Turning scattered data into a single, reliable source of truth.
Custom data pipelines, cloud data warehouse architecture, ETL/ELT design, and analytics dashboards that give your team decisions, not spreadsheets.
Service Data Engineering & Analytics
01 — Problems
You are probably here
because of one of these.
Reports that take hours to generate because no one query has all the data.
Business decisions made on exports that were accurate last Tuesday, not today.
Three systems with customer data that disagree on the customer count.
An analytics tool nobody trusts because the numbers never match the finance sheet.
Cloud data costs that grow every month with no clear explanation why.
Engineering time spent writing one-off data scripts instead of building product.
02 — Capability
What we actually do.
Cloud data warehouse design and build (BigQuery, Snowflake, Redshift)
ETL and ELT pipeline engineering with dbt and Apache Airflow
Real-time streaming pipelines for event and operational data
Data consolidation from CRM, ERP, ecommerce and ad platforms
Analytics dashboards connected to a governed, reliable data layer
Data modelling, documentation and team knowledge transfer
03 — Scope
What is in, and what is not.
The right-hand column is the one most studios leave out. Naming the exclusions up front is how a fixed price stays fixed.
Deliverables
- Data architecture diagram and entity-relationship model
- Documented, tested pipeline code in a version-controlled repository
- A governed data warehouse with documented table contracts
- Dashboard with agreed-upon metric definitions, not raw table queries
- Runbook for adding new sources and maintaining the pipeline
- Full handover of all infrastructure to your own cloud account
Not included
- Ongoing pipeline operation after handover, unless in a care plan
- Cloud compute costs — those run in your own account at cost
- Business intelligence licences (Looker, Tableau, Power BI) — quoted separately
- Historical data enrichment or third-party data acquisition
05 — Technical approach
What we build it with.
Chosen per project against your constraints — not a fixed house stack, and not a logo wall. The test is what your team can maintain after we hand it over.
Warehouse
- BigQuery
- Snowflake
- PostgreSQL
- Redshift
Transform
- dbt
- SQL
- Python
- Apache Spark
Orchestration
- Apache Airflow
- Cloud Composer
- Prefect
Visualisation
- Looker Studio
- Metabase
- Power BI
- Custom dashboards
How this gets proved
- Every pipeline ships with documented tests; a metric that cannot be traced to a source query is not published.
- Infrastructure is deployed in your own cloud account from day one — no data passes through ours.
- We define metric contracts in writing before building dashboards, so finance and product see the same number.
06 — Delivery
Checkpoints and timelines.
You sign off at each of these
- Discovery You approve the data model, source inventory and fixed price
- Foundation You review the warehouse schema and pipeline architecture
- Staging You validate data accuracy against known baseline figures
- Launch You sign off on the dashboard and the accounts transfer to you
Typical timelines
- Single-source pipeline and dashboard
- 3–5 weeks
- Multi-source consolidation and warehouse
- 6–12 weeks
- Full data platform with real-time streaming
- 12–20 weeks
Ranges, not promises. Your actual timeline is fixed in writing in the proposal once the scope is agreed.
07 — Engagement & Pricing
How you buy this.
Fixed price against a written scope. Payment is milestone-based, the number does not move unless you change the scope in writing, and the complete source code plus every account is transferred to you at launch. A 30-day defect warranty is included as standard.
Typical Pricing
- Starting Price
- ₹1,50,000+
Premium fixed-price contracts. Final costs depend on the approved scope.
08 — Questions
The ones people actually ask.
Yes. The first step in every engagement is a source audit. We document what exists, assess quality, and design the migration as part of the fixed scope — not as a surprise addition later.
All infrastructure runs in your own cloud account. Data does not leave your environment, and we document the access controls applied. If a specific compliance framework applies, tell us at discovery and we will design for it from the start.
Every metric has a written definition agreed before we build it, and every pipeline has documented tests. The UAT checkpoint at staging is specifically a data-accuracy review against known baseline figures — not just a visual sign-off.
Yes. We build the data layer — the warehouse, pipelines and governed tables — and connect it to whichever visualisation tool you already use or prefer. We are tool-agnostic at the presentation layer.
Also from the studio
Other disciplines.
- AI & Intelligent Automation Put the repetitive half of your operation on rails.
- Web Platforms Web products that load fast, rank, and convert.
- Mobile Products iOS and Android apps, released and maintained on both stores.
- Custom ERP & Business Systems Software shaped like your operation, not like someone else’s.
- Product Design & UI/UX Decide how it works before deciding how it looks.
- APIs, Cloud & Integrations The layer everything else depends on — built to be handed over.
- Care Plans Someone still responsible after launch — by arrangement, not obligation.
Project qualification
Get a fixed-price proposal.
Tell us the problem, the stage you are at and the budget band. You get a discovery call, then a written proposal with exact scope, milestones and a final number — within 24 hours.
- Response
- Fixed-price proposal within 24 hours
- Confidentiality
- Mutual NDA signed before any project detail is discussed
Prefer to just talk? WhatsApp is answered fastest during studio hours.