SAP to open-stack ERP migration
Manufacturer, 500+ employees. Phased migration off SAP ERP onto an open stack with custom modules — run in stages while the plant kept working.
- 0 downtime
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Data engineering: making the numbers trustworthy before anyone models them
Every AI project that stalls has the same first slide: the data existed, and nobody could vouch for it.
What we can show you
Data engineering
Five layers, and trust is the output
Ingestion, transformation, storage, quality, observability. A dashboard on top of untested data is a confident-looking guess.
What data engineering services actually build
A data platform is five layers. Ingestion pulls from applications, APIs and files on a schedule that can be re-run safely. Transformation turns raw rows into the shapes the business talks in. Storage keeps both, because the raw copy is your only way to fix a mistake later. Quality tests the data the way you test code. Observability tells the owner when something stopped arriving.
The fifth layer is the one that gets skipped, and it is why so many companies have dashboards nobody trusts. We build data platforms so that a wrong number is loud, not quiet.
When to invest in data engineering
When two reports disagree and nobody can say which is right; when an AI or analytics project is spending most of its time on data rather than on modelling; when a person re-runs an export by hand every Monday. If a single managed database and three queries still cover you, we will say so rather than sell a warehouse.
Signals your data needs engineering, not another dashboard
- two reports show different numbers for the same thing;
- someone re-runs an export by hand on a schedule;
- an AI project is stuck on data preparation, not on the model;
- nobody can say where a particular field came from;
- a pipeline failure is noticed a week later, by a person.
Price
Audit $1,790, builds from $7,990
Timeline
Audit in 5 days
Payment
50/50 by milestone
Comparison
Warehouse project or three reliable pipelines
The expensive answer is not always the right one — and this is the comparison vendors avoid.
Criterion
Warehouse programmeTargeted pipelines
Targeted pipelinesFull warehouse programme
Time to first trustworthy number
Months
2–4 weeks
Cost
Six figures is common
From $7,990 per scope
Who has to staff it
A data team you may not have
Your existing engineers
Fits when
Many sources, many consumers, governance requirements
A handful of sources and a few decisions that depend on them
Risk
Stalls when priorities change
Delivers value before the next reorg
When it wins
Regulated reporting, dozens of sources
Most companies under a few hundred people
Process
How we build a pipeline you can trust
Data tests are the part that distinguishes engineering from scripting — they come with the first pipeline, not later.
Tests on data, not just on code
Freshness, completeness, uniqueness, referential integrity and business rules — checked on every run. Failures quarantine the bad rows and alert the owner rather than dropping them quietly.
A pipeline that hides bad data is worse than no pipeline: it converts a visible problem into an invisible one.
Source contracts
Every source gets a written contract: fields, types, expected volume and what happens when the application team changes a schema without telling anyone.
Schema drift is the most common cause of silent breakage, so it gets its own alert.
Re-runnable by design
Idempotent loads, partitioned by time, with backfill built in. Re-running yesterday must produce the same result, not duplicates.
Raw data is kept as it arrived, because the only way to fix a transformation mistake later is to still have the original.
Ownership handed over
Code in your repository, infrastructure as code, documented lineage and a runbook. The business owner of each number is named in the documentation.
Where the goal is AI, this is the layer that makes it possible — see AI development.
Why us
Why teams bring data work to us
Four things we can prove, rather than four adjectives.
Boring tools on purpose
Postgres or a managed warehouse, a scheduler your team can read, dbt where transformations justify it. Fashionable stacks are expensive to hire for and hard to hand over.
Failures are loud
Alerts go to a named owner with the failing check and the affected rows, not to a channel everyone has muted.
We will say you do not need a warehouse
Three reliable pipelines often beat a warehouse programme nobody staffs. Recommending the smaller project is part of the job.
Your repository, your data
Everything lives in your accounts, with lineage and a runbook. The platform survives us leaving, which is the only real test of a handover.
Industries
Data engineeringby industry
The hard questions differ by domain: stock accuracy in logistics, commission truth in property, unit economics in e-commerce.
- IT companies and digital agencies
- Insurers and brokers
- Lenders and microfinance
- E-commerce
- Wholesale and distribution
- Manufacturing
- Freight and forwarding
- Clinics and medical centres
- Dental
- Car dealers and service centres
- EdTech
- Legal and consulting firms
- Equipment rental
- Real estate agencies
- Property developers
- Travel and events
- Beauty and wellness
- Construction and renovation
FAQ
Data engineering questions
Six answers that usually replace a discovery call.
Where do you start?
With an audit: sources, volumes, quality profile, the reports in use today and who owns which number. $1,790, credited against the build.
Do we need a data warehouse?
Not always. Three reliable pipelines and a managed database often beat a warehouse programme nobody has the people to staff.
Which tools do you use?
Deliberately boring ones: Postgres or a cloud warehouse, Airflow or a managed scheduler, dbt where the transformations justify it, Python for the rest.
Can you prepare our data for AI?
That is the most common reason clients arrive here. Retrieval quality and evaluation depend on the data layer more than on the model.
What happens to bad data?
It is quarantined and the owner is alerted, never silently dropped. Hiding bad rows turns a visible problem into an invisible one.
Who owns the pipelines afterwards?
You do: code in your repository, infrastructure as code, documented lineage and a runbook your team can follow without us.
2–4×
faster on the code-writing share
in 3 weeks
from idea to MVP
230+
projects delivered
17 years
of engineering experience
Work
Work we can show you
SMARTWILL
Digital inheritance service (Web3)
AIRDEVELOPPA
Indoor air quality finder driven by sensor data
CAR5
Car-sharing app
APP.DENTAL
Cloud CRM for dental clinics
NEXORA
Warehouse and logistics management platform
As-built docs
Digital as-built documentation
EX211
Telegram Mini App for currency exchange
SMARTWILL
Digital inheritance service (Web3)
Have data nobody trusts?
Send the list of sources and the report that is currently disputed. You get a quality profile, the three fixes with the highest payoff and a fixed quote for the first pipeline.