We’ve built pipelines that feed multi-terabyte training loops, support regulatory reporting, and run mission-critical pricing engines in production.
From Spark clusters and Airflow DAGs to GitOps and Terraform, we handle design and deployment — integrating directly with your cloud and CI/CD stack.
Unlike vendors who stop at reporting, we engineer pipelines that support ML, LLMs, and AI-driven decision systems — with the structure and tags to back it.
Every pipeline we ship comes with tests, alerts, documentation, and monitoring hooks — so data engineers, scientists, and auditors can trust what’s flowing.
Yes. We integrate with modern data platforms (Snowflake, Databricks, GCP, AWS, Azure) and tools like dbt, Airflow, and Kafka — or help you evolve from scratch.
We build hybrid pipelines optimized for each workload — with flexibility to scale as your use cases evolve.
We embed data tests, anomaly detection, drift checks, and lineage metadata into every pipeline — with alerts and dashboards built in.
Absolutely. We’ve designed pipelines to serve fine-tuning data, feed vector DBs, and support structured prompt generation with governance hooks.
We typically ship production-ready pipelines in 4–8 weeks — faster for focused use cases or quick-start pilots.
We begin with a $0 working session to review your current data setup and high-priority needs — and recommend a plan of action.
