01
Indian businesses who need data engineering built for their real operational constraints.
Fits you
Active engagements
99.9%
Uptime
2.4×
Velocity lift
0
Data loss
Data Engineering
Stop making decisions from a day-old spreadsheet.
Real-time pipelines, modern warehouses, reliable dashboards. The data stack that survives year two.
ManufacturingBFSIE-commerceHealthcareLogisticsManufacturingBFSIE-commerceHealthcareLogisticsManufacturingBFSIE-commerceHealthcareLogistics
Who this is for
Who this is for.
If any of these sound like you, we should talk.
02
Teams who’ve tried off-the-shelf data engineering tools and hit ceilings within 6 months.
Fits you
03
Founders who want an engineering partner, not a vendor.
Fits you
04
Established enterprises modernising legacy data engineering systems.
Fits you
The proof
How we structure data engineering projects.
LAYER 1
Foundation
Infrastructure, observability, security baked into day-one architecture.
LAYER 2
Capability layer
Core features your team uses daily. Built iteratively, validated weekly.
LAYER 3
Operations
Monitoring, on-call runbooks, upgrade paths. How it runs in year two.
What we build
Modules we ship.
01
Data warehouse
Data warehouse integrated into your operations, not bolted on.
02
Real-time streaming
Real-time streaming integrated into your operations, not bolted on.
03
Transformations
Transformations integrated into your operations, not bolted on.
04
BI layer
BI layer integrated into your operations, not bolted on.
ClientAn enterprise clientEnterprise
BI refresh went from <em>overnight batches to minutes</em>. Executive dashboards live.
Retailer rebuilt data stack on Snowflake + Airbyte + dbt. Eliminated 3 legacy ETL tools.
01
99.9%
Uptime
02
40%
Cost reduction
03
2.4x
Velocity improvement
04
0
Data loss incidents
What it's like working with us
They didn’t try to sell us the biggest solution. They sold us the right one. That’s rare.
RV
Rajiv V.
VP Engineering · An enterprise client
Data Engineering · India
Tech stack
The tools. Chosen for your reasons.
07technologies in rotation
01Snowflake
02BigQuery
03Airbyte
04dbt
05Airflow
06Kafka
07ClickHouse
Process
How we actually work.
06 stages
- 01
Discovery
Weeks 1-2Current state, pain points, constraints.
- 02
Architecture
Weeks 2-4Blueprint for what’s being built.
- 03
Core build
Weeks 4-10Foundation modules, tested.
- 04
Integration
Weeks 10-12Connect to your existing systems.
- 05
Rollout
Weeks 12-16Phased deployment, training.
- 06
Operate
Week 16+Monitoring, improvements, retainer.
Questions
Answers, without the fluff.
Still have questions? Talk to us — we answer within a business day.
04common questions
01How long does a typical engagement take?
Most projects run 10-18 weeks from kickoff to production launch. We share a milestone plan in week one and update weekly.
02Do you sign an NDA?
Yes. Standard mutual NDA on request, before the first technical conversation.
03Who owns the code and IP?
You do. Code is in your GitHub org from day one. All IP transfers unambiguously on delivery.
04What does your pricing model look like?
For v1 builds: fixed scope, fixed milestones. For ongoing work: monthly retainer with a defined team. We don't do time-and-material surprise billing.
Related
Explore more of what we do.
Data Engineering built properly.
A 30-minute call. No sales deck. Specific answers to specific questions.
NDA available.