Service · Data Engineering

Data engineering for AI and real-time analytics

The foundation that holds everything up.

Data Engineering

ETLReal-timeAnalytics

What we do

Data Engineering for businesses in Guadalajara and Mexico

Any serious AI or analytics project fails without a well-built data foundation. Before training models or building dashboards, the data needs to live in a reliable place, with consistent schemas, quality tests and clear lineage. That's what we build.

We work with companies in Mexico whose data is spread across ERPs, spreadsheets, legacy systems, vendor APIs and operational databases. We ingest it with versioned pipelines (Airflow, dbt, Dagster), transform it with tested logic, store it in modern warehouses (Snowflake, BigQuery, Redshift) and serve it to BI, ML and real-time applications.

We include vector databases (Pinecone, Weaviate, pgvector) for cases where generative AI needs proprietary memory via RAG. The same source of truth that feeds the dashboards is what the agent queries.

How we do it

4 proven steps, from discovery to production

Step 01
We ingest from everywhere
APIs, operational databases, files, events. All turned into a reliable flow.
Step 02
We transform with logic
Versioned pipelines (dbt, Airflow) with tests and observability.
Step 03
We store to scale
Data warehouse + vector DB so AI and BI share the same source of truth.
Step 04
We serve in real time
APIs, dashboards and ML-ready features in milliseconds.

Typical use cases

Where data engineering applies

Data warehouse from scratch: ingestion from ERP + CRM + marketing → executive dashboards
Legacy-to-cloud migration: from on-prem SQL databases to Snowflake/BigQuery with validation
Real-time streaming: product events → Kafka → dashboards and alerts
Feature store for ML: precomputed, versioned features ready for models
Vector DB for RAG: proprietary documents indexed for AI agents
Data quality monitoring: automatic alerts when pipelines break or metrics drift

Tech stack

With the best of the ecosystem

Warehouse

Snowflake
BigQuery
Redshift
Databricks

Orchestration

Airflow
dbt
Dagster
Prefect

Streaming

Kafka
Kinesis
Pub/Sub
Redpanda

Vector / Cache

Pinecone
pgvector
Redis
Weaviate

When we don't recommend it

Honest about where it doesn't apply

We don't build data warehouses because they're trendy. If your company uses 2 data sources and a consolidated Excel file works, a full pipeline is overkill. We suggest starting light — dbt + Metabase, for example — and scaling when volume or integrations justify it.

Frequently asked questions about Data Engineering

What we get asked most

Do I need data engineering before doing ML or AI agents?

Sometimes yes, sometimes no. We can start an agent with data that already exists and organize the data layer in parallel. For robust ML, usually yes — without clean data there's no reliable model.

Which warehouse do you recommend?

It depends. Snowflake for enterprises willing to pay for it, BigQuery if you're already on Google Cloud, Databricks if you combine analytics + ML, Postgres to start light. We choose per case, not by preference.

How much does it cost to run a pipeline each month?

It depends on volume. An SMB with <1TB and 20 master tables: ~$300-800 USD/month in infrastructure. Enterprise with streaming and ML: $3k-15k+ USD/month. We estimate it during discovery.

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