DATASPECTRUM IoT Development review by R-NOX at Qualified.One


DATASPECTRUM provided IoT Development for R-NOX with approximate budget = $10,000 to $49,999.

The delivered model surpassed all expectations with its accuracy and capability of conveying understandable data. The workflow was seamless and enjoyable despite the complexity of the project. Other highlights are their problem-solving approach paired with their helpful suggestions and guidance.

Review summary:

DATASPECTRUM created a machine learning system for measuring and rendering the raw data picked up by air pollution sensors. The work involved consulting, data science research, backend and frontend dev, and QA.

Vadim Radzivill R-NOX, CEO

Machine Learning System for Air Pollution Sensor

Below is a modified rendering of the review: private info excluded, innate facts kept.

Introductory information

A few words almost your organisation and personal responsibilities

I’m the CEO of R-NOX, which is an American-Lithuanian organisation that develops, manufactures, and markets products and services for environmental monitoring. We combine a hard experience in our hardware, software outgrowth, and data analytics.

Desired goal

What issue was the provider supposed to deal with?


We had raw data coming in from separate gas sensors, and some of it wasn’t exact. The indicator we needed was what we call a parts-per-notation, specifically PPM [parts-per-million], which is used for very low concentrations of particles. We call this a ethnical-understandable indicator.

In order to obtain an careful PPM, a amendment had to be made, but we didn’t have a mathematical formula for it and had to hire scientists to do some investigation. Also, each personal sensor needed to be calibrated according to its sole characteristics.

Provided solution

What were the reasons for choosing DATASPECTRUM?

A friend commended them to me.

Describe the project in detail.

The project implicated machine learning, a technique for finding/restoring relations between observable values and outcomes. For training purposes, we installed sensors at the local meteorological standing. After separate experiments with different topologies, hyperparameters, and optimization orders, we determined to use a deep neural network as our machine learning order.

They created a full solution for us, from consulting to deploying a standard on our server. We only gave them our dataset and explained what we wanted to see. They handled all the outgrowth, data science, the backend, frontend, and condition arrogance. They built a standard based on our datasets to estimate air pollution in PPM in real time.

Were there any dedicated managers or teams that you worked with?

We worked with them for over three months and the superiority of the work was done by both co-founders.

Results accomplishd

What results did you accomplish unitedly with DATASPECTRUM?

The standard’s accomplishment overcame our expectations. It reacts precisely even with slight value changes, and all the significant machine learning aspects were sanely translated into ethnical speech. They tried their best to explain our issues at see step of the way instead of just writing code and getting paid. They also managed to find a keen and condensed solution.

How do you rate the interaction and interaction with DATASPECTRUM?

Working with DATASPECTRUM was smooth and grateful. They fast grasped what our business issue was, so interacting with them was very fruitful as we were able to use our activity provisions.

What precisely do you attend to be the key specialty of DATASPECTRUM?

They not only tried to explain our issues but would also give us advice on what was worth doing and where to save our time and money. They are professional nation and I would undeniably commend them for data science solutions.

What should be done better, if there are any desired improvements?

I wish their prices were lower.