Beyond cookies and device IDs
The deprecation of third-party cookies and increasing restrictions on mobile advertising identifiers have created a gap in how advertising teams understand audiences. Veridia Context fills that gap with a fundamentally different approach.
Rather than relying on persistent identifiers that are being phased out, Context uses a proprietary neural classification model trained on billions of European programmatic events to infer audience relevance, data precision, and contextual meaning from the attributes available in each bid request.
Quality Scoring
Each incoming event receives a confidence score based on data precision, source reliability, and contextual consistency — so your models only work with data you can trust.
Audience Segmentation
Probabilistic audience segments derived from contextual attributes, without needing deterministic user-level identifiers.
Centroid Detection
Adaptive algorithms identify and filter IP-derived false-precision records that plague bidstream data, eliminating up to 94% of low-quality location events.
Continuous Learning
Context models retrain on a rolling basis using the latest European auction data, adapting to shifts in SSP behavior, app ecosystems, and regulatory changes.
Trained on European data
Generic global models underperform in European markets due to the fragmented SSP landscape, multilingual content environments, and stricter consent requirements that reduce data availability. Context's models are purpose-built for this environment, trained exclusively on European programmatic data spanning 18 months and 38 markets.
The result is materially better precision when scoring data quality, classifying context, and building audience segments for European campaigns — compared to global platforms applying the same model everywhere.
Explore what Context can do
Request a demo and we'll show you quality scoring and audience segmentation on your own data.