquet built for exactly that. Legally, it depends on the sources, and you should treat any provider claiming otherwise with caution.

What we do is document the access status and collection methodology of every source in your feed, so your legal team can assess training rights source by source. What we cannot do is grant rights we do not hold. If training is your use case, raise it during scoping — source selection changes materially, and some sources are better excluded from a training corpus than included.

Test the service on your own coverage brief

Give us the entities and topics you track. We return deduplicated, entity-resolved news records within 48 hours, at no cost.

Free pilot, no obligation, no card. You'll have a fixed monthly quote after one scoping call.
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Why Global Leaders Trust Actowiz

Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.

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7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
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4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
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200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
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9.2M
Automated Workflows
Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
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270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
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380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
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Connect the Dots Across
Your Retail Ecosystem

We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

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Analytics Services
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Ad Tech
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Price Optimization
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Business Consulting
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System Integration
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Market Research
Become a Partner →

Popular Datasets — Ready to Download

Browse All Datasets →
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Amazon
eCommerce
Free 100 rows
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Zillow
Real Estate
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DoorDash
Food Delivery
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Walmart
Retail
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Booking.com
Travel
Free 100 rows
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Indeed
Jobs
Free 100 rows

Latest Insights & Resources

View All Resources →
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Blog

EU AI Act for Data Teams: What Scrapers Must Change in 2026

The EU AI Act impact on web scraping & AI training data GPAI transparency, copyright reservations, prohibited practices & a compliance checklist from Actowiz.

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Case Study

B2B Supplier Automates Government Tender Discovery from GeM & eProcure

How a B2B supplier replaced manual tender-portal checking with an automated, filtered feed of relevant government tenders from GeM and CPP/eProcure never missing a bid deadline again.

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Report

FIFA World Cup 2026 Aftermath: Hotel & Airfare Normalization in Host Cities (Data Study)

Actowiz Solutions tracks post–World Cup 2026 travel pricing — hotel ADR & airfare normalization across host cities, event-premium decay data & lessons for travel teams.

Start Where It Makes Sense for You

Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

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Enterprise
Book a Strategy Call
Custom solutions, dedicated support, volume pricing for large-scale needs.
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Growing Brand
Get Free Sample Data
Try before you buy — 500 rows of real data, delivered in 2 hours. No strings.
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Just Exploring
View Plans & Pricing
Transparent plans from $500/mo. Find the right fit for your budget and scale.
Get in Touch
Let's Talk About
Your Data Needs
Tell us what data you need — we'll scope it for free and share a sample within hours.
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    Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment.
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    Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
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    US-Based SupportOffices in New York & California. Aligned with your timezone.
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    ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.
Request Free Sample Data
Fill the form below — our team will reach out within 2 hours.
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Free 500-row sample · No credit card · Response within 2 hours

Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

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Free 500-row sample · No credit card · Response within 2 hours