Data engineering & analytics servicesPipelines and reporting
people stop arguing with.
Ingest, warehouse, models and dashboards that the in-house team can run after we leave. Measured before and after every increment.
Batch → incremental
Nightly runs that finish.
One source of truth
Warehouse and semantic layer.
Dashboards used
By the people who asked.
Engagement
Data & AI · project or retained
What you get.
Ingest and warehouse work the in-house team can run after we leave
One source of truth: a warehouse and a semantic layer, documented where your team looks
Dashboards the people who asked for them actually open
A before-and-after measurement on every increment
Engagement: Data & AI · project or retained · Ask about your pipelines →
Good for
— Reporting nobody trusts
— A nightly batch that is now a daily problem
— Numbers that differ by who ran the query
We will say no when
— You want a dashboard before anyone agrees what the numbers mean
— The data does not exist yet and nobody owns collecting it
Stack · only what we have shipped to production
From the record.
All case notes →01 · Life sciences · data
Data & AI
Preclinical research portal for global labs
Problem — Preclinical data was growing exponentially across labs and formats; nobody could compare it, let alone predict from it.
What we did — A compliant cloud architecture with custom ETL to harmonise heterogeneous lab data. Machine-learning models for predicting therapeutic response and model relevance. Dashboards and real-time visualisation for researchers.
Measured — Data analysis time down 60%. 100% of data visible across global teams. Predictive accuracy up.
Client withheld · details on the callRead the case note →
02 · Data · ETL
Data & AI
Marketing data consolidated into ML-ready datasets
Problem — Marketing data scattered across sources, none of it ready for a model.
What we did — An R&D engagement on AWS Glue and Athena consolidating marketing data into ML-ready S3 datasets.
Measured — ML-ready datasets delivered — [ADD SOURCES / VOLUME].
Client withheld · details on the callAsk about this →
03 · E-commerce · analytics
Data & AI
Funnel analysis and CRO for a large store
Problem — A store with over Rs 95M in sales and thousands of orders did not know where the funnel leaked.
What we did — Deep funnel analysis across 1.15M sessions to find drop-offs before checkout. Conversion-rate work at the points that mattered. A data foundation for budget allocation.
Measured — 2.05% store conversion held at high traffic. Average order value Rs 6,074. Over Rs 95M total sales analysed.
Client withheld · details on the callAsk about this →
04 · Buildings · IoT
Product
Real-time operational intelligence for 200+ buildings
Problem — Building operators needed live operational data, not last month’s report.
What we did — Product leadership of a telemetry platform delivering real-time operational intelligence across a large building estate.
Measured — Powering 200+ buildings.
Client withheld · details on the callAsk about this →
Data engineering & analytics · the call
Bring the report nobody trusts. We will tell you why.
Bring the problem, not a brief. If it is outside what we do well, we will say which of our contacts to call instead.
Or email info@hopsig.com — reply within one working day.