Data collection your AI can trust.
Webclat designs, collects, validates, and delivers model-ready behavioral datasets - including event schemas, identity resolution, conversion signals, lineage, and warehouse-ready pipelines.
We deliver compiled event data, cross-device identity stitching, offline conversion schemas, real-time lineage, and privacy-safe data warehouse delivery.
Relationships Made Visible
A spatial representation of one customer's identity - web sessions, mobile events, and offline conversions resolved into a single trusted record.
flawed data in,
flawed predictions out.
Models do not fail because of algorithms. They drift, miss fraud signals, and hallucinate because the behavioral data underneath was never engineered: events fire inconsistently, identities fragment across devices, offline conversions never arrive. We craft the data layer first.
See the craft process →Scattered & Fragmented Inputs
Source logs arrive across disparate formats, schemas, and standards. Without rigorous feature instrumentation and data contracts (Stone), the baseline is volatile.
Disconnected Real-World Outcomes
Models learn from behavior, but lack the ground-truth of what happened next. True calibration requires joining delayed offline conversions to event sequences (Wood).
Opaque Feature Lineage
Data scientists consume features without context. Model teams need full semantic dictionaries and privacy-compliant data governance (Glass).
A craft process
for trusted AI data
Click on each stage in the pipeline to explore how we curate raw metrics, validate accuracy consensus, and lock in model-ready data.
Instrument
We engineer tracking plans and event schemas across web, mobile, and server-side (Segment, Adobe Launch, Tealium, Google server-side tagging).
What We Deliver
Concrete data engineering outputs designed for production use across your analytics and machine learning pipelines.
Craft proven
in production
Raw Challenge
A Fortune 100 electronics retailer's cross-device sessions fragmented attribution and product recommendation models.
Before Webclat
35% of offline purchases unlinked to web activity
Production Outcome
99% deterministic and probabilistic ID resolution across platforms
Delivered
Segment + Snowflake pipeline, resolved Identity Graph, QA dashboards
Raw Challenge
A Big Five Canadian bank needed its digital behavioral data - millions of daily web and app events - reliable enough to feed production AI/ML pipelines for behavior prediction and risk.
Before Webclat
Untrusted clickstream feeds causing model drift and false positives
Production Outcome
Analytics feeds trusted as direct, low-latency ML inputs for risk teams
Delivered
Governed Adobe Analytics schemas, automated QA, production event feeds
Raw Challenge
A global vehicle-rental group needed consent management unified across international digital properties, each governed by different privacy regulations.
Before Webclat
38% events missing verifiable consent state across EU and US
Production Outcome
Consent-aware collection compliant across jurisdictions (GDPR/CCPA)
Delivered
OneTrust integration, consent-gated tracking architecture, privacy audit trails
Raw Challenge
An overwhelming catalog and even more varied customer preferences; features shipping without full tracking.
Before Webclat
Key features untracked - no visibility to measure conversion or improve it incrementally.
Production Outcome
Feature-level schema powering A/B testing and personalization - conversion measurable and improvable release by release.
Delivered
Feature tracking schema, experimentation-grade event QA, preference signals for personalization.
Raw Challenge
The revenue-critical booking and checkout flow ran on product-data logic scattered across the codebase.
Before Webclat
Any product-string change took weeks of sprint work; drop-off causes hard to isolate.
Production Outcome
Product logic centralized in one place - changes ship in minutes; correct merchandising variables and events fire per context (a room, a show); properties enriched.
Delivered
Centralized product data layer, funnel drop-off measurement, Web SDK migration with validated parity.
Raw Challenge
Dozens of member clubs, each on its own subdomain with its own analytics.
Before Webclat
Sessions and attribution broke on every crossing from club site to member subdomain.
Production Outcome
National tracking layered on top of each club's own - coexisting without collision, journeys unbroken, member visits countable end to end.
Delivered
Multi-instance collection architecture, cross-subdomain session continuity, federation-wide tracking guidelines.
Raw Challenge
Onboarding split across legacy web, a redesigned platform, and the mobile app.
Before Webclat
Three platforms, three pictures - no unified view of the prospect-to-customer journey.
Production Outcome
One schema across all three - measurable from first visit to KYC-verified customer, including the moment a prospect becomes an internal-ID customer, nothing over-collected.
Delivered
Unified cross-platform schema, migration parity, compliance-reviewed event design.
Industries in focus
Four elements.
Perfect integration.
Our design system maps the stages of dataset crafting into four elements. Together, they create a robust, production-ready data pipeline.
Solid as stone
Structure: schemas, tracking plans, governance
Event taxonomies, tracking plans, and schema governance across web, mobile, and server-side sources. Every field named, typed, and documented before a single event flows.
Read integration notes →Stone establishes the immutable logic. We instrument the capture of online and offline events, enforcing typed schemas and data contracts at the source. This structural integrity prevents training-serving skew.
Crafted like wood
Human calibration: identity resolution, field semantics
Machines capture; craftsmen calibrate. We manually verify tracking against real user journeys and stitch identities across web, mobile, and offline conversions into unified profiles your data science team can model on without guessing.
Read integration notes →Wood breathes organic causality into raw logs. By stitching identities across web, mobile, and offline conversions, we join delayed real-world outcomes to behavioral sequences so models learn from what actually happened.
Clear as glass
Transparency: lineage, causal integrity, compliance
Separating correlated touchpoints from outcome-driving events, performing deduplication, drift detection, and causal signal curation before they reach the model.
Read integration notes →Glass ensures clarity in the signal. We denoise and deduplicate datasets in the warehouse, engineering causal feature vectors that separate correlated noise from true outcome-driving events.
Lasts like bronze
Delivery: compiled, warehouse-native, built to last
Governed datasets delivered where the work happens - Amazon Athena, S3, Parquet, your warehouse, your CDP audiences, your CRM - compiled to stay useful as model versions advance.
Read integration notes →Bronze represents the lasting finish. We orchestrate ETL with validation gates, ensuring data scientists consume well-documented, privacy-preserving behavioral feature vectors that pass any regulatory audit.
Audit trails for
precise model decisions
Data cannot train models if it sits behind opaque processes. Our Glass workflow turns metadata into traceable lineage logs. From complete audit tracking to statistical validation graphs and consent states, your team gets a clear record of how each dataset was defined, checked, and prepared - datasets your teams can trust in production.
"The best AI pipelines operate with complete visibility. Glass converts disconnected input streams into traceable, verifiably accurate assets."
The unified power of reliability,
craft, and insight
Stone, Wood, Glass, and Bronze are designed to work as one production system, not as isolated services. Tracking architecture creates a stable foundation (Stone), identity refinement improves signal quality (Wood), governance makes every dataset auditable (Glass), and delivery puts governed data where teams can actually use it (Bronze).
The result is behavioral data that stays trustworthy across analytics, decisioning, and model iteration. Instead of fragile pipelines and disconnected fixes, Webclat builds a dataset foundation that withstands rapid model versioning.
The trustworthy data foundation your AI systems need.
Book a Webclat audit to find gaps in your event collection, identity resolution, consent state, conversion signals, and model-ready datasets.