Introduction

Ladrillera Mecanizada is internationally recognized for combining clay-based construction tradition with advanced manufacturing technology and sustainable processes. With dozens of branches and a nationwide distributor network, the company relies on precise, continuous operations and a high-performing commercial engine.\

To sustain near 24/7 production and expand commercial reach, leadership needed deeper visibility than disparate systems and spreadsheets could provide. They turned to Push.ai to build their first modern data warehouse and layer AI-driven analytics on top—giving them a single, always-on view of operations and growth.

Company Background

Ladrillera Mecanizada is Mexico’s leading producer and exporter of 100% natural clay products. Their portfolio includes structural bricks, tiles, flooring, and decorative elements used in projects across the country.

With 28+ branches, thousands of distributors, and a dedication to sustainability, the company blends traditional clay production with cutting-edge manufacturing systems. Their scale and operational model require constant uptime, precise quality control, and a strong commercial strategy backed by trustworthy data.

Challenges

Operations and commercial leader Jacob Villarreal identified three core challenges blocking performance and growth:

• Fragmented data across systems
  
  • Factory systems tracked production, downtime, and stop reasons
  • CRM tracked opportunities, accounts, orders, and activities
  • None of it lived in a single warehouse—making the full picture impossible to see
• Limited visibility into downtime
  
  • Stops were logged at each facility
  • Analysis was ad hoc and spreadsheet-based
  • There was no cross-factory, always-on view of lost machine time
• Sales and customer health disconnected from operations
  
  • Sales leaders wanted to understand customer health: order cadence, volume, risk
  • But this data lived separately from manufacturing insights
  • Commercial and operations teams operated in two different worlds

The company needed a unified intelligence layer to run plants better and grow the business faster.

Solution

Ladrillera Mecanizada selected Push to build both the foundation (data warehouse) and the intelligence layer (Business Graph + AI).

• First Modern Data Warehouse on Snowflake
  Push worked with the team to:
  
  • Stand up Snowflake
  • Ingest manufacturing and sales CRM data via DLT-powered pipelines
  • Normalize and clean all datasets
  • Structure everything for cost-effective, scalable analytics
• The Business Graph™:
  A Connected Model of the Entire Operation
  Push modeled:
  This became the foundation for AI to understand relationships, trends, and business context—not just tables.
  
  • Factories, lines, shifts, machines
  • Downtime events, stop reasons, operator notes
  • Customers, opportunities, orders, activity patterns
• AI on Top of Manufacturing + Sales
  Push delivered:
  
  • Downtime analytics
  • Automated shift reports
  • Root-cause detection via note clustering
  • Customer health and sales analytics
  • Unified operations + commercial insights

Key Results

• Factory Downtime Analytics & Automated Shift Reports
  
  • End-of-shift AI-generated summaries
  • Stop reasons grouped and analyzed
  • Recurring issues surfaced automatically
  • Teams no longer wait for weekly reviews to act
• Plants running closer to 24/7
  
  • Rising downtime flagged early
  • Maintenance prioritized with real data
  • Supervisors and managers aligned on the same facts
• Sales & Customer Health Insights
  
  • Prospect health (activity, quote velocity, pipeline movement)
  • Customer health (order cadence, product mix, payment patterns, risk)
  • Unified operational + commercial insights enable smarter strategy

Implementation

• A. Build the Warehouse & Pipelines
  
  • Snowflake deployed as central store
  • DLT pipelines ingest, transform, and refresh manufacturing + CRM data
  • Compute costs optimized through efficient orchestration
• B. Translate Data Into a Business Graph
  Push mapped:
  This graph allowed AI to “understand” the manufacturing and commercial ecosystem.
  
  • Factories, lines, shifts, machines
  • Stops, downtime reasons, operator notes
  • Customers, opportunities, regions
  • Orders, volumes, engagement signals
• C. Activate AI Agents
  AI drives:
  
  • Downtime pattern detection
  • Note clustering for root-cause identification
  • Automated shift reports
  • Customer health scoring
  • Sales funnel friction detection
  • Alerts and actions across operations and commercial teams

Business Outcomes

1. Faster response to plant issues
Teams know exactly where downtime is happening—and why.

2. Shared source of truth
From operators to executives, everyone sees the same real-time data and explanations.

3. Better commercial strategy
Sales teams prioritize the right prospects and protect at-risk customers using AI-driven health scoring.

4. Unified operations + growth analytics
Factory output and commercial performance finally live in one system.

5. Future-ready data foundation
With Snowflake, DLT, and Push’s Business Graph, the company is positioned for long-term AI expansion.

We now operate and grow as one coordinated system—not two disconnected worlds.