Data people actually trust.
Pipelines, warehouse platforms, dbt modelling and quality testing that runs on every execution - built from one question the business needs answered rather than from ingesting everything and hoping.
What we actually deliver.
Ingestion & pipelines
Batch and streaming ingestion with replay, idempotency and reconciliation - so a failed run is a retry rather than an investigation.
- Batch, CDC and streaming ingestion
- Idempotent, replayable by design
- Reconciliation against source truth
Warehouse & lakehouse
A cloud warehouse by default because it is simpler and cheaper to operate; a lakehouse when volume or ML access genuinely justifies it.
- Snowflake, BigQuery, Databricks, Postgres
- Storage and partitioning design
- Cost modelling before commitment
Transformation & modelling
dbt models that are version controlled, tested and documented, with a semantic layer so metrics mean one thing across the business.
- dbt project structure and conventions
- Semantic layer and metric definitions
- Incremental models that stay correct
Data quality
Expectations asserted in code and failing loudly - with an agreed decision per dataset about which failures block and which only warn.
- Uniqueness, referential and freshness tests
- Distribution and anomaly checks
- Explicit block-vs-warn policy
Orchestration
Dependency-aware scheduling with lineage, so a failure upstream is visible to everyone downstream before they act on stale numbers.
- Airflow, Dagster or managed equivalents
- End-to-end lineage
- Freshness SLAs with alerting
Governance & access
Row and column level access, PII handling and retention rules built into the platform rather than enforced by convention.
- Row / column level security
- PII classification and masking
- Retention and deletion workflows
A sequence that de-risks delivery.
Pick one question
One high-value question the business needs answered. Building its pipeline surfaces the real problems at a scale you can actually fix.
Model the domain
Agree what a customer, an order and an active user actually mean before writing transformations. Most data distrust starts as definition drift.
Build with tests from day one
Quality expectations written alongside the models, not retrofitted. Retrofitted tests document current behaviour rather than correct behaviour.
Widen coverage
Once one answer is trusted, extend to adjacent domains. Trust compounds; coverage without trust produces a warehouse nobody queries.
What clients measure afterwards.
Numbers agreed
One definition per metric, so meetings stop debating whose figure is right.
-62% pipeline runtime
Incremental modelling and partitioning rework on a typical estate.
Freshness monitored
SLAs with alerting, so stale data is caught before someone acts on it.
AI foundation ready
Clean, current, permission-aware data - the prerequisite most AI work skips.
Work we have actually delivered.
Built a waste-management dashboard that turns field audits into decisions.
- 4-tier
- Role-based access control
- 10
- Dashboard views · 19 APIs
Built an online CA platform that ranks for 300+ search terms.
- 300+
- Indexable pages
- 162
- SEO articles shipped
Tools we work fluently in.
- Snowflake
- BigQuery
- Databricks
- Redshift
- Postgres
- dbt
- SQLMesh
- Spark
- Polars
- Python
- Airflow
- Dagster
- Prefect
- Temporal
- Great Expectations
- Fivetran
- Airbyte
- Debezium
- Kafka
Senior engineers, accountable outcomes.
We start with a question, not a platform
Teams that ingest everything first spend a year building a warehouse nobody trusts. Trust comes from a correct answer, not coverage.
Quality is tests, not an initiative
Expectations run on every execution and fail loudly. A quarterly data quality review finds problems weeks after decisions were made on them.
We resist over-building
More teams over-build here than under-build. A cloud warehouse handles far more than most mid-market companies will ever put through it.
The foundation AI needs
Retrieval quality depends on clean, current, permission-aware data. Tuning prompts against bad data wastes months and looks like a model problem.
Questions, answered.
The questions data leaders, CTOs and analytics teams ask us most often before a data platform engagement.
- For most mid-market companies, a cloud warehouse is the right default - it is simpler to operate, the tooling is mature, and the cost is predictable. A lakehouse earns its complexity when you have genuinely large volumes, semi-structured or streaming data, or machine learning workloads that need direct file access. We have seen more teams over-build here than under-build, so we start with the simpler option unless there is a specific reason not to.
Book a data call - leave with a first question.
A 45-minute session with a senior data engineer. We identify the one question worth building for first and what stands between you and trusting the answer - and you leave with that, whether you engage us or not.
Tell us about your project.
Briefs, NDAs, architecture reviews, anything goes. A senior engineer responds within 24 hours.
- Email[email protected]
- Phone+91 99228 74956
- LocationsHyderabad · Pune · Bangalore
- 1 · A senior engineer reviews your brief within 24 hours.
- 2 · We schedule a 30-minute discovery call.
- 3 · You receive a written proposal in 48-72 hours.