01
New projects
A paragraph about the problem is enough to start. We reply within two working days, usually with questions rather than a quote.
Web2 · Service Data engineering and analytics
Data pipelines, a warehouse and dashboards people trust: tested transformations, agreed metric definitions, data quality alerts and clean inputs for AI.
In short
Data engineering moves data from the systems where it is created to a place where it can be trusted and used. We build pipelines, a warehouse and the reporting on top, with tests on every transformation and one agreed definition for each metric — which is also the groundwork any AI or machine-learning project needs.
- How to engage
- Product build, Dedicated engineering pod, Modernization audit
- Practice
- Web2
Signs this is the right service
- Two reports give two different numbers for the same thing, and both teams are sure theirs is right.
- Reporting depends on one person, one spreadsheet and a manual export every Monday.
- An AI or forecasting project has stalled because the data it needs is scattered or unreliable.
- Dashboards are slow, and nobody knows which of them are still used.
What you get
Pipelines
Scheduled and streaming ingestion from your databases, SaaS tools and files, with retries, backfills and a record of every run.
Warehouse and modelling
A warehouse with documented, version-controlled models, so each table says what it means and where it came from.
Data quality tests
Checks for freshness, completeness and broken assumptions that alert the team before a wrong number reaches a meeting.
Metrics and dashboards
One written definition per metric, and dashboards built on those definitions instead of each analyst's own query.
Machine learning foundations
Feature tables and training data with known lineage, and forecasting or classification models where the data supports them.
The process
- 01
Audit sources and questions
What data exists, how reliable it is, and which decisions it has to support.
- 02
Agree the definitions
The handful of metrics that matter, defined in writing with the people who own them.
- 03
Build tested pipelines
Ingestion and transformations in version control, with tests that run on every change.
- 04
Hand over with monitoring
Alerts, documentation and cost controls, so your team can run and extend it.
PythonSQLdbtAirflowPostgreSQLBigQuerySnowflakeKafka
Data engineering & analytics: common questions
Do we need a data warehouse?
- If reports draw on more than one system, or analytical queries slow your production database, usually yes. For a small product, a read replica and a few well-tested views can be enough, and we will say so.
Which warehouse do you recommend?
- It depends on your cloud, your volume and your team. BigQuery, Snowflake and Postgres-based setups all work; the modelling and testing matter more than the brand.
How does this relate to AI projects?
- Most AI projects are limited by data rather than models. Clean, documented, permission-aware data is what retrieval systems, agents and forecasting models all depend on.
Can you build machine-learning models as well?
- Yes, where the data supports it — forecasting, classification, anomaly detection. We first check whether a simpler rule or report would answer the question.
06 / Let’s build
Bring us the difficult problem.
02
Careers
Engineers, designers and researchers who like systems that are hard to get right. Send something you built and what you would change about it now.
03
Studio
Based in India and working remotely, with overlap hours agreed up front so your team always has a time to reach us.
04
Read
Guides on AI agents, RAG, MCP, tokenization and chain abstraction, written by the engineers who build them.