Data
Pipelines, warehouses, and the reporting that makes them useful.
Data engineering that treats the quality layer as a deliverable in its own right. Without it, every dashboard downstream is a story someone tells themselves.
What we do
What we do.
- 01
Pipeline engineering
Batch and streaming pipelines against operational sources, with the orchestration and backfill tooling to run them reliably in production.
- 02
Warehouse and lakehouse design
Modelling, partitioning and storage choices made for your actual query patterns, not a pattern copied from a blog post.
- 03
Analytics modelling
Semantic models and reporting layers that your analysts and business users can trust, without three dashboards disagreeing on the same number.
- 04
Data quality and observability
Automated checks, lineage, and the alerting that catches a broken upstream before the Monday business review does.
- 05
Integration with industrial and scientific data
Laboratory instruments, manufacturing process data, telemetry at scale. Schemas that are irregular and vocabularies that are specialised.
- 06
ML and analytics enablement
Feature pipelines, training data access, and the governance layer that makes it safe for data scientists to use without needing engineering help.
The stack
What we actually use.
A working list.
- Python
- SQL
- dbt
- Airflow
- Dagster
- Snowflake
- BigQuery
- Redshift
- Databricks
- Kafka
- Spark
- Postgres
- ClickHouse
- Looker
- Metabase
Where this shows up
Work this capability has shipped.
Creator Economy
OyeCreators
A marketplace that matches brands with creators
A brand app and a creator app sharing a matching engine, campaign tools and reconciled payments.
- Application Development
- Data
- Platform & Cloud
Devotional Commerce
Oorja Devotion
Products, services and ritual bookings on one commerce platform
A commerce platform treating scheduled services and physical goods as the same kind of product. Multilingual, Indian payment methods, inventory and bookings in one system.
- Application Development
- Data
Food & Dining
Palate
A restaurant discovery app that gets return visits
Mobile app with a taste model learned from behaviour, location aware search, a social layer, and recommendations that work from the first open.
- Application Development
- Data
- AI & Agentic Systems
Marketplace
nookstor
A private marketplace for home businesses
Private shops for home businesses on web, iOS and Android. Customers join by invite or QR, payments settle direct to the seller via UPI, platform fee only when a sale happens.
- Application Development
- AI & Agentic Systems
- Data
- Platform & Cloud
Field Services
Qualtrec Solutions (UAE)
Field data reaching the office the same day
An offline first field service platform replacing phones, spreadsheets and paper with structured capture, live status, and client reports generated from the capture itself.
- Application Development
- Data
- Platform & Cloud
Fintech
A fintech trading app
An auditable financial core, built in from day one
Engineers embedded for the parts of a trading platform where getting it wrong is an incident, not a bug: ledger, invariants, idempotency, encryption, access control.
- Application Development
- Data
- Cybersecurity
How to engage
Three ways to bring this capability in.
As a hire
Hire data, analytics or ML engineers with real exposure to your domain and tools.
How talent worksAs a pod
A pod that owns a data platform workstream from pipeline to reporting layer.
How pods workAs a built team
A dedicated India data team embedded in your organisation, transferred to your entity when ready.
How BOT works
FAQ
What people ask us.
What data engineering problems do you solve?
Pipeline reliability, warehouse modelling for real query patterns, data quality enforcement, and integration against operational, scientific and industrial sources.
Do you work with cloud data and data that cannot leave our environment?
Both. Several engagements have had sensitive or regulated data that cannot leave the client environment. Our architecture starts with where the data is allowed to go.
How do you handle data privacy and compliance?
Access control, encryption, lineage and audit logging designed in rather than retrofitted. We can work inside existing compliance regimes (SOC 2, HIPAA, GDPR context) with the right engineer selection.
Can you work with our existing data stack?
Yes. Our bias is to extend what works rather than rebuild it. We screen specifically for comfort with your tools before placement.
Have a data problem that's bigger than one engineer?
Tell us about it. We'll tell you honestly whether it's a role, a pod, or a longer platform build.