Services
Data, Analytics & AI
Reporting you can trust, before automation you can rely on.
Data platforms, pipelines and analytics that produce one agreed set of numbers — and, where the case is real, machine learning and generative AI built on top of that foundation rather than instead of it.
The situation
Why clients call us about this
Two departments bring two different revenue figures to the same meeting, and the rest of the hour goes on reconciling them. Meanwhile there is pressure to 'do something with AI'. Both problems share a root: data spread across systems that were never designed to agree with each other.
What changes afterwards
- One definition of each core metric, agreed by the business and enforced by the pipeline
- Reports that refresh on their own instead of being rebuilt by hand each month
- Data quality problems that surface as alerts rather than as a surprise in a board pack
- A clear-eyed view of which AI use cases are worth funding and which are not
- Personal data handled in a way you can explain to a regulator or a customer
Capabilities
What this practice covers
Not every engagement uses all of it. We scope to the problem, not to the list.
Data platform and warehouse
A central warehouse or lakehouse sized to your data volume and budget, with modelling that reflects how the business actually thinks about its numbers.
Data pipelines
Scheduled, monitored ingestion from your operational systems, with retries, alerting on failure, and tests on the data itself rather than only on the code.
Business intelligence and reporting
Dashboards built around the decisions they support, with metric definitions documented so two teams cannot quietly compute the same number differently.
Machine learning engineering
Forecasting, segmentation, scoring and anomaly detection, put into production with monitoring for drift — not left as a notebook nobody can run.
Generative AI enablement
Retrieval-based assistants, document processing and internal knowledge tools, scoped against a specific task with an evaluation set, so quality can be measured rather than argued about.
Data governance and quality
Ownership, lineage, retention rules and access controls, including the classification of personal data that obligations under Indian data protection law depend on.
Deliverables
What you end up holding
Every item here is yours, in your systems, in a form your team can use without us.
- Data source inventory and current-state assessment
- Warehouse schema and documented metric definitions
- Orchestrated pipelines with monitoring and alerting
- Dashboards for the agreed decision set
- Data quality test suite
- Model or assistant with a documented evaluation approach
- Access control and retention policy documentation
Tooling
Technologies we work in
- BigQuery
- Snowflake
- Amazon Redshift
- dbt
- Apache Airflow
- Python
- SQL
- Pandas
- scikit-learn
- PyTorch
- Metabase
- Power BI
- Looker Studio
- PostgreSQL
Listed as capabilities rather than partnerships or endorsements. All product names are the trademarks of their respective owners. We choose tools per engagement and will explain the trade-off behind each choice.
FAQ
Data & AI, answered directly
We want to use AI but are not sure where. Can you help us decide?
Yes, and this is the most useful place to start. A short assessment looks at where your people spend time on repetitive judgement, what data exists to support automating it, and what a wrong answer would cost. Some of those cases justify a model. Several usually turn out to be better solved by fixing a process or a report, and we will say so.
Is our data good enough for analytics or machine learning?
It is rarely as bad as feared or as good as hoped. We assess coverage, consistency and history against the specific question you want answered, then tell you what would have to improve first. That answer is sometimes that you are not ready yet, which is cheaper to hear early.
Where does our data go when you build an AI feature?
Wherever you decide, and it is agreed in writing before anything is built. We will lay out the options — self-hosted models, an Indian region of a cloud provider, or a third-party API — with the cost, quality and data-residency trade-off of each, so the choice is yours and it is informed.
Other practices
Tell us what is not working
A first conversation costs nothing and commits you to nothing. Describe the problem in your own words and we will tell you honestly whether we are the right people for it.