How to Hire the Most Trusted AI Developers

Are you looking for AI developers to make your digital products smarter?

You’re at the right place.


You need Artificial Intelligence developers that you can trust, who can create AI products your customers can trust too.


Trust is a vital part of AI. Individuals or businesses that would use your product or service want to be able to trust you to create an ethical piece of software. And you need to trust those you put in charge of creating that software to do what’s right.


Rootstrap is an AI company you can trust. We don’t just build AI software for our clients. We collaborate fully with them. We take ownership of our work. We educate our clients.


Do you want to get started on your project?


While there are many AI developers out there, very few can give you the quality service you’ll get from us.

Below we share why our clients chose us. And why you should do the same. 


We don’t use unsuitable languages to build AI software for our clients. The right language helps us write less code and improves the performance of the product and services we build.


Here are our favorite and best languages for writing AI software:


1. Python

2. R

3. Java

4. Lisp

5. JavaScript


Let’s dive into each language.



We use the best programming languages for AI development

1. Python


Python is the best language for data science and machine learning (ML). There’s a massive number of frameworks and libraries for ML written in Python. The language has a big community that supports it.

R is mainly used for data analysis, big-data modeling, and data visualization. Banks use it to predict risks. There’s a large number of libraries and frameworks written with it.


2. R


Python is the best language for data science and machine learning (ML). There’s a massive number of frameworks and libraries for ML written in Python. The language has a big community that supports it.

3. Java


3. Lisp


Lisp is a family of programming languages. The most popular dialects are Clojure and Common Lisp. Lisp used to be the most popular language for AI. While its AI concepts still exist, they’re quite different from today’s AI ideas. 

4. Javascript


JavaScript (with Node.js) is the most popular programming language for web development. It has increasingly become even more popular over the past years, and that is making developers extend its use to AI.

We adequately pre-process and store data

Data is a big part of AI. Your project will collect a lot of data from customers or users.


We’re comfortable taking you through our process of handling and storing data. We’ve done that many times with our clients. 


We ensure that your data pipeline is well set-up and smoothly connects multiple datasets to the next stage (or process), where data analysis is performed.


Our talented data engineers can effectively bring terabytes of data from where it lives to where it can be analyzed. Our data engineers work with databases, data processing frameworks, web interfaces and APIs, along with other components that help them prepare data for use.

Someone writing on a sticky note next to a laptop

We have the right people to analyze your data

Of course, different kinds of data could be collected. If the people analyzing it have zero or little experience in data analysis, it becomes useless. 


Our data scientists have decades of experience working in various industries, analyzing different kinds of data.


Our data science team consists of skilled mathematicians and statisticians who know computer science, machine learning, deep learning, advanced analytics, and data storytelling.


At Rootstrap, we’ve worked with different clients in various industries, analyzing their data and gaining deep insights that helped them improve their digital products and services.


We don’t work on your project alone. We also bring in domain experts during data analysis.


Let’s say we’re building an app that helps people work out from home. Our data scientists may work with athletic trainers, physical therapists, and nutritionists to fully understand what to analyze.


We apply the best machine learning models

We’ve applied various machine learning algorithms in different projects for our clients. Each situation is different.


Machine learning algorithms are applications that can automatically learn and improve from experience without human intervention. 


There are three types of ML algorithms:


1. Supervised learning

2. Unsupervised learning

3. Reinforcement learning


Allow me to explain each one.


1. Supervised learning algorithms


Supervised learning means teaching the model based on a labeled dataset. The labeled dataset has both input and out parameters.


In supervised learning, we train and validate the datasets.


Let’s say you run a fashion store. We could develop a supervised learning algorithm that predicts what a customer will buy based on their age, gender, and salary, assuming we have this data.


We may say that a male customer is likely to be interested in men’s wear. And a female customer would want to buy women’s wear.


Then we start testing, and if it’s right 80% of the cases, we could consider it successful.


2. Unsupervised learning algorithms


Unsupervised learning algorithms work with unlabeled datasets, as opposed to supervised learning. We give the algorithm unsorted information and ask it to find similarities, patterns, and differences all by itself.


For example, let’s say you upload pictures of dogs and cats into a machine, which it has never seen before. The machine has no idea of what they are, but it can categorize them into two groups based on their similarities, patterns, and differences.


3. Reinforcement learning algorithms


Reinforcement learning means learning through trial and error. The machine learns behaviors that maximize reward in a particular situation.


No labeled dataset is given like supervised learning. But the model learns from its experience.


For example, let's say you play a video game where you move to certain places at certain times to earn points. The model will start by moving randomly. Over some time, through trial by error, it would know where to be and when to be there to get high rewards.


It learns from both positive and negative reinforcements.

Positive reinforcement increases its strength. And it’s likely to do more of those behaviors that led to them.

Negative reinforcements are events or actions that led to errors, and it'll learn to stop or avoid them.


We inform our clients about available machine learning services

Once we decide on the most appropriate learning model, we can either choose to build a model from scratch or utilize available services.


There’re lots of ML services that are available to us. We don’t always have to start from scratch.


For example, if you’re building a chatbot, we can use Amazon Lex. Amazon Lex gives us deep learning functionalities of automatic speech recognition (ASR) for converting speech to text. This service also gives us natural language understanding (NLU), which helps your chatbot know the intent of the text.


Amazon uses Lex to power Alexa, its virtual AI assistant. So, we can quickly build a chatbot with it.

Amazon Lex is just an example. There are many other ML services available to developers.


We inform our clients about available services. For us, our clients are part of the development process. We educate them on the best ways to move forward.

We tell clients how we deploy their work

We don’t spend hours, weeks, or months building a product to watch it run slow. For us, building the product is half of the work. Making sure it runs smoothly is the other half. We manage our work throughout the process to make sure there is perfect completion.


We ensure that your digital product works for people across the globe. 


Deploying regular applications is hard. Deploying a machine learning app is even harder.


Regular applications are often written in one programming language. ML applications are usually built with multiple languages, and they don't always interact well with each other.


Working with Rootstrap means you won’t have to worry about your product performance because we’ve got you covered. 



We secure your machine learning applications

We don’t put security on the back burner. Our goal isn’t just to get the software to work. We also apply the best security practices as we develop the app. 


Like most apps, AI products could have vulnerabilities that can be exploited. The risks are even greater when you consider the fact that AI products collect more data than other technologies.


The best AI companies should explain if and how they deal with security in the early stages of development.


If your AI technology is exploited and data is stolen, you would find it difficult to earn the trust of users.


Your AI app would probably collect different kinds of data that users may not want others to have. That is why we take security seriously when developing an AI feature, product, or service for our clients.


Instead of going through the time-consuming process of vetting AI companies out there, why not work with trusted AI developers at Rootstrap?


We have experience and expertise in building AI applications consumers and businesses trust.


Start Your Journey Today - With Rootstrap

The premier app development company trusted by the fastest growing startups + Fortune 500

“Rootstrap is completely dedicated and obsessed with being the best development partner in the world for VC-Backed Startups & Fortune 500... speak to myself or anyone on the team and you'll see why our clients trust us with their mission-critical projects!"

Rootstrap

Founder & CRO

New York, San Francisco & Buenos Aires

3 Satellite Offices

Los Angeles & Montevideo

2 HQs

Team Members

134

$25B

Combined value of our unicorns

and enterprise clients

2011

Founded

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Rootstrap does away with the traditional model of blind guesswork.

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Rootstrap builds mobile apps, websites, and other digital products for clients ranging from early-stage founders to Fortune 100 enterprise companies.


We are a full-service design and development team, as well as strategic partners to our long term clients.


We’ve won 15 industry awards for our digital transformation process. Our client list features marquee names like Google, MasterClass, Tony Robbins, and Disney. Thanks to the efforts of our global team of designers, engineers, and thinkers, Rootstrap alumni have a 2,600 percent increase in their chances of getting funded compared to the average startup. One of our alumni received a $25 million acquisition only 36 months after inception.

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