DATA ENGINEERING + BI
From Data Estate to Decision Engine.

Modernise legacy warehouses, migrate at scale, run analytics that actually move metrics, and put a live digital cockpit in front of every executive who needs one.

The Problem We Solve

Most enterprises have invested in data – yet executives still wait days for a single number. The problem is rarely the data itself; it is the platform, the pipelines, and the people-process layer wrapped around it. We modernise all three.

Whether you are mid-migration, mid-analytics build, or mid-board-meeting with no dashboard, we plug in fast and engineer the outcome.

Four Ways We Engage
Modernise. Build. Transform. In Any Order You Need Them.

Three practices. One delivery model. Picked and sequenced around the outcome you need first.

Data Modernisation

Replatform legacy warehouses (Teradata, Netezza, Oracle Exadata) and on-prem Hadoop into a modern cloud lakehouse – Databricks, Snowflake, Microsoft Fabric, or BigQuery. We design for cost as much as for performance.

Data Analytics

From descriptive dashboards to predictive ML, we build the analytics layer that connects raw events to revenue. Every model ships with an evaluation harness so quality stays measurable in production.

Data Migration (Lift & Shift)

When the business needs to move, not redesign, we run zero-downtime lift-and-shift migrations – SQL Server to Azure SQL, Oracle to RDS, on-prem Hadoop to S3 / ADLS / GCS. We carry the migration risk so you do not have to.

Power BI / Data Control Tower / Digital Cockpit

The C-suite should not be the last to know. Our Digital Cockpit practice builds Power BI-led executive control towers that surface the metrics that matter – cash, cycle time, customer health, capacity – in one live view, on every device.

How We Deliver - Modern Data Stack

We are platform-agnostic but opinionated. Our reference architecture leans on best-in-class tooling – swapped per client constraint.

Lakehouse / warehouse
We prefer
  • Databricks
  • Snowflake
We also support
  • BigQuery
  • Redshift
Ingestion / ELT
We prefer
  • Fivetran
  • ADF
We also support
  • Airbyte
  • Talend
Orchestration
We prefer
  • Airflow
  • Dagster
We also support
  • Prefect
  • ADF pipelines
Governance
We prefer
  • Unity Catalog
  • PurviewCollibra
We also support
  • Atlan
  • Alation
BI / Cockpit
We prefer
  • Power BI
We also support
  • Tableau
  • Looker
  • Sigma
ML Ops
We prefer
  • Databricks MLflow
  • Azure ML
We also support
  • Vertex AI
  • SageMaker
How We Engage

Whether you are assessing your current state or ccelerating an existing roadmap, we bring structure to every stage.

Step 1
Tech Stack and Integration Audit
We evaluate your existing architecture, surface integration gaps, and identify where AI-native tooling will produce the highest near-term leverage.
Step 2
Change Management Planning
We build adoption frameworks alongside the technical work, so your teams operate the transformation, not just survive it.
Step 3
KPI-Anchored Roadmap
Every initiative is mapped to financial and operational KPIs from day one. Quarterly milestones. No vanity metrics.
Step 4
Co-Delivery Model
SMI builds; your team operates; knowledge transfers as we go. Governance covering AI risk, data residency, and compliance is built in from the start.
Engagement Models

Deploying AI assistants without discipline creates noise, cost overruns, and inconsistent output. We have built a structured operating model around five engineering principles that determine how our teams use Claude, Copilot, and Cursor in practice.

AI ENGINEERING OPERATING MODEL
Industries We Serve
Five sectors where AI-native delivery produces the largest P&L impact.
Manufacturing
OEE, predictive maintenance, energy & emissions reporting
Supply Chain
control-tower visibility, demand sensing, supplier risk
Healthcare
population-health dashboards, claims analytics, HEDIS reporting
Retail
assortment, markdown, store-ops, and customer-360
BFSI
risk & exposure analytics, AML alerts, regulatory reporting
ENGAGEMENTMODELS
Four Ways to Engage. Match Your Risk Appetite.

Pick the model that fits your budget, risk profile, and roadmap maturity.
Same delivery team across all four.

Outcome-Driven

You define the KPI; we carry the
execution risk. Ideal for funded
transformation programmed.

Time & Material
Transparent, capacity-led
engagement for exploratory or
evolving work.
External IT Team

Build-Operate-Manage, Build operate Transfer, or productivity On- Demand pods. Specialist teams stood up in 4 weeks.

Staffing
Individual AI-native engineers, vetted and embedded in your sprints.

FAQ

Frequently Asked Questions

Can you work with our existing warehouse, or do we have to replatform?

We meet the data where it lives. Many engagements start with quick wins on the existing warehouse before any replatform decision is on the table.

Every engagement includes a data-handling appendix covering residency, encryption, access control, and audit. We deliver SOC 2-aligned controls and support GDPR, HIPAA, and RBI / SEBI data-residency requirements.

Both. Our delivery teams are full-stack data – platform engineers, ML engineers, analytics engineers, and BI designers – because the cockpit is only useful if the pipes feeding it are right.

30 days, MVP. A Power BI control tower with 5 KPIs and one data domain. We do this as a fixed-scope, fixed-fee engagement.