Data Health Audit
A Data Health Check with a quality index, detected anomalies and correction priorities.
Data Architecture & Predictive Analytics
AI Data Intelligence
Clean, standardize and enrich your company's database using algorithms and Artificial Intelligence. Turn disorganized records into a reliable source of truth that powers strategic decisions and predictive models.
Technology & Processing
Data Ecosystem
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Google Gemini
ClaudeReal-time normalization, enrichment and scoring
Diagnosis
From bad-data friction to a Data First system
A direct comparison between the current operation and the standard we implement across your database.
Current situation / Problem
- Inefficient ProspectingTeams wasting hours manually researching each company's profile, industry and size.
- Operations & ITDatabases with duplicates, formatting errors and incomplete fields that break analytics.
- Sales LeadershipUncertainty about which prospects to prioritize or which customers are at risk of churning.
Our Solution: AI Data Intelligence
- Inefficient ProspectingAn Automatic Enrichment pipeline connected to APIs that completes each account's profile in seconds from just an email address.
- Operations & ITDeduplication and spelling-correction workflows that raise the data health index above 95%.
- Sales LeadershipPredictive Lead Scoring algorithms and Churn Prediction built directly into your CRM.
Roadmap
Implementation Phases and Methodology
A gradual, auditable rollout, with a verifiable KPI at the close of each phase.
- Phase 01
Planning & Audit (Data Health Audit)
In-depth diagnosis (AS-IS) looking for anomalies and duplicates, and establishing the data health baseline.
Data Health Report delivered with an initial quality index.
- Phase 02
Data Mapping & Architecture (TO-BE)
Design of the ideal data model, definition of cleanup rules, enrichment pipelines and predictive models.
Data Architecture and Property Dictionary approved.
- Phase 03
Configuration via Technology Sprints
Agile sprints for cleanup and normalization (deduplication), enrichment via external APIs, and lead/churn scoring algorithms.
Scoring models and enrichment flows active in test/CRM environments.
- Phase 04
Go-Live & Ongoing Support
Live launch, user training and real-time monitoring. Transition to continuous-improvement support with a guaranteed SLA.
Database with a data health index above 95% and an active maintenance SLA.
Visualization
Lead Scoring and Data Health Dashboard
Monitor enrichment progress, duplicate resolution and each account's predictive buying intent.
CRM Data Health
Data Health & Lead Scoring Dashboard
Data health index96%
Enriched contacts88%
Duplicates resolved73%
Buyer Intent Score (predictive)
- Andes Construction Corp.High intent92
- Northern Logistics GroupMedium intent74
- Cordillera Retail Inc.Nurturing41
Scoring recalculated with every interaction logged in the CRM.
Deliverables
Key deliverables and technology
The entire project stays documented, operational and measurable inside your own CRM.
Automatic Enrichment Pipeline
Automatic completion of each account's industry, size and technologies from a corporate email.
Predictive Models
Lead Scoring to prioritize prospects and Churn Prediction to anticipate attrition risk.
Data Quality Dashboards
Reports on completeness, resolved duplicates and the health index's evolution over time.
HubSpot CRMAWS Data ServicesInsider OneREST APIsOpenAIApollo
Investment model: One-off Setup Fee (project implementation) + Monthly Fee for ongoing maintenance and support (SLA).