// case study · saas & technology

Analytics That Tell You Something.
Not Just Something That Looks Like Analytics.

We built the Affiniti SaaS analytics platform from concept to production - an Angular frontend, a Node.js API layer, and a Python analytics pipeline, all containerized with Docker.

// the receipts
SaaS
Full Platform
3-tier
Architecture
Python
Analytics
Docker
Containerized

// the challenge

Most analytics software looks like answers without being them.

Analytics software has a design failure mode everyone recognizes: impressive dashboards full of charts that don't tell anyone anything actionable. Lots of color. Lots of data. No signal. Affiniti was building a SaaS analytics platform that needed to do the harder thing - surface the information that actually matters to the people who need to act on it.

That requires product judgment as much as engineering execution. Building good analytics software means understanding what questions users are actually trying to answer. The technical requirements were real too: a responsive, real-time-capable frontend, a Python data processing layer with real analytical depth, and an architecture that could evolve as the product learned what users needed.

Dashboards Aren't Answers. Signal Is.

200+analytics
shipped
Scope Your Analytics Build

// the approach

Four layers, one signal.

Angular for a Data-Rich Frontend

Analytics applications put unusual demands on the frontend - complex state, frequent updates, dense information displays that have to stay usable. Angular's structure and component model handled that complexity without the interface becoming a maintenance problem.

Node.js API Layer

The service layer connecting the frontend to data processing - handling authentication, routing, and the operational concerns that let the Python layer focus on the actual analysis.

Python for Analytical Depth

Python's ecosystem for data processing and analysis is unmatched. The analytical logic - the part that turns raw data into the signal Affiniti's users needed - lived here, with access to the tooling that makes serious data work possible.

Docker for Deployment Consistency

Containerized services meant the same behavior in development and production, easier scaling, and a deployment process that didn't involve praying. The architecture could evolve individual services without disrupting the whole system.

// the outcome

Production

SaaS platform - concept to deployment

A production SaaS platform with a data pipeline capable of delivering real analytical signal.

An Angular SaaS interface over a Node.js API and a Python analytics pipeline - built from concept and shipped to production as a containerized, three-tier architecture.

3-tierFrontend + API + Python pipeline
DockerContainerized deployment
AngularSaaS analytics interface
Concept To Production - Real Signal, Shipped

// technologies used

Built on a full-stack SaaS toolchain.

Angularfrontend
Node.jsapi layer
Pythonanalytics
Dockerdeployment
REST APIsintegration

got a project?

If your analytics don't change how your
team makes decisions, you're paying for art.

Real signal beats dashboard wallpaper every time. Tell us what you're trying to learn from your data.