The unified
data layer beneath
every intelligent system.
Data engineering and platform services that unify fragmented data across legacy ERP, IoT telemetry, operational databases, and document repositories, making your data usable for AI, analytics, and operations.
Ask AI about this service
Intelligent software is only as good as the data underneath it. Most enterprise AI projects fail not because the models are wrong but because the data infrastructure underneath them cannot support production use. We design and build the data engineering layer that makes AI and analytics possible: ingestion pipelines from heterogeneous sources, ETL/ELT orchestration, data warehouse and lake architecture, real-time stream processing, and data quality frameworks. This is the work that makes the AI work.
What we build.
Ingestion Pipelines
Reliability-first ingestion from heterogeneous sources, legacy ERP, IoT telemetry over MQTT, document stores, and operational databases, with idempotent operations, dead-letter queues, and freshness monitoring.
ETL/ELT & Transformation
Pipelines that clean, normalise, join, and enrich raw source data into analytical-grade datasets using dbt, Apache Spark, and Apache Airflow with dependency management and SLA monitoring.
Data Warehouse & Lake
Warehouse and lake architectures matched to your scale and deployment constraints, PostgreSQL on-prem, Snowflake, Databricks, and Redshift in the cloud, with Delta Lake or Iceberg for ACID operations.
Real-Time Stream Processing
Stream processing on current data for telemetry monitoring, anomaly detection, and real-time dashboards using Apache Kafka and Flink, with time-series storage in TimescaleDB or InfluxDB.
First pipeline moving live data in 10 days.
From the day you engage us, not from a kickoff weeks later. Every milestone is a result you can see.
- Day 3
Source systems inventoried and data contracts drafted
- Day 10
First production pipeline with freshness and quality checks
- Day 30
Governed warehouse or lake serving your first dashboards and models
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Fixed scope
Scope, price, and milestones are agreed before work starts.
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No surprises
Weekly demos and plain-language status. Changes are priced before they are built.
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Compliance from day one
Lineage, access policies, and data-quality gates are built into each pipeline as it is written.
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You own the IP
Code, models, and documentation transfer to you on successful handover.
Know the price, the date,
and the exit before we start.
Fixed-scope project. A defined deliverable at a fixed price and date. Payment is released as each milestone is accepted. The same terms are written into every statement of work for data engineering, so there is nothing to discover later.
- Quote Within 5 working days Price per milestone, dates, and named architect
- Priced One price per milestone Third-party costs listed separately, at cost. Invoiced in USD for international clients
- Paid On accepted working software Not on calendar dates or hours logged
- Changes Written and priced first Cost and date impact before work starts
You fund accepted work, never a promise.
Drag to move through the engagement. Money is released only as each gate is accepted, so what you have paid never runs ahead of what you can use.
- Milestone 1Day 3
Source systems inventoried and data contracts drafted
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 20% - Milestone 2Day 10
First production pipeline with freshness and quality checks
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - Milestone 3Day 30
Governed warehouse or lake serving your first dashboards and models
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - HandoverAt final acceptance
Code, architecture documents, deployment scripts, and credentials transferred to you.
Accepted whenFinal acceptance signed. Defects then fixed under warranty at no charge.
Releases 20%
Illustrative split. The exact percentages and dates are fixed in your statement of work.
When something goes wrong, who pays?
Every project meets surprises. What matters is who carries them. Pick one and compare how three commercial models respond.
The hours keep running while the team investigates. You pay for the surprise.
A change request is raised, usually priced at a premium and argued over.
We verified it before quoting, and the risk register held an allowance. Any overrun beyond it is ours.
It is absorbed into the hours. The date slips quietly and you learn at the invoice.
A change fee arrives, often as a lump sum with little explanation.
You get a written amendment with cost and date impact. Nothing starts until you approve it.
You pay for the difference. The estimate was only ever a guess.
The vendor reopens scope to recover margin, or cuts corners to hold it.
The price stays fixed. The overrun is ours to carry, and you see the budget position at every demo.
It is billed on the calendar anyway. The status report says "on track".
The invoice follows the date in the contract, whether or not the work is done.
Payment for that milestone is held until it passes the written criteria. You do not fund a miss.
One price, built in the open.
The proposal itemises every feature or milestone, names its assumptions, and keeps a risk register with allowances. Allowances you never use are not billed.
- Number and type of source systems
- Data volume and freshness needs
- Quality and lineage requirements
- Warehouse, lake, or lakehouse target
Source-system access and a data owner are named in week one.
If falseDates move by the delay, agreed in writing. The price holds.
The source schemas match the inventory we agreed.
If falseExtra sources or schema drift become a costed change request.
Data-quality thresholds are fixed in the data contracts.
If falseChanging a threshold is priced before it is built.
- Cloud hosting
- Third-party licences
- Model and API usage
Invoiced in USD for international clients, with the currency fixed in the SOW. You own it. Code, models, documents, and credentials transfer on payment.
Built on a production-grade stack.
We pick tools proven in production and maintainable over long lifecycles - not whatever is trending. These are a few we reach for here; explore the full stack behind our engineering.
Explore our technology stack →
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Read article →What buyers ask before they commit.
Straight answers on timelines, price, risk, and ownership, the same terms that appear in every proposal.
01What is Data Engineering?
Data engineering and platform services for enterprises and PSUs. ETL/ELT pipelines, data warehouse, stream processing, and unified data infrastructure across legacy ERP, IoT, and documents.
02How quickly does Data Engineering deliver a first result?
First pipeline moving live data in 10 days. Typical milestones are Day 3: source systems inventoried and data contracts drafted; Day 10: first production pipeline with freshness and quality checks; Day 30: governed warehouse or lake serving your first dashboards and models. Exact dates are written into the statement of work before you commit.
03How is Data Engineering priced?
Fixed-scope project: one price per milestone, with a first-release date, itemised in a statement of work within five working days of scoping. Payment is released as each milestone is accepted against written criteria. Cloud hosting, licences, and model usage are passed through at cost. International clients are invoiced in USD. The main price drivers are number and type of source systems, data volume and freshness needs, quality and lineage requirements, warehouse, lake, or lakehouse target.
04What happens if the scope changes or the work overruns?
The price stays fixed. A change to scope is quoted in writing, with its cost and date impact, and starts only after you approve it. If our own estimate was wrong, the overrun is ours, not yours. You see the budget position against milestones at every demo.
05Who owns the code, models, and documentation?
You do. Code, models, and documentation transfer to you on successful handover. Code, architecture documents, deployment scripts, and credentials transfer on payment, with no licence back.
06How are security and compliance handled?
Lineage, access policies, and data-quality gates are built into each pipeline as it is written.
07Do you work with clients outside India?
Yes. We are based in Noida and work with clients around the world, delivering remotely. Calls and demos are scheduled in your time zone, proposals and contracts are in English, international clients are invoiced in USD, and every engagement follows the same fixed-scope terms regardless of where you are.
08When would you decline to fix a price?
If the scope is not yet defined, we do not guess a number. We propose a short, time-boxed discovery with its own fixed price, then quote the build.
Build the data layer your AI depends on.
If your AI pilots are failing because the data is unreliable, incomplete, or inaccessible, the problem is the data infrastructure, not the model. We can audit your current data landscape and design the engineering layer that makes your analytics and AI investments work.