Capability
One trusted source of truth for your data.
We build the pipelines, platforms and dashboards that turn scattered, siloed data into one version of the truth your whole organisation can rely on.
Overview
Most reporting arguments are not really about the numbers. They are about which number is right, because finance, operations and sales each pull from a different system and get a different answer. Data engineering is how you end that argument: one trusted source of truth, built on reliable pipelines, so people spend their time deciding rather than reconciling.
We work end to end. We advise on the platform and architecture that fit your scale and budget, without gold plating. We build the pipelines, warehouses and lakehouses that bring siloed systems together, and the dashboards that make the result usable for people who do not write SQL. Then we help you run it, with the data quality checks and monitoring that keep a trusted platform trusted.
Heavy industry gave us a healthy respect for messy data. In oil and gas, mining, manufacturing and logistics, data arrives from sensors, legacy systems, spreadsheets and paper, at wildly different speeds and qualities. Making that reliable, joined up and governed is exactly our craft, and the same foundations serve analytics teams in finance, retail, healthcare and the public sector.
From Pune, Bengaluru, Indore and Kolkata, we bring engineers who care about the boring things that make data trustworthy: lineage, testing, cost and clear ownership. The payoff is an analytics and AI foundation you can build on with confidence, rather than a pile of pipelines nobody dares to touch.
What we deliver
Inside our data engineering & analytics work.
Data Platforms & Architecture
Cloud data warehouses and lakehouses designed for your scale and budget, giving you one governed foundation for analytics, reporting and AI to build on.
Pipelines & Integration
Reliable ETL and ELT pipelines that connect siloed systems, spreadsheets and applications, so data flows automatically instead of being copied by hand.
Business Intelligence & Dashboards
Clear dashboards and self service reporting that answer real questions, so decision makers get the number they need without waiting on a data team.
Data Quality & Governance
Testing, validation, lineage and clear ownership so people can trust what the numbers say, and so your data meets audit and compliance expectations.
Real Time & Streaming Data
Streaming pipelines for cases where minutes matter, from operations monitoring to live dashboards, delivering fresh data without overwhelming the platform.
How we work
The same senior team, all the way through.
Map the sources
We map where your data lives, who owns it and how good it is, then agree the questions the platform must answer. Understanding the mess comes before fixing it.
Model & design
We design a data model and architecture that fit your scale and budget, favouring simple, maintainable choices over fashionable ones you will regret later.
Build the pipelines
We build tested, automated pipelines into your warehouse or lakehouse, with quality checks baked in so bad data is caught early rather than in a board report.
Serve & govern
We deliver the dashboards and access people need, set up monitoring and ownership, and keep the platform reliable, documented and cost aware as it grows.
- One
- Trusted source of truth across the organisation
- -40% typical
- Less time spent reconciling and preparing data
- 99.9%+
- Pipeline reliability, so reports are ready on time
Selected work
Data Engineering & Analytics in practice.
A global management consultancy
An AI radar that turns one sentence into a meeting-ready target list
- Research to target list
- Weeks → minutes
- Decision-makers found
- 231
- Contacts verified real
- 90%
A 28-division enterprise
An AI knowledge platform that answers from approved enterprise content
- Departments unified
- 28
- Enterprise data indexed
- 150+ GB
- Document governance
- Automated
FAQ
Questions we hear a lot.
What is the difference between a data warehouse and a data lake?
A data warehouse stores structured, cleaned data ready for reporting and analysis. A data lake stores raw data of any kind, including files, logs and images, at low cost. A lakehouse blends the two, giving you cheap flexible storage with the reliability of a warehouse, which is why it suits many teams doing both analytics and AI.
What is the difference between ETL and ELT?
Both move data from source systems into a central store. ETL transforms the data before loading it, which suits smaller or sensitive workloads. ELT loads the raw data first and transforms it inside a powerful cloud warehouse, which scales better and is now the more common choice. We pick based on your data, tools and cost, not on habit.
How do we get a single source of truth across our systems?
You bring the important data together into one governed platform, agree clear definitions for key metrics, and build pipelines that keep it up to date automatically. The technical work matters, but so does agreeing what a term like active customer actually means. We handle both, so reports finally agree with each other.
Why do our reports and dashboards show different numbers?
Usually because each report pulls from a different system, on a different schedule, using a slightly different definition. Without a shared source and agreed metrics, small differences pile up into contradictory figures. A proper data platform with governance fixes this by giving every report the same trusted, defined data to draw from.
Ready to put this to work?
Tell us the problem you are trying to solve. We will tell you how we would approach it, and whether this is even the right place to start.