Most organizations have more data than they know how to use. Our data science and data engineering team turns that raw exhaust into governed pipelines, decision-grade analytics, and the labeled training corpora your AI initiatives are blocked on. One team, one foundation, two payoffs.
The most expensive AI failures happen because someone tried to train a model on data that wasn't ready for it. We do the unglamorous work first — and the modeling that follows actually has a chance.
Privacy protection, a record of where every number comes from, and controls on who can see what are built in from the start, not patched in later.
We connect your older systems and newer tools so everyone in the company works from the same single set of trustworthy numbers.
Dashboards that answer the questions your team actually asks, not the ones a vendor template guessed at. Built together with the people who will use them.
If you're building AI, we prepare the clean, well-organized training material it needs to learn properly, and keep honest test material set aside.
Four practice areas, often delivered together. Most engagements touch at least two; many touch all four.
We gather data from all your systems, clean it up, and keep it flowing automatically. We use the tools you already have where possible, and add the right new ones where not.
Dashboards and reports your team can use to answer their own questions. We build them with the people who'll use them in the room, not in isolation.
Forecasting, trend analysis, and digging into the numbers to answer hard business questions, often without needing to build any AI at all.
Getting your data ready for AI: organizing it, labeling it, removing private details, and setting aside clean material to test against.
Most data engagements start with a focused 90-day phase that gets your foundation defensible, then settle into a steady cadence of expansion and improvement.
Two weeks mapping what data you have, where it lives, and what shape it's in. You get an honest inventory and a short list of what to fix first.
We build the core foundation: one reliable, central place where your most important data lives and stays current.
The first round of useful output: dashboards your team will actually use, and analysis ready to guide real decisions.
We add monitoring, alerts, and access controls, so your data setup can stand up to an audit.
Each quarter we bring more of your data into the fold, ship new reports, and prepare new AI training material as needed. Most clients keep us around through year two.
Data engineering builds the pipelines, governance, and infrastructure that move and prepare data; data science analyzes that data to produce insights and models. Galatine Technologies delivers both from one team, so the foundation feeds both decision-grade analytics and AI/ML training workloads.
Galatine Technologies' data engineering services include governed data pipelines, schema unification across legacy and modern systems, PII redaction and lineage tracking, analytics and BI enablement, and labeled training datasets for AI/ML. The stack includes Snowflake, Databricks, dbt, Airflow, Spark, and Postgres.
Most AI failures are data problems in disguise. Models trained on ungoverned or low-quality data underperform and create compliance risk. Strong data engineering and data science make AI initiatives feasible and durable.
Tell us what systems you have, what decisions you're trying to make, and which projects are stuck waiting on better data. We'll come back with a 90-day plan to fix the foundation under all of it.