Your AI models deserve complete data, not whatever survives the browser.
Ad blockers delete 20-40% of behavioural events. Safari expires cookies in 7 days. Client-side tags batch and sample. The result: your ML models train on a biased, incomplete picture of reality. Server-side collection fixes the input layer.
20-40%
Signal Loss
Behavioural events blocked by ad blockers and privacy tools, invisible to your models
7 days
Safari Cookie Cap
Before every Safari user becomes 'new' again, fragmenting behavioural sequences
<50ms
Processing Latency
Server-side event processing and Kafka delivery for real-time feature stores
99.99%
Delivery Rate
Events delivered with exponential backoff retry and dead-letter queues
The data quality problem for AI
AI models are only as good as their training data. Client-side collection introduces three systematic biases that no amount of model tuning can correct.
Signal Loss = Model Bias
30% of high-value user behaviour is invisible to client-side tags. Your models learn from a biased sample that systematically under-represents tech-savvy, privacy-conscious users.
Cookie Expiry = Broken Journeys
Recommendation engines need complete behavioural sequences. Safari's 7-day cookie cap fragments user journeys into disconnected sessions, destroying sequential pattern recognition.
Client-Side Batching = Stale Data
Tags that batch events every 30 seconds miss real-time signals for live personalisation. By the time your feature store updates, the user has already moved on.
Why server-side collection is better for AI/ML
Server-side collection solves the input layer for machine learning. Complete signals, continuous identity, and real-time delivery to your data infrastructure.
Complete Signals
First-party delivery via your own subdomain recovers the 20-40% of events that ad blockers and privacy tools silently delete. Your training data represents all users, not just those without ad blockers.
Continuous Identity
Server-set cookies with 400-day expiry, fully ITP-exempt. Models see the complete customer lifecycle as a single identity, not dozens of fragmented 7-day visitors.
Real-Time Streaming
Events flow through Kafka in milliseconds, not 30-second client-side batches. Feed real-time feature stores for live personalisation and instant next-best-action decisioning.
Schema-Enforced Quality
The Org Data Layer validates and enriches every event before it reaches your data warehouse. No more null fields, type mismatches, or inconsistent property names breaking your pipelines.
Raw, Unsampled Data
Every event is delivered, not sampled. Vendor-side sampling hides the long-tail patterns that matter most for anomaly detection, fraud models, and niche segment discovery.
PII-Safe by Design
Hash, mask, or strip PII at collection time in the Org Data Layer. Your ML pipelines receive clean, privacy-safe data without building separate PII scrubbing infrastructure.
From browser to ML pipeline
A single collection endpoint feeds your entire data infrastructure. No duplicate integrations, no conflicting schemas, no data reconciliation.
One event, two destinations
A single purchase event is simultaneously delivered to BigQuery for model training and to a real-time webhook for your feature store. Schema validation, PII handling, and consent enforcement happen once at the Org Data Layer.
- Batch delivery to BigQuery with configurable flush intervals
- Real-time webhook delivery for feature store ingestion
- PII hashed or stripped before data leaves the processing layer
- Version-controlled YAML configs reviewed in pull requests
# ai-dual-delivery.yaml — Events to warehouse + feature store
name: ai_dual_delivery
trigger:
event: track
conditions:
- field: event
operator: in
value: [Product Viewed, Added to Cart, Purchase]
org_data_layer:
schema: ecommerce/interaction-v2
pii:
email: sha256
phone: strip
ip_address: mask
consent:
required: [analytics]
pipelines:
- name: bigquery_training_data
vendor: bigquery
dataset: ml_training
table: user_interactions
mapping:
- source: user_id
target: user_id
- source: event
target: event_name
- source: properties
target: event_properties
- source: context.session_id
target: session_id
- source: timestamp
target: event_timestamp
batch: true
batch_size: 500
flush_interval: 10s
- name: realtime_feature_store
vendor: webhook
endpoint: https://features.internal/ingest
format: json
mapping:
- source: user_id
target: entity_id
- source: event
target: feature_event
- source: properties
target: feature_values
- source: timestamp
target: event_time
realtime: trueBuilt for AI/ML use cases
Complete, real-time, schema-enforced behavioural data powers every stage of your machine learning lifecycle.
Recommendation Engines
Complete browsing and purchase sequences give collaborative filtering models the full picture. No more phantom users or truncated sessions.
Demand Forecasting
Accurate traffic and conversion signals at every funnel stage. Models trained on complete data produce tighter confidence intervals.
Personalisation
Real-time behavioural features streamed in milliseconds. Serve personalised experiences based on what users are doing right now, not 30 seconds ago.
Customer Lifetime Value
Continuous 400-day identity resolution means CLV models see the full customer lifecycle, not fragmented 7-day windows stitched together.
Fraud Detection
Every event delivered, unsampled and unblocked. Fraud models need the outliers that ad blockers and sampling would otherwise hide.
Next-Best-Action
Schema-enforced, PII-safe event streams feed decisioning engines with clean, real-time signals. No stale batches, no missing context.
Start collecting complete, real-time data for your AI models
Book a technical walkthrough where we show you how Datafly Signal delivers complete behavioural data to your warehouse, feature store, and ML pipelines — with zero signal loss.