Custom Artificial Intelligence and Machine Learning Services

Take a Step into the Future of Business with AI and Machine Learning

Artificial Intelligence and Machine Learning are two of the most critical drivers of innovative business opportunities today. These technologies are accelerating the evolution of processes, products, and services in virtually every industry and vertical in the world. And, with our help, you can leverage their full potential and seamlessly integrate them with your day-to-day operations. 

BairesDev hires the Top 1% of IT Talent in the region to provide the highest-quality software services in the market. Our goal is to help you understand and seize all of the high-value opportunities that AI and Machine Learning bring to the table. No matter the size or complexity of the project, we deliver custom-built technology solutions designed to exceed your expectations. 

Business AI

Our Services

We work with the Top 1% of IT Talent to deliver the highest-quality AI/ML solutions.

  • Custom AI-Driven Enterprise Solutions

    Access the full spectrum of artificial intelligence solutions with our enterprise services package. From personalized experiences and augmented operations to predictive models and collaborative intelligence, we deliver custom-built AI software designed to accelerate your company on the back of next-gen technology.

  • AI-Driven Processes

    Build a competitive advantage in your industry by integrating artificial intelligence and machine learning into your business processes. We design powerful systems focused on innovation, readiness, and outcome effectiveness. With AI-Driven Processes, your business will run on data-driven initiatives that generate value consistently and predictably.

  • Data Provisioning

    The best AI/ML systems have robust and scalable data infrastructures behind them. Implement a comprehensive data culture that covers all processes related to information management, including data collection, data mining, data creation, data aggregation, exploration, linguistic assets, and Natural Language Processing.

  • Machine Learning Models

    Design a custom Machine Learning model for your company or have your current implementation tested and assessed by the best AI and ML engineers in the industry. Our testing methodology guarantees the reliability and accuracy of your Machine Learning model via model testing, regional validation, testing with real-world target audiences, and performance reporting.

  • Deep Learning Technologies

    Augment the capabilities of your business, employees, products, services, and processes with cutting-edge technologies driven by AI. These include demand prediction, anomaly detection, fraud detection, medical diagnosis, face detection, object identification, optical character recognition, person tracking, augmented reality, voice recognition, text mining, sentiment analysis, and many others.

  • Human to Machine & Machine to Machine

    Automation is the future of business. We design, develop and implement custom human-to-machine and machine-to-machine AI solutions that create interactive, flexible, and secure automated processes. Interactive next-gen chatbots, digital assistants, voice recognition, intention recognition, and programmed decision making.

BairesDev Best Practices

Artificial intelligence and Machine Learning can be fundamental components of successful software development and delivery. While there are many approaches towards achieving this goal, these are the Best Practices that we follow and recommend at BairesDev and have proven to be successful across many customer engagements. Our AI/ML Circle is the center of excellence that defines and maintains these standards and ensures that knowledge and practices are shared across our organization. Throughout our engagements we:

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    Collaborate With the Product Owner
    To understand the context of the problem and the business impact the solution will have in order to define a clear objective for the machine learning service.
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    Determine the Most Appropriate Use Case
    In order to fulfill the defined objective. 
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    Conduct a Thorough Examination of the Available Data
    This includes assessing the quantity, quality, and sources of the data to ensure that we have or can obtain the appropriate data required to fulfill the objective. 
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    Engage in Data Pre-processing and Feature Engineering
    We document the feature engineering process to guarantee that the model inputs are as clear as possible. 
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    Architect Loosely Coupled Services
    By keeping the training and predicting services separately, we can more easily isolate errors resulting from changes in the code.
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    Perform Sanity Checks
    Before models are released into production.
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    Understand the Frequency
    With which the model should be updated and the business impact of the update frequency.
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    Apply Machine Learning Operations (MLOps)
    By leveraging continuous integration and continuous delivery to ensure that code changes in the machine learning service will not negatively impact the application.
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    Start With a Low-Complexity Machine Learning Model
    That addresses the business problem before using a more complex one. Examples of less complex models include using a linear regression or logistic regression model.
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    Identify Objective Metrics
    To measure the performance of the model.
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    Use Experimentation Techniques
    To test and improve the model.
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    Create Pipelines
    That will orchestrate these tasks.
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    Perform Feature Importance Analysis
    To make machine learning models explainable and reduce dimensionality when possible.
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    According to the infrastructure changes that may be necessary.
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    Limit Technical Debt
    By cleaning up features we are no longer using.

At BairesDev, the machine learning engineer participates actively in all stages of the application development to guarantee that the solution will be optimized for each specific case. 

What Can Your Company Do With AI and Machine Learning?

A lot! Here are some of the highlights.

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    Natural Language Processing
    Process data from human language like never before.
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    Smart Assistants and Chatbots:
    Create efficient and engaging automated interactions.
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    Process Automation
    Use cutting-edge technology to maximize efficiency.
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    Smart Segmentation
    Identify and track data from customer segments automatically.
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    Smart Supply Chain
    Optimize and automate supply chain processes.
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    Software for Robotics
    Turn robots into smart robots with artificial intelligence.
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    Inventory Forecasting
    Accurately predict future inventory levels and requirements.
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    Computer Vision
    Gather complex sets of data from visual environments.
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    Recommendation Engines
    Predict and streamline the search experience of your users.
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    Predictive Monitoring
    Detect, raise flags, and prevent issues with smart monitoring.
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    Social Intelligence
    Add human-like behavior to augment AI capabilities.
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    Smart IoT
    Unleash the power of the Internet of Things with AI-driven datasets.

Our Process

This is how to transform the idea in your head into a reality


Understanding the Context

First of all, we analyze where your company is standing. The most important part of this stage is to understand the business and data requirements of the project and use them to list quantifiable goals and their consequent outcomes.

Data Engineering

With a clear picture of the context, we can begin collecting all internal and external data that is relevant to the AI implementation. This process implies curating, cleaning, and contextualizing the gathered information to build a comprehensive data lake.

AI Model Development

Now, the fun part. The AI model development starts with a Proof of Concept developed by our expert AI engineers. Our team will define the project scope, tech stack, implementation methodology, software architecture, tools, and quality assurance requirements.

AI Model Deployment

The first working version of the AI model will be deployed following the implementation methodology defined in the previous step. This will be the time to make any stabilization corrections, enhancements, and real-world testing.


After the initial deployment, we can begin to focus on the complete integration of the AI model and start the self-learning and self-improvement process. We provide continuous support on deployed models to guarantee the fulfillment of your project’s goals.

Frequently Asked Questions

Here are some FAQs on Artificial Intelligence and Machine Learning

A lot of people get these two confused, so here is the definitive answer: machine learning is a subset of artificial intelligence. AI involves all developments of technologies created to simulate human behavior. Machine learning is part of this, and what makes it unique is that ML algorithms are designed to automatically learn from past data and carry out actions that have not been programmed explicitly.

That depends on what your company is trying to achieve. Both are great technologies with unlimited potential and tons of use cases but, as established in the previous question, machine learning is a subset of artificial intelligence, so any ML project is also an AI project. By that logic, one could argue that AI is best, as it includes a wider range of technologies and implementations.

Broadly speaking, Machine Learning algorithms can be categorized into 3 types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Here’s a quick look at what each of these means. 

  • Supervised learning happens when we provide the machine with a ton of information about a case and its outcome, and we tell it whenever its results are correct—hence, all of the work done by the machine is supervised. 
  • Unsupervised learning is the opposite, as there is no help from AI engineers and the computer has to learn on its own. Unsupervised learning is extremely useful to recognize patterns in data, find anomalies, cluster problems, and help us make decisions. 
  • Reinforcement learning is probably the closest to how we as humans learn. In this case, the algorithm or the agent learns continually from its environment by interacting with it and gets a positive or a negative reward based on its action.

AI and Machine Learning are important to businesses because they are redefining the ways we all do business. Making use of these technologies has the potential to completely change how your company operates and how it relates to the customers, by harnessing the true power of data collection, data processing, automation, and all of the resulting insights. At this time, using AI and other cutting-edge technologies is defining which businesses become market leaders—and which are trying to catch up.

Hiring Guide

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    Hiring Guide

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    Interview Questions

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    Job Description

What is Machine Learning?

This technology falls under the blanket of Artificial Intelligence and serves as a data analysis method used to automate analytical model building.

The idea behind Machine Learning is that systems can learn from data, identify patterns, and make decisions without having to rely (too much) on human interaction. One of the most obvious examples of machine learning is the autonomous vehicle. Automotive manufacturers (such as Tesla) are spending billions of dollars to finally achieve the goal of the self-driving car.

But this isn't an easy challenge. Because we're talking about automobiles, these industries must pass vigorous safety inspections and tests, as well as get beyond current restrictions in state, federal, and global laws. On top of that, they'll need to have the infrastructure in place to make self-driving cars happen. 

But before those manufacturers can pull that off, they need to actually achieve the goal of the truly autonomous vehicle. On top of that, those manufacturers must be able to answer the question, "Why do we need autonomous vehicles?" One of the biggest answers the industry has is lowered costs. Autonomous delivery vehicles would drastically cut down the price of shipping products. 

To achieve this, machine learning must be applied successfully. Machine learning makes it possible for those manufacturers to achieve higher levels of driver assistance, including how the vehicle perceives the world around it. For a vehicle to become autonomous, it must be able to recognize things like obstacles, traffic patterns, speed limits, routes, and even weather patterns. For that, a vehicle employs numerous sensors around the vehicle, which collect data to be interpreted by the onboard system. 

This gets problematic if the machine learning algorithm mistakenly interprets a stop sign for a yield sign, which could lead to catastrophe. This is why machine learning is not only crucial but challenging.

How Does Machine Learning Work?

Machine learning algorithms find patterns in data that generate specific insight for a particular system. Take our autonomous vehicle example from above. The machine learning algorithm must be able to tell the difference between the various signage found on streets. Without the ability to recognize those signs, a car wouldn't know the difference between a stop sign, a speed limit sign, or a sign indicating a turn.

The issue is way more complicated, mainly because machine learning isn't just applied to self-driving cars, but to medical diagnosis, stock trading, energy forecasting, weather predictions, market trends, speech translation, face recognition, sentiment analysis, social media analysis, fraud detection, and product recommendation, among many other things.

Machine learning uses 2 different techniques for the learning process:

  • Supervised learning - builds a model based on a known set of data and known responses to the data and trains a model to represent reasonable predictions in response to new data. Examples of supervised learning include classification techniques and regression techniques. You would use this option if you need to train a model to make a prediction.
  • Unsupervised learning - finds hidden patterns and structures from datasets without labeled responses. The most popular type of unsupervised learning is clustering. You would use this option if you need to explore data and split it into clusters of information.

Most Popular Programming Languages For Machine Learning

Once you've decided the type of machine learning technique you want to use, the next step is to select the right programming language to build your systems. Keep in mind, though, that not every language is useful for machine learning. 

The top 10 most popular programming languages that can be applied to machine learning include:


It's not a matter of if, but when your company will need to add machine learning into your systems. Before you get too far behind this curve, start planning for the eventuality, so you can catch up to the competition. To do that, you're going to need to hire the right people.

What is a neural network?

A neural network is a simplified model of the human brain. This network is a combination of algorithms that aim to identify patterns in data sets through a process that mimics how our brain operates.

What is Naive in the Naive Bayes method?

Instead of the standard Bayes theorem:

P(yi | x1,..., xn) =P(yi)P(x1,..., xn | yi)(P(x1,..., xn)

Naive Bayes uses the naive conditional independence assumption that each xiis independent and simplifies this relationship to:

P(xi | yi, x1, ..., xi-1, xi+1, ...., xn) = P(xi | yi)

What is PCA?

PCA stands for Principal Component Analysis and is used for dimensional reduction.

What is the SVM algorithm?

The Support Vector Machine algorithm is a supervised machine learning model that is capable of performing linear or non-linear classification, regression, and outlier detection.

What is cross-validation?

Cross-validation is a method of splitting data into 3 parts: training, testing, and validation.

What is bias?

Bias informs there's an inconsistency in data that's usually the result of the algorithm being in favor or against a particular view, perspective, or idea. Basically, bias appears because the learning model uses wrong assumptions that are then replicated in how the algorithm itself operates.

What is the difference between classification and regression?

Classification is used to produce discrete results and classify data into specific categories. Regression deals with continuous data.

What are precision and recall?

  • Precision answers the question, "Of every item a classifier predicted to be relevant, which ones are actually relevant?"
  • Recall answers the question, "Of every item that's relevant, how many were found by the classifier?"

What is collaborative and content-based filtering?

Collaborative filtering is a type of recommendation system that predicts new content by matching an individual's interests. Content-based filtering is focused on the preferences of a user.

We are looking for an expert in machine learning to help extract value from our data. You will be in charge of this process from collecting, cleaning, and preprocessing data to training models and deploying them to production. The ideal candidate will be passionate about artificial intelligence and stay up to date with the latest developments in the field.


  • Understand business objectives and develop models that help to achieve them, along with metrics to track their progress
  • Managing all resources such as hardware, data, and personnel to meet deadlines
  • Analyze ML algorithms to solve a given problem and rank them by their success probability
  • Explore and visualize data and identify differences in data distribution that could affect performance when deploying the model
  • Verifying data quality, and/or ensuring it via data cleaning
  • Supervising the data acquisition process if more data is needed
  • Finding available datasets online that could be used for training
  • Defining validation strategies
  • Defining the preprocessing or feature engineering to be done on a given dataset
  • Defining data augmentation pipelines
  • Training models and tuning their hyperparameters
  • Analyzing the errors of the model and designing strategies to overcome them
  • Deploying models to production

Skills and Qualifications

  • Proficiency with a deep learning framework such as TensorFlow
  • Proficiency with Python, Java, JavaScript, and the basic libraries used for machine learning
  • Expertise in visualizing and manipulating large datasets
  • Proficiency with OpenCV
  • Familiarity with Linux
  • Ability to select hardware to run an ML model with the required latency
  • Fundamental understanding of version control systems (such as Git)
  • Solid problem-solving skills
  • Excellent written and verbal communication
  • Good organizational skills
  • Ability to work as part of a team
  • Attention to detail
  • Understanding the nature of asynchronous programming and its quirks and workarounds
  • A positive attitude

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