Turn operational data into decisions.
Pipelines, warehouses, predictive models, and dashboards built so everyone works from a single trusted source of truth.
The problem
You have the data. Getting value out of it is the hard part.
Operational systems emit more data than any team can read. Most of it is locked inside exports, dashboards nobody looks at, and tables only two engineers understand.
Trelnox builds the infrastructure that moves data from where it's generated to where it's used.
What we build
Every layer of a modern data stack.
Data pipelines & ELT
Reliable ingestion, transformation, and orchestration across databases, SaaS tools, events, and files — with monitoring that catches issues before downstream teams do.
Warehousing & modeling
Well-structured data models that business users can reason about, built in Snowflake, BigQuery, Redshift, or whatever you already run.
Predictive models
Forecasting, scoring, anomaly detection, and propensity models trained on your data and integrated into the systems where decisions get made.
Dashboards & reporting
Self-serve analytics and executive dashboards that surface the metrics that matter — not every metric the source system can emit.
Retrieval & search
Vector search, semantic retrieval, and enterprise search infrastructure that powers both human workflows and AI agents.
Data quality & governance
Contracts, tests, lineage, and access controls so your data can be trusted by the decisions — and the models — that depend on it.
Outcomes
What changes when your data stack works.
From raw data to decisions
We close the gap between operational systems and the teams that need answers. Analysts, operators, and executives all get what they need from the same source of truth.
AI that actually has context
Reliable data infrastructure is the prerequisite for useful AI. We build the pipelines and retrieval systems that let models reason about your real business.
Reduced reporting burden
Replace spreadsheets, ad-hoc SQL, and manual monthly reports with systems that surface the right numbers automatically.
FAQ
Common questions.
- We already have a data warehouse. What would you do differently?
- We meet you where you are. Often the work is in the modeling layer, the pipelines feeding it, or the last-mile tooling that turns data into decisions — not the warehouse itself.
- Can you train and deploy custom ML models?
- Yes — classic ML and modern ML/AI both. We scope each project around what is actually needed, and avoid bringing out heavy machinery when a simpler approach works.
- Do you work with dbt / Airflow / Dagster / other tools?
- Yes. We use whatever orchestration and transformation tooling fits your team. Greenfield work usually lands on dbt + a modern orchestrator; in existing stacks we use what's already there.
- How does this tie to your AI work?
- Every AI system we ship is only as good as the data behind it. Our data and AI teams work together — the same engineers think about retrieval, quality, governance, and modeling from day one.
Ready to get more from your data?
Tell us where data currently gets stuck. We'll map a path to turning it into decisions.