Artisanal data
craft for AI
We transform raw information into model-ready behavioral datasets. Expert-engineered, verified, and compiled to stay useful as model versions advance.
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 activation cycle →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 carve raw metrics, validate accuracy consensus, and lock in Market Share.
Instrument
We engineer tracking plans and event schemas across web, mobile, and server-side (Segment, Adobe Launch, Tealium, Google server-side tagging).
Craft proven
in production
Industries we serve
Four elements.
Perfect integration.
Our proprietary design system maps the stages of dataset crafting into four tactile physical materials. Together, they create raw data's finest synthesis.
Stone: Tracking Architecture
Event Capture & Contracts
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.
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Wood: Expert Human Refinement
Cross-Device Stitching
Machines capture; craftsmen calibrate. We manually verify tracking against real user journeys and transform raw behavior — attribution logic, session semantics, conversion definitions — into fields your data science team can model on without guessing.
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Glass: Quality & Compliance Audit
Attribution & Cleansing
Real-time lineage from source event to delivered dataset. Consent travels with every record — collection and processing engineered to comply with GDPR, CCPA/CPRA, and applicable privacy laws. Data minimization by default.
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Bronze: Lasting Handoff
Consent & Cataloging
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 →Unveiling clarity for
precise model decisions
Data cannot train models if it sits behind opaque processes. Our Glass workflow turns metadata into absolute visual clarity. From complete audit log tracking to statistical validation graphs, your team retains absolute mastery over the datasets that fuel your proprietary architectures.
"The finest AI pipelines operate with absolute visibility. Glass converts dark, chaotic input streams into traceable, verifiably pure assets."
The unified power of reliability,
craft, and insight
When Stone, Wood, Glass, and Bronze integrate, they synthesize something greater. This is the icosahedron of Webclat: an intricate geometric layout representing flawless technical synthesis. The data becomes an impenetrable corporate asset.
By structuring the architecture (Stone), stitching identities across devices (Wood), engineering causal signals (Glass), and locking in privacy-compliant semantics (Bronze), we synthesize datasets that withstand rapid model versioning.
