The Client

A venture-backed AI startup in the United States building a retail-focused language model — a shopping copilot designed to answer product questions, compare alternatives, and reason about prices across categories. Their model architecture was ready. Their data was not.

The Challenge

The team had attempted in-house collection for four months and hit the wall every AI startup hits:

Scale without structure. Generic crawl dumps gave them billions of tokens of boilerplate — navigation text, cookie banners, duplicated templates — and almost no clean product semantics (title, brand, price, specs, reviews) in usable form.
Anti-bot attrition. Coverage of major US retail domains kept collapsing mid-run as detection systems escalated. Engineering time went to firefighting scrapers instead of training models.
Freshness decay. A shopping copilot answering with last quarter's prices is worse than useless. They needed refresh cycles, not a one-time dump.
Compliance pressure. Their enterprise pilot customers demanded documentation: where did the training data come from, was PII handled, could lineage be produced for review.

The brief to Actowiz Solutions: 50 million structured product records across US retail, multilingual review context included, delivered training-ready, with weekly refresh on a 5-million-record hot subset.

The Actowiz Solution

1. Source architecture.

We mapped a 200+ domain universe across marketplaces, big-box retailers, category specialists (beauty, electronics, home), and D2C brands — weighted to match the client's target category distribution.

2. Agentic extraction at scale.

Our self-healing scrapers ran the collection with automatic re-mapping when site layouts changed — no coverage holes mid-run, no engineering escalations on the client side.

3. Structuring & normalization.

Every page rendered into a typed JSON record: title, brand, canonical category, price + currency, spec key-values, rating distribution, review summaries, image metadata, timestamps. Currencies, units, and category taxonomies normalized across all sources.

4. Deduplication & quality scoring.

Exact-hash plus MinHash near-duplicate removal collapsed cross-domain template repeats; every record shipped with a quality score so the training team could weight or filter. Suspected AI-generated review text was flagged for exclusion.

5. Compliance by construction.

PII masked at the edge (reviewer names, seller contact data never entered storage), full data lineage per record, and a documentation pack formatted for enterprise procurement review.

6. Delivery & refresh.

Parquet drops to the client's S3, partitioned by category and collection week. A 5M-record "hot set" (fast-moving categories: electronics, beauty, grocery) refreshed weekly with hash-based delta detection — only changed records re-delivered.

Sample Record Structure (Illustrative)

{
  "record_id": "us-retail-2026-06-14-4471820",
  "domain": "example-bigbox.com",
  "collected_at": "2026-06-14T11:02:41Z",
  "title": "Stainless Steel Air Fryer 6QT",
  "brand": "SampleBrand",
  "price_usd": 89.00,
  "category_path": ["Home", "Kitchen", "Air Fryers"],
  "specs": {"capacity_qt": 6, "wattage": 1700},
  "rating_avg": 4.5,
  "review_count": 8912,
  "review_themes": ["easy cleanup", "basket size", "noise"],
  "quality_score": 0.95,
  "pii_status": "masked_at_edge",
  "lineage_id": "lin-8823-a"
}

Engagement Metrics (Representative)

Metric	Value*
Total structured records delivered	50,000,000
Source domains	200+
Raw-to-clean reduction (dedup + boilerplate)	~38% removed
Records with full lineage & PII masking	100%
Hot-set refresh cadence	Weekly (delta-based)
Time to first pilot delivery	3 weeks
Full corpus delivery	11 weeks
Client engineering hours spent on scraping	0

Representative engagement figures — illustrative of project structure.

The Outcome

The client's retail LLM moved from prototype to enterprise pilots with a corpus their customers could audit. Internally, the team reported the shift that matters most for an AI startup: their engineers went back to modeling. Retrieval-facing features (price answers, comparison reasoning) ran on the weekly-refreshed hot set, keeping the copilot's answers aligned with live market reality — the difference between a demo and a product.

The engagement has since expanded into a second workstream: multilingual product data (Spanish, Hindi, Arabic) to support the client's international roadmap.

Why This Pattern Repeats

Every AI team building on commercial reality — shopping, travel, food, real estate — eventually faces the same equation: months of in-house scraping pain versus weeks to training-ready data with a specialist. As we documented in our 2026 Web Scraping Industry Report, the majority of generative AI models now train primarily on scraped web data, and the differentiator has shifted from access to curation quality.

Frequently Asked Questions

How long does a 50M-record dataset take to deliver?

Pilot subsets typically ship in 2–4 weeks; full corpora of this scale in 2–3 months depending on source complexity, with refresh pipelines running from week one.

Can the dataset be customized to our category mix?

Yes — source universe, category weighting, languages, fields, and refresh cadence are all scoped to your model objectives.

How is compliance handled for AI training data?

Public catalog data only, PII masked at the edge during collection, per-record lineage, and documentation packs built for enterprise and regulatory review (GDPR, CCPA, DPDP, EU AI Act workflows).

Do you deliver review text for training?

We deliver review-derived signals (themes, summaries, distributions) and can scope raw review corpora with appropriate filtering and masking based on your legal team's requirements. Contact Actowiz Solutions to scope a pilot.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

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1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

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2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

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1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner