Data intelligence and engineering is the work of moving your operational data into a governed platform where every metric has one definition, every field has known lineage, and quality is measured rather than assumed. We build these on Databricks, Snowflake and Zoho Analytics, and we consider it the mandatory first investment before any serious AI deployment.
Why this is now urgent
For twenty years bad data produced bad reports, and humans quietly corrected for it. AI removes that correction layer. An agent doesn’t know that the “revenue” field has meant three different things since 2022. It just answers.
What we build
Ingestion
Pipelines from Zoho, ERP, core banking, RTA feeds, payment systems, spreadsheets. Incremental, idempotent, monitored.
Warehouse or lakehouse
Databricks for ML and streaming. Snowflake for SQL analytics. Zoho Analytics for Zoho-centric estates.
Modelling
Dimensional models designed around business questions. Customer 360 that resolves the same customer across systems.
Governance
Metric layer with one definition per measure. Column-level lineage. Access controls, PII classification, masking.
Quality
Automated tests on freshness, completeness, uniqueness, referential integrity. An owner for every failure.
Serving
Dashboards, self service datasets, and a clean permissioned retrieval layer for agents.
The retrieval layer for AI
This is where data engineering and agent engineering meet. Every production agent needs chunked documents, governed views, permission inheritance, and citation metadata. We build the retrieval layer so agents return deterministic, citable answers instead of plausible guesses.
Chunked documents with semantic boundaries
Governed views with metric definitions
Permission inheritance from source systems
Citation metadata on every retrieval
Where we win
Zoho-to-warehouse work
We know the Zoho data model inside out. When your analytics outgrow Zoho reports, we move you to Snowflake or Databricks without losing continuity.
BFSI data models
NAV reconciliation, AUM computation, commission waterfall, regulatory submission datasets. We have built the models that matter in Indian financial services.
Learn moreData built for agents
Most warehouses are built for dashboards. We build the retrieval layer, permission model and citation metadata that agents need to give deterministic answers.
Learn moreCommon questions
Databricks or Snowflake?
It depends on the workload. Databricks excels at ML workflows and streaming; Snowflake excels at SQL analytics and governed sharing. Many clients run both. We recommend based on your actual use cases, not brand preference.
Do we need a warehouse if we run Zoho One?
Often not initially. Zoho Analytics handles reporting well for Zoho-centric estates. When you outgrow it — cross-system joins, ML features, agent retrieval — we move you to a warehouse without losing continuity.
Can our data stay in India?
Yes. AWS Mumbai, Azure Central India, GCP Mumbai, and Zoho’s India data centres all support full residency. We design for this from day one.
How long before we see value?
First governed domain — one subject area with defined metrics, lineage and quality tests — in weeks, not months. Value compounds as more domains come online.
What about DPDP compliance?
PII classification, column-level masking, retention rules and consent tracking are part of the build, not an afterthought. We design the governance layer to meet DPDP requirements from the start.
Let’s govern your data before your agents use it
Ninety minutes with our data engineers. You’ll leave with a data architecture view and a clear first domain to govern.
Book a working session