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Machine learning solutions to optimize and automate your workflows
What sets Fresh apart from other machine learning consulting companies is that our staff of designers, developers, data scientists, and engineers analyze your use case AND build whatever product you need. Our full-service machine learning development approach includes generative AI services, AI software development, ML-powered cloud architecture, ML to optimize customer experience, ML algorithms for autonomous mobile robots, and much more.
We have a proven track record of helping clients with every aspect of creating a machine learning solution, from ideation to delivery. Whether your use case involves predictive analytics, computer vision, deep learning models, information extraction, image recognition, inventory automation, or something else, we create machine learning solutions that deliver value.
End-to-end machine learning services
Understand the underlying problem: Define your goals, outline business processes, consider platforms and tools, and discuss optimal hardware and embedded systems.
Explore and analyze your data: Research current pre-trained ML models, deep learning models, and machine learning algorithms for data collection, data mining, historical data, and other data sources.
Conduct data preparation and modeling: Select your platform, construct data pipelines, and strategize for storage and integration needs.
Complete ML model development: Design, build, test, and implement your ML model.
Get support with on-going maintenance: Receive guidance on scaling your machine learning project with continued support from the Fresh team as needed.
Fresh ML expertise: Brancher.ai
Fresh’s proprietary Enterprise tool, Brancher.ai, facilitates the connection of AI models to develop cutting-edge applications. With its intuitive no-code drag-and-drop interface, you can effortlessly create generative AI apps for your company and team.
Harness the potential of generative AI: Brancher.ai helps users swiftly create refined and distinctive applications that cater to diverse use cases without being data scientists
No coding proficiency required: Brancher.ai provides a comprehensive set of tools and features that allow you to develop robust generative AI apps effortlessly, without a background in data science or creating sophisticated ML models
Versatile integration with diverse AI platforms and tools: This generative AI platform seamlessly enables the connection and integration of AI models, empowering you to develop AI and ML applications.
Robust safeguards and data privacy: While venturing into generative AI, security remains paramount. Brancher.ai diligently protects your data and your applications.
Extensive user assistance: Brancher.ai provides a wealth of resources, including comprehensive documentation, step-by-step tutorials, and direct support from the Fresh team.
Fresh ML expertise: Project Bonsai & Moab
Fresh developers and engineers have deep experience working with Microsoft’s Project Bonsai solution, which enables engineers without a background in data science to apply their subject matter expertise, create ML models, and accelerate the software development of intelligent control systems in the real world. We partnered with Microsoft to design Moab: a ball-balancing robot that showcases the power of this AI solution and equips engineers with a tool to experiment with Bonsai’s power and understand its impact on the fields of AI and Machine Learning.
Creating a robot powered by Bonsai required Fresh designers, developers, and engineers to combine ML models, computer vision, data science, artificial intelligence, and hardware design into one solution. We’re happy to extend that expertise to future clients looking for a novel solution to their unique use case or seeking to understand how an actionable machine learning strategy could benefit their company.
Our primary machine learning services
+ Healthcare: Our ML models can diagnose diseases, predict patient outcomes, and personalize treatment plans.
+ Retail and E-commerce: We develop ML models to enable personalized recommendations, assist with pricing strategy, consumer demand forecasting, and supply chain optimization and automation.
+ Finance: Our engineers create ML models to detect fraud, perform credit scoring, and conduct an automated risk assessment.
+ Oil & Gase, Energy, and Utilities: The ML models we create can be used to optimize consumer and corporate energy consumption, predict potential equipment failures, and improve grid management.
+ Retail and E-commerce: Fresh-developed ML models enable personalized recommendations and assist with pricing strategy, consumer demand forecasting, and supply chain optimization/automation.
+ Manufacturing and Supply Chain: Our ML models help organizations optimize production processes, predict equipment failures, analyze sensor data, identify and eliminate bottlenecks, and enhance supply chain efficiency.
+ Agriculture: We build ML models for crop monitoring, yield prediction, and disease detection.
+ Transportation and Logistics: We build ML models to optimize route planning, predict traffic patterns, manage fleets, and forecast infrastructural demands.
+ Marketing and Advertising: Fresh-developed ML models help organizations to perform more targeted advertising, conduct detailed sentiment analysis, and automate customer segmentation.
Our algorithms leverage ML techniques and data science tools to extract patterns, trends, and correlations from data. This enables our clients and partners to make data-driven decisions and gain a competitive edge.
But creating impactful BI ML algorithms is a challenge that involves data collection and preprocessing, feature engineering, model selection and training (regression, decision trees, random forests, and neural networks), model evaluation and validation, and deployment and monitoring.
The practical value, however, speaks for itself:
+ Data-driven decision-making: We can help you make informed decisions based on data insights rather than intuition or guesswork.
+ Improved operational efficiency: Our ML algorithms help companies identify inefficiencies, bottlenecks, or anomalies in their business processes and take action to optimize operations, reduce costs, anticipate equipment failures, conduct proactive maintenance, and minimize downtime.
+ Enhanced customer experiences: Fresh-developed BI ML algorithms can analyze your customers' behaviors, preferences, and interactions to provide personalized recommendations, improve marketing strategies, and tailor products to individual customer needs.
+ Risk management and fraud detection: Our ML algorithms can identify patterns and anomalies indicative of potential risks or fraudulent activities, enabling companies across various industries to take action and protect their assets and reputation.
We use ML technology to manage the vast amounts of data that organizations like yours collect, store, and generate. Analyze and extract actionable insights, discover hidden patterns, identify trends, and distinguish correlations.
From data preprocessing to predictive analytics, pattern recognition, personalization, and recommendation systems, we help you enhance user experiences, increase customer engagement, detect fraud, perform natural language processing (NLP), and more.
The ML models developed by Fresh learn patterns and structures within large data sets. This allows the models to perform information extraction and make meaningful predictions. Machine learning algorithms leverage deep learning models, including neural networks (RNNs) and transformers. These models are used in NLP tasks such as language translation, sentiment analysis, text summarization, and question-answering.
The result for Fresh's clients is that through machine learning, NLP models can learn language semantics, syntax, and context to perform various tasks, including sentiment analysis, entity recognition, language generation, and more.
+ Analyzing and understanding the steps involved in a given task
+ Replicating human actions, allowing software robots to perform tasks accurately and efficiently
+ Performing data extraction and information extraction from unstructured data sources (invoices, emails, or documents)
+ Automating cognitive processes through reinforcement learning, analyzing situations, evaluating options, and choosing (or recommending) the best path forward
+ Conducting process mining or anomaly detection, identifying bottlenecks and inefficiencies, optimizing processes, reducing errors, and driving continuous improvement in automation workflows.
For clients across a range of industries, Fresh's ML-powered RPA systems present the potential to streamline operations, reduce unnecessary expenses, and focus human resources on higher-value, higher-interest activities.
+ Object detection and recognition. Our ML models, including convolutional neural networks (CNNs), detect and recognize "objects" within images or videos. This is useful for the creation of autonomous vehicles and biometric recognition.
+ Object tracking. Our ML models can track and follow object motion patterns and appearances. Predicting the position of objects over time can be applied in biometric recognition, robotics, and sports use cases.
+ Image generation and data synthesis. The ML models our team creates can generate new images based on learned patterns and styles. Diffusion models that leverage generative adversarial networks (GANs) and variational autoencoders (VAEs) process training images to produce new, realistic variations. Generative AI is breaking new, innovative ground across the spectrum.
+ Image classification and categorization. Our models can be used to classify images into different predefined categories. This gives companies in industries like healthcare, e-commerce, customer experience, and manufacturing the power of high-quality image search, content filtering, and visual quality control.
+ Pose estimation. Fresh-developed ML models can estimate the 3D pose or position of objects or human bodies in images or videos. Understanding and interpreting the spatial relationships between objects is useful in motion capture, VR, AR, and biometrics.
+ Identifying your competitive advantage. Machine learning strategy enables companies to identify unique opportunities for machine learning applications. By implementing machine learning effectively, you can act on opportunities for competitive differentiation.
+ ML Goal alignment. Our team will help you build a well-defined machine learning strategy aligned with your goals and objectives.
+ ML risk management. Machine learning initiatives carry inherent risks. Data privacy, algorithm bias, and low accessibility or interpretability for users are vital to consider. An effective machine learning strategy enables you to identify and address risks proactively.
+ ML resource allocation. Machine learning requires a significant investment of time, data, technology, and talent. Developing a concrete ML strategy will help you assign your resources in a practical, scalable way.
+ Data acquisition and quality. Machine learning necessitates high-quality data. A machine learning strategy helps companies assess data needs, identify data sources, and establish processes to acquire, store, and manage data.
+ Building the right team. Implementing and managing your machine learning initiatives is a serious undertaking. Developing a machine learning strategy allows you to identify the skills and expertise you need. Additionally, you'll be able to create a plan for upskillling existing employees to work on ML-related projects.
Expertise in best-in-class machine learning technologies
A key Fresh differentiator when compared to other machine learning companies is that we’re technology and platform agnostic. Some ML solutions share the same tech stack, but each challenge is unique, and we never take a one-size-fits-all approach. Explore the technology we use. We’ll help you identify the best combination during our engagement.
TensorFlow's ecosystem of tools allows engineers to build, deploy, and scale various machine learning-based applications like computer vision, object detection, language modeling, and voice.
Microsoft Project Bonsai
Bonsai helps engineers scale their industry expertise while confidently deploying and managing explainable solutions, from wind turbine optimization to continuous machine calibration.
AWS IoT Greengrass
AWS IoT Greengrass allows engineers to run ML inference capabilities on connected devices securely. Greengrass also extends AWS to edge devices to act locally on the data they generate while still leveraging the cloud.
The AWS platform scales dynamically based on each system’s needs, offering computing, storage, analytics, infrastructure as a service.
Azure IoT Edge allows businesses to move cloud-based workloads to their network's edge. Fresh developers and engineers can help you deploy artificial intelligence, Azure, and third-party services into IoT.
Microsoft Azure is a powerful cloud infrastructure solution that provides a quick and effective way to deploy solutions across various languages, including .Net, PHP, Java, Node.js, and Python.
Related software capabilities
Explore our other development services, which allow our team to solve challenges for your organization and others, regardless of size, industry vertical, or product category.