Product data intelligence
Catalog enrichment, normalization, taxonomy, attribute extraction, and entity resolution across fragmented product ecosystems.
Commerce intelligence, engineered
Enterprise-ready product intelligence from the open web.
Turn volatile retail, social, and media signals into governed data your teams can trust—at the scale your decisions demand.
- 2017
- Operational experience
- AI + human
- Designed for difficult data
- Global
- Specialist delivery network
Built for high-consequence decisions
Data operations that keep pace with the market.
When the signal lives in product pages, reviews, videos, retailer promotions, or unstructured content, ordinary data pipelines fall short. We design the right blend of automation, AI, and expert review to make it useful.
Capabilities
Intelligence for the complete product lifecycle.
Every engagement is designed around the decision, source landscape, quality threshold, and handoff format that matter to your team.
Digital shelf intelligence
Availability, pricing, promotion, assortment, and creative visibility signals across the retailers and marketplaces that matter.
Review intelligence
Multilingual review collection, classification, and voice-of-customer analysis that turns opinion at scale into usable evidence.
Visual & video intelligence
Brand, product, and placement detection across video, social media, display creative, and image-led retail experiences.
AI quality operations
Human-in-the-loop evaluation, dataset QA, annotation, and targeted model-improvement workflows for teams that cannot accept guesswork.
Custom data pipelines
Purpose-built web acquisition, validation, harmonization, and delivery systems for complex sources and evolving business questions.
Delivery model
Designed around the work, not a one-size platform.
We decompose complex workflows into the right software, AI, and specialist-review components—then deliver the combined output against a clear quality bar.
- 01
Discover
Define the decision, signal sources, data rights, output requirements, and success measure.
- 02
Design
Build the most efficient operating model across automation, AI, and human assurance.
- 03
Deliver
Acquire, structure, classify, validate, and quality-check data at the required cadence.
- 04
Operationalize
Hand off governed data in the format and workflow your teams already use.
Selected work
Evidence that lives beyond the dashboard.
We retain the discretion expected in enterprise work while giving decision-makers a clearer view of the problems we solve.
Fortune 100 CPG
Retail promotional exposure, quantified.
We paired retailer-page imagery, placement signals, and structured tagging to measure share of promotional visibility across major retailers.
Fortune 500 consumer brands
Global review corpus, made useful.
Millions of reviews across languages and platforms were gathered, tagged, and delivered in client-specific data structures.
Brand intelligence
Video placement at scale.
Automation, machine learning, and specialist review identified meaningful brand presence across linear television and social video.
Trend intelligence
Signals before they become obvious.
Style experts and structured web signals revealed emerging patterns across short-form video and retail culture.
The Tagging Ninja difference
More than a model. More than a marketplace.
- Signal-first design
We start with the business question, not a predetermined tool.
- Human assurance where it counts
Expert review steps in where automation lacks context or confidence.
- Delivery that fits your operation
Outputs are structured for the systems, teams, and cadence you already have.
Start with the decision
What would better data make possible?
Tell us about the question, the sources, and the scale. We will come back with a practical path to an answer.
For general inquiries, email sales@tagging.ninja.