Data engineering services for AI and real-time analytics
The foundation that holds everything up.
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.
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.
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
With the best of the ecosystem
- Snowflake
- BigQuery
- Redshift
- Databricks
- Airflow
- dbt
- Dagster
- Prefect
- Kafka
- Kinesis
- Pub/Sub
- Redpanda
- Pinecone
- pgvector
- Redis
- Weaviate
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.
How do we quote a data engineering project?
We don't publish a single price: cost depends on how many sources need integrating and how fast data has to arrive. This is how we reach a fixed figure:
- 01
Discovery
We review sources, volume, update frequency and who consumes the data (BI, models, applications).
- 02
Phased proposal
Each phase (ingestion, data model, dashboards or serving) has a fixed scope and price before we start.
- 03
Infrastructure billed separately, estimated upfront
You pay cloud and warehouse costs directly to the provider; we give you the monthly estimate during discovery.
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 data volume, the number of sources and whether you need streaming or ML models. We estimate it during discovery, with your real data, before you decide; you pay the infrastructure directly to your cloud provider.
Cases where we applied data engineering
500M+ coupon campaigns
Platform to run massive free mobile data campaigns for Coca-Cola, GEPP and Bimbo. AWS infrastructure with load balancers and RDS Aurora.
Cloud migration of recordings
Migration from Zoom Cloud to multiple destinations (AWS, Azure, GCP, Dropbox).
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Let's talk about Data Engineering in your company.
Tell us your challenge and we'll propose how to apply it in your operation. We reply by email or WhatsApp to book a call.