Data Engineering
Streaming and batch pipelines, large-scale dataset processing, mathematical optimization, and predictive models that move business metrics — because every AI initiative is only as good as the data layer under it.
What We Build
Reliable ELT/ETL pipelines, warehouse modeling, and data quality monitoring — so 'where does this number come from?' always has an answer.
Forecasting, churn prediction, scoring, and anomaly detection — deployed as monitored services with retraining schedules, not notebooks that rot.
Routing, scheduling, pricing, and allocation problems solved with optimization techniques that find margins spreadsheets can't see.
Event streaming and live dashboards for the decisions that can't wait for the nightly batch.
Why Algogile
Our data work inherits backend discipline: idempotent jobs, retries, monitoring, and tests. Pipelines fail loudly, recover automatically, and never silently corrupt.
We optimize the business number, not the model leaderboard. A simpler model that ships and is trusted beats a clever one that isn't.
Pipelines are designed with retrieval, training, and evaluation use cases in mind — so your future AI projects start from clean ground.
Questions, Answered
That's the normal starting point. We begin with a data audit, stand up ingestion for the highest-value sources first, and improve quality iteratively while delivering usable analytics early.
Yes — cloud warehouses, Postgres/MySQL estates, spreadsheets, third-party APIs, and legacy systems. We meet your data where it lives.
Analysts answer questions; we build the systems that make questions answerable — pipelines, models, and monitoring that keep working after the engagement ends.
A 30-day POC delivers one working pipeline or one deployed model against a metric you choose.