Bespoke ML Solutions

Build bespoke computer vision, natural language, classical ML and time-series forecasting models

Machine Learning Solutions For Your Organization

Aya Data offers full-stack machine learning (ML) solutions, from data collection through to training and deployment.

Our team can build bespoke models across the full spectrum of ML, including:

  • Computer vision models.
  • Natural language processing models.
  • Classical and statistical models.

We are well-versed in both classical or statistical machine learning and neural networking.

Computer Vision Models

Aya Data offers a full suite of computer vision (CV) services, from building high-quality datasets to training state-of-the-art models tailored to specific applications and use cases. 

CV services
Computer vision services

We assist with the end-to-end process of creating and deploying custom CV models, utilizing the latest tools combined with our multi-industry expertise and experience. 

How We Can Help

Object Detection

Identify and localize objects within images or video streams.

Image Classification

Categorize images based on their content.

3D Object Detection and Segmentation

Recognize and localize objects in 3D space using sensor data like LIDAR.

Natural Language Processing Models

Text is one of the most valuable and plentiful sources of unstructured data on the planet. Natural language processing (NLP) is the domain of machine learning (ML) tasked with processing and understanding this immense resource. 

CV services
NLP services

Aya Data provide end-to-end solutions for deploying, managing, and scaling NLP models, empowering businesses and organizations to extract valuable insights, leverage their language data, and unlock competitive advantages. 

How We Can Help

Sentiment Analysis

Sentiment analysis utilizes machine learning models such as Naïve Bayes, Support Vector Machines, LSTM, and BERT to discern emotions or opinions in text. This is crucial for applications such as social media monitoring, brand reputation management, and customer feedback analysis.

Chatbots and Virtual Assistants

Chatbots are changing the technology industry, but there is much work must be done to develop reliable, non-prejudice chatbots and AI-driven conversational agents. 

These AIs rely on natural language understanding (NLU), natural language generation (NLG), dialogue management, and text-to-speech synthesis. 


Translation models automatically convert text between languages, with applications in multilingual communication, content localization, and language learning. 

Early statistical machine translation (SMT) approaches have been replaced by neural machine translation (NMT) models, which use deep learning architectures such as RNNs, LSTMs, and attention mechanisms. In addition, transformer models like BERT, OpenAI's GPT, and Google's T5 have further advanced the field. 

Text/document Classification

Text/document classification involves assigning predefined categories or labels to text, with applications including spam filtering, news categorization, sentiment analysis, and medical document classification. 

This process leverages machine learning techniques like Naïve Bayes, SVM, and deep learning models such as CNNs, LSTMs, and Transformer-based models to achieve accurate classification.

Classic ML Solutions

Traditional statistical and machine learning techniques are still widely used in a broad range of industries and sectors. 

classical ML
Classical ML

Classical ML models require less data to train, are faster to implement, computationally inexpensive, and easier to manage. 

How We Can Help

  1. Linear Regression: A simple model used for predicting continuous target variables based on one or more input features. The backbone of classic statistical ML. 
  2. Logistic Regression: Used for binary classification tasks. Works by fitting a logistic function (Sigmoid) to the input features.
  3. Decision Trees: Tree models used for both regression and classification tasks. They split the input features to create a hierarchical structure and prediction system. 
  4. Random Forest: An ensemble model that consists of multiple decision trees, each trained on a random subset of the data. 
  5. Support Vector Machines (SVM): A classification algorithm that seeks to find the optimal hyperplane that separates instances of different classes.
  6. Naïve Bayes: A probabilistic model based on Bayes' theorem, which assumes conditional independence between input features given the class label.
  7. K-Nearest Neighbors (K-NN): A non-parametric model used for classification and regression tasks. 
  8. Principal Component Analysis (PCA): A dimensionality reduction technique used to transform the original features into a lower-dimensional space, while preserving as much variance as possible.

Why Aya Data

Our mission is to deliver exceptional data annotation services and create good jobs in emerging economies. Our recruitment process selects for capability and resourcefulness, not academic credentials. Once a part of the family our staff receive continuous training to help them reach their potential.

Alongside our talent, we differentiate on:


Only way to exceed expectations is to understand them in real-time. Effective communication is a requirement of achieving the best outcomes, fast.


Computer vision companies today need to maintain ultra-tight security protocol and compliance. That’s why we maintain the highest standards of data security and are GDPR and SOC 2 compliant. In addition, for sensitive projects, we provide dedicated high-security Clean Rooms.


Quality is defined by you. Once KPIs are set, we iterate our workflow to deliver the exact results that you need to get the best out of your model. We leverage cutting-edge tools in all computer vision machine learning projects.


Delays cost money. We operate with 20% slack at all times to ensure that you have the data to meet your deadlines.





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