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System Design: The DataInc.ai Platform

A 5-layer marketing data reliability architecture — from connector ingestion through AI-powered observability to autonomous alerting and remediation.

Layer 1 · Connectors & Ingestion

Google AdsMeta AdsGA4TikTokDV360...

Layer 2 · Mapping & Discovery

Auto-DiscoverySchema ScanLineageColumn Profiling

Layer 3 · Taxonomy & Governance

UTM RulesNaming ConventionsDrift DetectionData Contracts

Layer 4 · Observability

Revenue ImpactSpend ParityAnomaly DetectionSLA Monitoring

Layer 5 · Alerting & Automation

Incident MgmtEscalationAuto-RemediationNotifications

Data Warehouse Layer

SnowflakeBigQueryDatabricksRedshift

Measurement Models

MMMMTALift StudiesForecasting
Reasoning & Action · AI-tool Interaction
DataInc AI

AI Backend

Pipeline Orchestration
Autonomous Action Proof
Auto-Discovery Engine
Data Contract Enforcement
Identity & Access
Context Pattern GraphDecision intelligence layer
Revenue-at-Risk Scoring
Marketing-Aware MemoryChannel / Campaign / Historical

Agent Node

MMM Input Validation

Agent Node

Attribution Integrity

Agent Node

Taxonomy Governance

Agent Node

Creative Integrity

Agent Node

Identity Monitoring

Agent Node

Spend Parity

Agent Node

Incident Response

Agent Node

Data Drift Detection

The 5-Layer Architecture

DataInc.ai monitors marketing measurement pipelines across five interconnected layers. Each layer feeds into the DataInc AI backend, which orchestrates specialized agent nodes to continuously validate, govern, and remediate data quality issues.

Layer 1

Connectors

Ingest data from ad platforms, analytics tools, CRMs, and marketing APIs into the warehouse.

Layer 2

Mapping

Auto-discover schemas, profile columns, infer relationships, and map lineage across tables.

Layer 3

Taxonomy

Enforce UTM rules, naming conventions, and data contracts. Detect semantic drift automatically.

Layer 4

Observability

Score revenue-at-risk, monitor spend parity, detect anomalies, and track SLA compliance.

Layer 5

Alerting

Classify incidents, escalate by severity, trigger auto-remediation, and notify stakeholders.

Auto-Discovery Engine

Scans warehouse schemas, classifies marketing tables, profiles columns, and infers join paths — all without manual configuration.

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Revenue-at-Risk Scoring

Quantifies the dollar impact of every data quality issue so teams prioritize fixes by business value, not guesswork.

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Context Pattern Graph

Extracts marketing decision context from documents and workflows, building a structured graph of precedents and decision traces.

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Incident Monitoring

Detects data incidents through anomaly detection, pattern matching, and threshold alerts, with built-in escalation and resolution workflows.

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Measurement Validation Agents

Specialized AI agents continuously validate the integrity of your measurement models and marketing data across every touchpoint.

  • MMM Input Validation — Validates spend, impressions, and conversions for model accuracy. Reconciles across sources and performs time-series stability checks.
  • Attribution Integrity — Cross-platform validation, divergence detection, conversion deduplication, and privacy-era attribution handling.
  • Identity Monitoring — Tracks identity join rates, resolution quality, match accuracy, and duplicate detection across devices.
  • Creative Integrity — Monitors creative ID consistency, detects metadata drift, and validates performance attribution at the creative level.

Governance & Reliability Agents

Autonomous agents that enforce data quality standards, detect drift, and ensure compliance across your marketing data stack.

  • Taxonomy Governance — Enforces naming conventions, UTM rules, and hierarchy standards. Catches violations before they propagate downstream.
  • Data Drift Detection — Identifies schema changes, value distribution shifts, and semantic drift that silently corrupt measurement outputs.
  • Spend Parity — Reconciles spend data across ad platforms, invoices, and warehouse records to detect discrepancies and revenue leakage.
  • Data Contract Enforcement — Automated schema and quality rule validation with SLA monitoring, violation tracking, and compliance reporting.

How the AI Backend Works

1

Ingest & Discover

Connectors pull data from ad platforms and warehouses. Auto-Discovery maps schemas and relationships.

2

Classify & Govern

Taxonomy rules and data contracts are applied. Marketing-aware classification detects channels, campaigns, and conversions.

3

Monitor & Score

Agent nodes validate MMM inputs, attribution, identity, and creative data. Revenue-at-risk scores quantify impact.

4

Alert & Remediate

Incidents trigger alerts, escalate by severity, and where possible, auto-remediate — closing the loop autonomously.

Ready to see it in action?

Explore how DataInc.ai's 5-layer architecture and AI agents can transform your marketing measurement infrastructure.

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