Artificial Intelligence & Machine Learning Services
We’ve helped established companies like yours explore the possibilities and identify the right approach to save time, reduce costs, and gain a competitive edge.
Tools & frameworks
Build, borrow, or buy?
Best for:
- Complex problems requiring a unique solution, complete control, and valuable IP creation.
Benefits:
- Tailored solution for your specific needs.
- Full control over the model and its future development.
- Potential for significant competitive advantage through unique IP.
Considerations:
- Requires the most time and resources (financial and personnel).
- Needs a strong foundation built on a successful PoC (Proof-of-Concept).
- Requires an ML engineer with domain expertise in your field.
Best for:
- Faster time-to-market with readily available solutions and cost-effectiveness.
Benefits:
- Leverages existing open-source libraries and frameworks.
- Quicker implementation and reduced development costs.
- Potential to start with a partially working solution.
Considerations:
- May not perfectly address your specific needs.
- Legal limitations might apply to certain open-source licenses.
- May require additional work to integrate and customize.
Best for:
- Accessing powerful pre-built solutions and achieving rapid time-to-market.
Benefits:
- Integrates powerful, ready-to-use models from leading providers.
- Fastest path to deployment for specific functionalities.
- Cost-effective solution for accessing advanced capabilities.
Considerations:
- Limited control and customization compared to custom solutions.
- Legal restrictions may apply to specific third-party solutions.
- May not be a suitable option for highly unique problems.
Download our free guide to the must-have open-source tools for AI and ML. Straightforward, powerful, and ready to use.
Artificial Intelligence development solutions
Chatbots
Improve customer service with automated chatbots that answer questions, resolve issues, and improve response times.
We build this using:
Frameworks:
(recommended) TensorFlowKerasPyTorchLangChainThird-party APIs:
OpenAI API
Google AI
huggingface Inference Endpoints
Azure OpenAI serviceOpen-source projects:
Approximate PoC estimate (give estimate):
- Custom development: > 6 month
- Based on open-source: 8–20 weeks
- Based on third-party API: 6–12 weeks
Machine Vision
Automate tasks, improve quality control, and gain valuable insights with advanced computer vision solutions
We build this using:
Frameworks:
(recommended) TensorFlowPyTorchOpenNNYOLOv8Third-party APIs:
AI Removal, Restore, and AnimationOpen-source projects:
- OpenCV is a library for computer vision tasks.
- Detectron2: Facebook AI Research's next-generation software system for object detection.
- Mask R-CNN: Implementation of Mask R-CNN on Python and Keras.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
Sensor Fusion
Turn complex data streams into actionable insights by combining data from various sources.
We build this using:
Frameworks:
(recommended) PyTorchTensorFlowScikit-LearnMXNetOpen-source projects:
- ROS (Robot Operating System): Flexible framework for writing robot software.
- Kalman Filter Library: A set of algorithms for sensor fusion using Kalman filters.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
Voice and Speech Recognition
Improve user experience and automate tasks with voice and speech recognition models, like: NLP, NLG and NLU
We build this using:
Frameworks:
(recommended) TensorFlowPyTorchKerasMicrosoft Cognitive Toolkit (CNTK)Third-party APIs:
ElevenLabs
AWS Amazon
Google CloudOpen-source projects:
- DeepSpeech: An open-source speech-to-text engine using a model trained by Mozilla.
- Kaldi: A toolkit for speech recognition.
- CMU Sphinx is an open-source toolkit for speech recognition.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
Recommendation Systems and AI-enhanced Search
Personalize customer experiences and boost sales with intelligent recommendation systems.
We build this using:
Frameworks:
(recommended) TensorFlowPyTorchScikit-LearnXGBoost
ElasticsearchThird-party APIs:
Open-source projects:
- RecBole: A unified, comprehensive, and flexible recommendation library.
- LightFM A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.
- Surprise: A Python scikit for building and analyzing recommender systems.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
Predictive Analytics and Maintenance
Personalize customer experiences and boost sales with intelligent recommendation systems.
We build this using:
Frameworks:
(recommended) Scikit-LearnTensorFlowXGBoostPyTorchThird-party APIs:
Open-source projects:
- Prophet: Tool for producing high-quality forecasts for time series data.
Approximate PoC estimate (give estimate):
- Custom development: > 3 month
- Based on open-source: 6–12 weeks
- Based on third-party API: 4–10 weeks
Expert Systems
Leverage AI to gain expert-level insights on specific topics.
We build this using:
Frameworks:
(recommended) Scikit-LearnPyTorchTensorFlowOpenNNThird-party APIs:
Open-source projects:
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
IoT applications
Transform your operations with intelligent IoT solutions that collect, analyze, and utilize data from connected devices.
We build this using:
Frameworks:
(recommended) TensorFlowPyTorchKerasMXNetThird-party APIs:
Open-source projects:
- ThingsBoard: Open-source IoT platform for data collection, processing, visualization, and device management.
- Node-RED: Flow-based development tool for visual programming.
- Mainflux: Open-source, patent-free, and industrial IoT platform.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
Cyber Security and Fraud detection tool
Protect your data and assets with advanced AI-powered security solutions.
We build this using:
Frameworks:
(recommended) TensorFlowScikit-LearnPyTorchXGBoostThird-party APIs:
Open-source projects:
- Snort: Open-source network intrusion detection system (NIDS).
- OSSEC: Open-source host-based intrusion detection system (HIDS).
- Suricata: High-performance network IDS, IPS, and network security monitoring engine.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
AI for robotics
Revolutionize your robotics applications with intelligent AI that improves automation and decision-making capabilities
We build this using:
Frameworks:
(recommended) TensorFlowPyTorchKerasThird-party APIs:
Open-source projects:
- ROS (Robot Operating System): Middleware for robotics.
- Gazebo: Open-source 3D robotics simulator.
- MoveIt: Open-source software for robot manipulation, mainly with ROS.
Approximate PoC estimate (give estimate):
- Custom development: > 12 month
- Based on open-source: 16–24 weeks
- Based on third-party API: 6–12 weeks
Schedule a 30-minute consultation with our ML team to discuss your specific needs.
Our validation process
Not every AI/ML project is guaranteed success. We take a data-driven approach to validate your ideas before significant investment, minimizing risks and maximizing ROI.
- Data Collection and Preparation (2-4 weeks)
- Model Selection and Training (3-5 weeks)
- Model Evaluation and Validation (2-3 weeks)
- Deployment and MLOps (3-4 weeks)
- User Testing and Feedback (2-3 weeks)
Data Collection and Preparation
- Gather data from databases, APIs, sensors
- Clean, transform, and organize data
- Ensure data accuracy and consistency
Model Selection and Training
- Choose open-source models, frameworks, or AI APIs
- Train models with preprocessed data
- Optimize model parameters
Model Evaluation and Validation
- Evaluate model performance
- Validate with separate datasets
- Fine-tune if necessary
Deployment and MLOps
- Deploy models in production
- Integrate with existing systems
- Implement MLOps practices
User Testing and Feedback
- Conduct user testing
- Gather and analyze feedback
- Refine and enhance the model
Team composition and responsibilities
Don’t settle for less. Our free guide highlights the top open-source tools in AI and ML.
Generative AI & LLMs solutions
Large Language Models (LLMs) are revolutionizing various industries by automating tasks, improving creativity, and augmenting human capabilities. Here’s how Ralabs can help you leverage LLMs to achieve real-world results:
Boost Team Productivity with AI-powered Assistants:
- Generate reports and documents in seconds
- Answer questions and provide summaries of complex information
- Automate repetitive tasks like scheduling meetings or data entry
Real-World Examples:
Empower Creative Innovation:
- Generate unique product descriptions and marketing copy
- Brainstorm new ideas and develop creative concepts
- Design stunning visuals and layouts, even with limited artistic skills
Real-World Examples:
AI in industries
Why Ralabs
Our partners say
Relevant cases
back to portfolio Website: waythru.com Duration: 3 Weeks (60 Hours) Customer location: South Carolina, US Dedicated team behind the project DanielSolution Architect ElidorSenior ML Engineer
back to portfolio Duration: 2019 Customer location: Palo Alto, US Dedicated team behind the project AndrewSenior Engineer DmytroSenior Data Engineer AndrewFront-end Engineer Previous Next PARTNERSHIP
back to portfolio Duration: 2018 – 2019 Customer location: The US PARTNERSHIP The Client Apri Health, a healthcare provider, was looking for a reliable platform
back to portfolio Website: zerve.ai Duration: 2022 – ongoing Customer location: Ireland Dedicated team behind the project IvanSenior Front-end Engineer Vadym Front-end Engineer Olha Project
Our data engineering solutions
Data engineering is the discipline of designing, building, and maintaining the infrastructure that efficiently processes, stores, and analyzes your data. It transforms raw data into a usable format, enabling you to extract valuable insights for informed decision-making.
Consolidate data from various sources to gain a holistic view of your operations.
Process large datasets efficiently and reliably, ensuring timely access to insights.
Gain valuable knowledge from your data to drive strategic actions and achieve your business goals.
Schedule a consultation with our ML experts to discuss your specific project requirements.
MLOps / DataOps
MLOps is all about reducing the costs of deploying and maintaining ML models while ensuring they're reliable and scalable.
DataOps is a methodology focused on improving the quality and reducing the cycle time of data analytics through better data management and collaboration.
Applications:
- Data pipeline automation
- Data quality monitoring
- Data integration and transformation
- Data lifecycle management
- Continuous data integration
- Collaboration between data engineers, analysts, and business users
- Automated testing and deployment of data updates
DataOps: Concentrates on the end-to-end orchestration and management of data workflows. It aims to ensure data quality, streamline data processing, and foster collaboration among data teams to improve analytics outcomes.
DevOps: Centers on the practices and tools for automating and integrating software development (Dev) and IT operations (Ops). It aims to shorten the development lifecycle, deliver high-quality software continuously, and improve collaboration between development and operations teams.
Your perfect AI/ML partner
At Ralabs, we understand that every business has unique needs. That’s why we offer a variety of engagement models to ensure a perfect fit for your AI/ML project:
Ideal for: Filling specific skill gaps in your existing team or scaling up temporarily for short-term projects.
Benefits:
- Quickly access specialized AI expertise.
- Seamless integration with your in-house team.
- Cost-effective solution for short-term needs.
Ideal for: Ongoing projects requiring a dedicated team of AI/ML developers.
Benefits:
- Increased efficiency and focus on your project.
- Flexibility to adapt the team composition as needed.
- Experienced project manager ensures smooth execution.
Ideal for: Clients seeking a complete end-to-end AI solution without in-house involvement.
Benefits:
- We handle everything, from initial concept to deployment and maintenance.
- Clear project scope and deliverables.
- Reduced workload for your internal team.
Let’s Talk Solutions
FAQ
- Building custom AI models
- Analyzing and preparing data for those models
- Providing the infrastructure to run them
- Offering expert guidance throughout the process
AI and ML development is the process of creating intelligent systems that can learn and improve from data. Here’s a concise breakdown:
AI Development: Focuses on building intelligent systems that can mimic human capabilities like reasoning, problem-solving, and decision-making.
ML Development: Involves creating algorithms that can learn from data without explicit programming. These algorithms can then make predictions or classifications on new data.
Here’s an example of how Machine Learning (ML) is used in Artificial Intelligence (AI):
Self-driving cars: The core technology behind self-driving cars is machine learning. These cars use complex algorithms trained on massive amounts of data (images, sensor readings, etc.) to navigate roads, recognize objects, and make real-time decisions. In this example, AI defines the overall goal of autonomous driving, while ML provides the learning capability to interpret data and react to the environment.
AI (Artificial Intelligence): The broad field of creating intelligent machines that can simulate human capabilities like learning, problem-solving, and decision-making. It encompasses various approaches, including machine learning.
ML (Machine Learning): A specific subfield of AI that focuses on algorithms that can learn from data without explicit programming. These algorithms improve their performance over time as they process more data.
AI/ML: Often used together, referring to the combined application of AI and machine learning techniques to create intelligent systems. AI defines the overall goal and desired outcome, while ML provides the learning engine that allows the system to adapt and improve.
AI is the umbrella term for intelligent systems, and machine learning is a powerful tool used to build some of those systems. They often work together to achieve complex tasks.
The timeline for implementing AI/ML varies depending on project complexity, available resources, data readiness, specific goals, and your chosen AI/ ML development company. It can range from several weeks for simpler projects to months or even years for very complex initiatives.
MLOps: Focuses on the lifecycle of ML models, from development and deployment to monitoring and maintenance. It’s about integrating ML models into production environments efficiently and reliably.
