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Architecture & Engineering

Data Architecture & Engineering

Modern data stack design, pipeline development, and platform optimization. Built for today's volume and tomorrow's AI ambitions.

Typical Timeline

8–12 weeks

Team Size

2–4 specialists + AI

Cost Reduction

30–50% cloud costs

Pipeline Reliability

99.9% uptime

SnowflakeDatabricksBigQuerydbtAirflowSparkKafkaTerraformAWSAzureGCP
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The Challenge

The challenge

Your data stack was designed for a different scale. Pipelines are fragile — one schema change breaks three downstream systems. Costs keep climbing because nobody optimized the warehouse after the initial setup. And your data engineers spend 70% of their time firefighting instead of building.

Worse, your current architecture can't support the AI and ML workloads your leadership wants to run. You need a platform that's reliable, cost-efficient, and AI-ready — not just adequate for today.

Our Approach

How we solve this differently

Architecture Assessment

AI agents analyze your current stack — pipelines, warehouse performance, costs, reliability — and identify specific bottlenecks and optimization opportunities.

Modern Data Stack Design

We design architectures on proven platforms (Snowflake, Databricks, BigQuery) with medallion/lakehouse patterns that scale.

Pipeline Engineering

AI agents generate pipeline code using dbt, Airflow, or your preferred orchestration tool. Human-reviewed, tested, and production-hardened.

Cost Optimization

AI agents continuously analyze compute and storage costs, recommend right-sizing, and identify waste — often saving 30–50% on cloud data platform costs.

AI-Powered

What our AI agents handle

Analyze current pipeline health, reliability, and performance across all data flows

Generate pipeline code in dbt, Python, Spark, and your preferred tools

Optimize warehouse configuration, clustering, and partitioning for cost and performance

Detect schema drift and data quality anomalies automatically

Auto-generate data lineage documentation and pipeline dependency maps

Timeline

Typical project timeline

Traditional Approach

Assessment

4–6 weeks

Architecture Design

4–6 weeks

Pipeline Build

12–16 weeks

Testing

4–6 weeks

Optimization

4–6 weeks

Agilityx + AI Agents

Assessment

1 week

Design + Plan

1–2 weeks

Build + Test

4–6 weeks

Optimize + Handoff

2 weeks

Outcomes

What you can expect

30–50%

Cloud cost reduction

Through AI-optimized configurations and right-sizing

99.9%

Pipeline reliability

Automated monitoring and self-healing pipelines

AI-Ready

Architecture from day one

Supports ML workloads without re-platforming

Ready to modernize your data architecture? Let's talk.

Book a 30-minute discovery call and we'll show you exactly how the Build With model applies to your situation.