Cloudnonic builds the data pipelines, analytics and reporting that make a product useful to the people who operate it: field devices streaming into a monitoring platform, product catalogs feeding an optimisation engine, operational dashboards a team opens every morning. We work in Python and PostgreSQL on AWS, inside the product rather than next to it, with the same ownership of correctness and operations as the rest of the platform.
Charts are built from whatever the database contains, and two teams read different numbers for the same metric.
Meetings spent arguing about data instead of deciding, and a board deck nobody trusts.
Each metric is defined once, with its owner and its source, before a chart is drawn.
A transformation bug is found weeks later and there is no way to recompute history, so the error is documented instead of fixed.
Permanently wrong reports and manual corrections for every period.
Raw data is kept, transformations are versioned and idempotent, and reprocessing a period is a routine operation.
A BI vendor builds reports next to the product, with its own copy of the data and nobody owning the gap between them.
Stale numbers, duplicated costs, and a product that cannot show its own users what is happening.
Analytics is built into the product by the team that owns the data model, and reporting tools read from the same definitions.
Data work is usually part of a larger platform engagement, because the value is in the product the data feeds. It can also start as a fixed scope: one pipeline and one set of dashboards with an agreed definition of correct, which then grows with the product.
Your product team · What this prevents: A prototype that never becomes a platform, and a CTO hire made before the product deserves one.
The two engagement models →Chosen per project. We pick what your team can run after us, not what is fashionable.
Python
PostgreSQL
FastAPI
AWS Lambda
ReactReactAWS LambdaFastAPIPostgreSQLPythonCase studies in this scope. Every figure comes from the study behind it.
PythonReactAWSPythonReactAWSFastAPIReactPostgreSQLAWS Lambda
TerraformWhen the product needs one. For most of the platforms we run, PostgreSQL with well-designed schemas and Python processing on AWS covers the analytical load. We introduce a warehouse or a streaming layer when the volume or the latency requires it, and we say which numbers justify it.
Yes. We have built ingestion for hundreds of field devices across several device types into a single monitoring platform, with per-tenant views for operators and partners. The pattern is the same for any source that streams events.
Definitions agreed with the business, tests on the data itself, freshness and volume checks, and reprocessing that lets us recompute a period after a fix. Correctness is monitored like uptime.
Dashboards built into the product in React when the users are your customers or operators, and your existing BI tool when the users are internal. Both read from the same metric definitions.
Every service on this page is delivered with our AI-Native process: specs as the source of truth, agents in the pipeline, and a named engineer signing off at staging.
How we build with AI →
