Data Foundation

For AI

“AI fails because of bad data.  We fix that first.”

Skip This and Your
AI Pitch Collapses

Every AI feature we build sits on a clean, unified, governed data layer. Without it, even the best models hallucinate, mis-score, and lose user trust in days.

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01

Data Cleaning, Deduplication & Normalization

Garbage in, garbage out - AI doesn't fix dirty data, it amplifies it. We audit, scrub, and standardize your records before a single model touches them.

  • Duplicate contact & account detection + merge
  • Field-level normalization (phone, address, date formats)
  • Blank-field analysis & enrichment strategy
  • Ongoing data quality scoring dashboards
Foundation Layer
🔗
02

Data Unification Across Systems

Your customer lives in five different systems. AI needs one coherent view. We stitch CRM, ERP, marketing, and support data into a single real-time profile.

  • Identity resolution across platforms
  • Data Cloud profile unification setup
  • Conflict resolution rules & golden record logic
  • Real-time sync vs. batch reconciliation strategy
Unification Layer
⚙️
03

Data Pipelines (CRM, Marketing, External Sources)

Static exports don't power AI. We build event-driven pipelines that keep your models fed with fresh, accurate signals - from every source that matters.

  • Salesforce → Data Cloud ingestion pipelines
  • Marketing automation & CDP integrations
  • Third-party data ingestion (firmographics, intent)
  • Pipeline monitoring, alerting & SLA management
Pipeline Layer
🛡️
04

Data Governance & Model Readiness

Uncontrolled data creates uncontrollable AI. We put guardrails, access policies, and quality gates in place so your models always train and infer on trustworthy data.

  • Data stewardship roles & ownership mapping
  • PII classification & consent management
  • Model readiness assessment & feature store prep
  • Lineage tracking & audit trail configuration
Governance Layer
87%
of failed AI projects trace back to data quality issues
3.2×
higher model accuracy on clean, unified data vs. raw CRM exports
40%
avg. duplicate record rate we find in new Salesforce orgs
14d
typical time to a clean, governed data foundation with our process

We Build the Layer
AI Stands On

Before writing a single prompt or activating a single Einstein feature, we establish the data bedrock. Five distinct layers - each validated before the next begins.

  • Data Audit First: We assess record quality, completeness, and duplication rates across every object before recommending a path.
  • No Lift-and-Shift: We don't just move dirty data to Data Cloud. We transform it in transit with validated business logic.
  • AI Readiness Score: Every engagement ends with a scored baseline - so you know exactly how model-ready your data is.
  • Continuous Governance: We don't hand off and walk away. We configure live data quality monitoring that alerts before problems compound.
Data Maturity Stack → AI Readiness
Raw Source Data
CRM exports, marketing data, ERP feeds - unprocessed
Layer 1
Cleaned & Normalized
Deduped, formatted, enriched, validated against rules
Layer 2
Unified Customer Profile
Single identity resolved across all source systems
Layer 3
Governed & Compliant
PII masked, consent enforced, lineage tracked
Layer 4
AI-Ready Feature Store
Model-ready signals, calculated insights, real-time triggers
AI Layer ✦
Get Started Today

Know Your Data Readiness
Before You Build on It

We'll run a data quality assessment on your Salesforce org and hand you a scored readiness report - no strings attached.

Request Data Audit

Frequently Asked Questions

Everything you need to know about building a strong data foundation for AI with Kizzy Consulting.

Why is data foundation critical before implementing AI?
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AI fails because of bad data. We fix that first.

  • AI models depend on clean, structured, reliable data
  • Poor data leads to inaccurate outputs and failed automation
  • A strong foundation ensures real, measurable business outcomes
What does Kizzy Consulting do in data cleaning and preparation?
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  • Remove duplicate and inconsistent records
  • Normalize formats across systems
  • Fill missing or incomplete data fields
  • Structure data for AI and analytics readiness
How do you unify data across CRM and other systems?
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  • Connect CRM, marketing, and external platforms
  • Create a single source of truth
  • Break data silos across departments
  • Enable consistent cross-system data flow
What is included in your data pipelines and governance?
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  • End-to-end pipelines from ingestion to transformation
  • Automated workflows for continuous updates
  • Governance for quality, compliance, and security
  • AI-ready datasets optimized for modeling
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Let's discuss how our AI-first approach can drive innovation and efficiency in your organization.

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