Data warehouse and lakehouse
One modelled source of truth, with access controls a compliance reviewer can read.
Snowflake · Databricks · Redshift · BigQuery · Synapse
DATA & ML
From dashboards to deployed models, with the lineage to prove it.
Warehouses, ELT pipelines and streaming on AWS, Azure or Google Cloud; models trained, deployed and monitored with a registry behind them; dashboards that agree with the source. Every table tested, every model versioned.
How it works
How a number reaches a dashboard, and a model reaches production: six stages, two of which can stop it.
Every stage leaves an artefact: a source register, a test log, a lineage graph, a model card, a drift report. That is what makes a number defensible in a board pack, and what an auditor sees when they ask where it came from.
- Ingest (Sources). Batch from databases and SaaS, streams from events. Every source is registered with an owner and a freshness expectation. Tools: Airflow, Fivetran or custom connectors, Kinesis, Kafka or Pub/Sub.
- Land (Lake and warehouse). Raw data kept immutable, partitioned and encrypted in the warehouse or lake, in an India region when residency requires it. Tools: Snowflake, Databricks, Redshift or BigQuery, S3, Blob Storage or Cloud Storage.
- Transform (Modelling). Transformations as versioned SQL with tests on every table: schema, uniqueness, referential integrity, freshness, the business rules you give us. Tools: dbt, Great Expectations. Gate: A failing data test stops the run and pages an owner.
- Serve (Modelled layer). One governed layer feeds dashboards, APIs and feature pipelines alike, so two teams never disagree on a number. Tools: Semantic layer, Metabase, Looker or Power BI, Feature store.
- Train and deploy (ML). Features from the same layer; experiments tracked; the winning model registered and deployed behind an API or a batch job. Tools: SageMaker, Vertex AI or Azure ML, MLflow. Gate: Promotion needs an eval and an approval.
- Monitor (Drift and quality). Data quality, model drift, dashboard freshness and cost, alerted to an owner before a customer sees the wrong number. Tools: Evidently or Model Monitor, Freshness SLAs, Cost per pipeline.
What we build
Six things we build, and what each one runs on.
ELT and pipelines
Transformations as versioned code, with tests that fail the run rather than the meeting.
dbt · Airflow · Fivetran or custom connectors · Great Expectations
Real-time streaming
Events processed as they happen, with replay for when they do not.
Kinesis · Kafka · Pub/Sub · Flink or Spark Structured Streaming
ML training and deployment
Models with a registry, a rollback and an owner.
SageMaker · Vertex AI · Azure ML · MLflow · feature pipelines
Model and data monitoring
Drift and data-quality alerts before the numbers go wrong in front of a customer.
Evidently or SageMaker Model Monitor · freshness SLAs · an owner per alert
BI and dashboards
Dashboards built on the modelled layer, so two teams never disagree on a number.
Metabase · Looker · Power BI · semantic layer
How we work with you
Start with the foundation. Models come after the data can carry them.
- 016–10 weeks
Data platform foundation
A warehouse or lakehouse, the first tested pipelines, and the modelled layer your dashboards will read.
You leave with
One governed source of truth and the tests that keep it that way.
Talk to a data engineer - 028–16 weeks
ML to production
A model trained on the modelled layer, registered, deployed behind an API or a batch job, and monitored.
You leave with
A model in production with a registry, monitoring and a rollback.
- 03Ongoing
Data and ML operations
Pipelines and models kept healthy, references and schemas kept current, and a review of cost and quality every month.
You leave with
An engineer on the platform, and a monthly record of freshness, drift and spend.
Five phases, and the artefact a data platform needs at each one.
The full process- 01
Discovery & Strategy
1-2 weeks
Data inventory, source-to-target map and the KPI definitions in writing
Signed off by you, before engineering starts
- 02
Architecture & Design
2-3 weeks
Warehouse model and pipeline architecture, reviewed with your security team
Signed off by your security team
- 03
Agile Development
4-12 weeks
Pipelines and models as code, with tests and the review trail
Signed off by a senior engineer, every PR
- 04
Quality Assurance
2-4 weeks
Data quality report and model evaluation against the agreed thresholds
Signed off by your QA and compliance teams
- 05
Launch & Evolution
Ongoing
Freshness, drift and cost dashboards, with a weekly digest
Signed off by your team, every week
A build engagement runs 9-21 weeks to launch, then ongoing. Every phase ends with a named artefact and a named sign-off, under ISO/IEC 27001 and ISO 9001 controls.
Recent work on this line
Two engagements on this line now.
Healthcare / Genomics
Active engagementData engineering for a clinical genomics platform in India
- Clinical genomics
- Data engineering
- Genomics platform
Life Sciences
In active developmentProvenance-tracked genomic analysis pipelines for InferaGen.ai
- AWS Batch
- Provenance trail
- From the Lab
How we work
Built like a product company, shipped like one.
- 01
Audit-engineered by default
Every engagement runs under ISO/IEC 27001, ISO 9001, ISO/IEC 20000, and ISO/IEC 42001 controls. DPDP-aligned, with HIPAA / GDPR / RBI overlays available per project.
- 02
Founder-led delivery
Your discovery call is with a founder. The architecture review is with a senior engineer who stays on the project. No body-shop, no offshore handoff, no account-manager translation layer.
- 03
Bias to ship, not slide
We don't write demos that can't survive a Friday production deploy. Every milestone produces an artifact your team can use immediately: code, diagrams, telemetry.
Questions buyers ask
Straight answers, before the first call.
Snowflake, Databricks, Redshift or BigQuery?
Whichever fits the cloud you already run and the workloads you have. Redshift on AWS, Synapse or Databricks on Azure, BigQuery on Google Cloud, Snowflake or Databricks when you need one warehouse across clouds. The modelling, testing and access-control disciplines are the same on all of them; the recommendation is written into the discovery brief with the reasons.
Do you replace our existing dashboards?
Not unless you want us to. We put a modelled, tested layer underneath them so that what they show agrees with the source, then rebuild only the ones whose numbers were wrong.
Can you work with data that cannot leave India?
Yes. The warehouse, the pipelines and the models run in your own cloud account in an India region, with region pinning enforced by policy and personal data handled under the DPDP Act.
How do you stop a bad number reaching a dashboard?
Tests on every table, run as part of the pipeline: schema, uniqueness, referential integrity, freshness and the business rules you give us. A failing test stops the run and pages an owner. The dashboard keeps showing the last good data rather than the new bad data.
Do we need machine learning, or just better data?
Often the second. The foundation engagement answers that honestly: once the data is modelled and tested, the cases where a model would earn its keep are usually obvious, and so are the cases where a well-built report does the job.
Insights
Reading on data & ML.
Engineering notes on data & ML, written by the engineers on the work.
Have a data or ML project in mind?
Book a 30-minute discovery call. We'll look at the data you have and tell you what it can support.
+91 912-195-7728Hyderabad, IndiaEvery brief gets a senior review. Reply within 1 business hour, 9 AM-7 PM IST.