Begin Your Business Transformation:
Custom &
Integrated
AI Solutions ✨
Exclusive, integrated solutions crafted by our experts just for your business. Unlock new possibilities and drive efficiency with our tailored AI Integration Process.
AI Models Typically Built In
To Fine-Tune the most powerful Large Language Models.
Leading brands and innovative startups count on us.
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Schedule your 45-minute AI Workshop session, led by Sonatafy Technology’s lead AI Engineer Dr. Antonio Tamayo. We will take a deep dive into the intricacies of our customized artificial intelligence development and discover practical ways to integrate it into your business operations.
We will discuss actionable use cases, provide real-time demonstrations and code, and together strategize your organization’s current operations and suggested AI solutions.
AI Custom Resource Directory
Healthcare & Life Science ✨
Developed By Sonatafy Technology
AI Technical Breakdown
Rapid Adverse Drug Events (ADEs) Detection
This demo uses Named Entity Recognition (NER) to scan text, highlighting drug names and adverse reactions. By uploading a medical text, the app quickly identifies and points out medicines and their potential side effects, acting like an assistant that instantly finds important details.
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Symptoms To Diagnosis
This demo reads input text, identifies symptoms, and diagnoses one of 22 diseases, including arthritis, typhoid, and diabetes. It uses a large language model trained on over 1000 clinical documents and fine-tuned with BERT, achieving 94% accuracy.
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Abstractive Clinical Document Summarization
Abstractive text summarization creates concise, rephrased summaries of complex medical records. Our Llama LLM model generates clear clinical summaries, capturing critical details in a human-readable format, aiding efficient information sharing and decision-making in healthcare.
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Diseases & Symptoms Detection
Using advanced NER, it swiftly identifies diseases in medical texts, from diabetes to ALS. Simply input your text, and it highlights diseases with precision, aiding research and comprehension. Empower your decisions and advance healthcare confidently with our efficient tool.
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Personal Healthcare Assistant
Our advanced healthcare assistant, built on the Llama LLM, excels in natural language processing. It understands medical terminology, analyzes patient descriptions, and offers diagnoses, tests, and treatment recommendations in a conversational style.
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Electronic Health Record (EHR) Analyzer
Uses advanced NLP, LLMs, and Generative AI to extract and present information from EHRs in PDF format. It quickly processes various EHR formats, accurately parsing medical terminologies, and presents key data in an easy-to-read format with visual aids, enhancing clinical workflow efficiency and decision-making.
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AI Custom Resource Directory
Talent Acquisition & Candidate CV Analysis ✨
Developed By Sonatafy Technology
Candidate CV & Resume Analyzer
This demo is designed for the task of analyzing candidate CVs. CV analysis helps by scanning the document and identifying the most relevant qualifications, experiences, and skills.
So, when you use this demo for analyzing CVs, you’re essentially uploading a CV, and the app quickly sifts through it, determining whether the candidate’s qualifications and experiences match the specific criteria provided.
It’s like having a helpful assistant that can instantly pick out the best candidates from a pile of CVs!
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Invoice
Analyzer ✨
Developed By Sonatafy Technology
Invoice Analyzer Tool
This demo showcases a cutting-edge solution designed to transform complex invoices from unstructured PDF format into structured JSON format. Leveraging the latest advancements in Natural Language Processing (NLP), the demo extracts essential information from invoices, presenting it in a structured and easily digestible format.
The web-based interface is user-friendly, providing an intuitive experience that simplifies the process of converting unstructured data into valuable structured data. Try It Out!
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Advanced Solutions ✨
To Common AI Challenges
Our team specializes in guiding organizations through their AI journey, addressing these challenges head-on. We offer expertise in simplifying complex AI concepts, sourcing top-tier talent, ensuring data readiness, crafting strategic roadmaps, and defining clear success metrics.
Partnering with us means transforming your AI ambitions into achievable, impactful realities. Let us help you navigate the intricacies of AI implementation with confidence and precision, ensuring your investment translates into real-world success.
Complexity and Understanding
We’ve Solved That.
Strategic Alignment
We Have The Experience.
Return on Investment
We Have The Data.
Expertise Availability
We Have The Talent.
Data Quality and Quantity
We’ve Compiled That.
Scalability and Integration
We Have The Solution.
Our Team’s Verified AI Certifications
AWS Fundamentals: Addressing Security Risk
AWS Academy Graduate – AWS Academy Cloud Foundations
AWS Fundamentals: Going Cloud-Native, Coursera
Sentiment Analysis with Deep Learning using BERT, Coursera
Google Cloud Platform Fundamentals: Core Infrastructure, Coursera
Google Cloud Platform Big Data and Machine Learning Fundamentals, Coursera
Deep Learning with TensorFlow, Cognitive Class
Deep Learning Fundamentals, Cognitive Class
Machine Learning with Python, Cognitive Class
Statistics 101, Cognitive Class
Machine Learning
Dimensionality Reduction, Cognitive Class
Data Analysis with Python, Cognitive Class
How Does The Tool Actually Work?
Healthcare Technical Breakdown ✨
Information extraction plays a crucial role in the clinical domain by enabling the automated extraction of relevant medical information from unstructured text sources such as electronic health records (EHRs), clinical notes, research articles, and drug labels. In this context, information extraction techniques facilitate the identification and extraction of key entities, relationships, and events pertaining to patient diagnoses, treatments, medications, adverse reactions, and other clinically relevant information. By automating the extraction process, healthcare providers can streamline clinical workflows, enhance decision-making processes, improve patient care outcomes, and facilitate medical research and analysis on a large scale.
These demos are trained to tackle the problem of information extraction in the medical field as a sequence labeling one, which is a fundamental task in natural language processing (NLP) that involves assigning labels to individual tokens in a sequence of text. To do this task, usually named entity recognition (NER), the BIO (Begin, Inside, Outside) schema has been used to annotate entities such as drugs and adverse effects within clinical text. Under this schema, each token in the input text is labeled with a tag indicating whether it represents the beginning (B), inside (I), or outside (O) of a named entity. This structured labeling scheme allows for the accurate identification and delineation of multi-token entities, which is essential for downstream tasks such as information retrieval, question answering, and clinical decision support systems.
Fine-tuning with the pre-trained language models called BERT (Bidirectional Encoder Representations from Transformers) for named entity recognition (NER) was carried out for these demos. It’s a powerful approach to address the task of identifying drugs mentions and their adverse effects as well as diseases in clinical text. By leveraging large-scale pre-trained language representations, fine-tuning enables the model to learn domain-specific patterns and semantic representations from annotated clinical data. This fine-tuning process involves adjusting the parameters of the pre-trained model on a task-specific dataset, thereby tailoring the model to the nuances of the clinical domain and improving its performance on NER tasks. The ability to accurately identify entities such as drugs and their adverse effects as well as diseases from vast amounts of clinical text is paramount for various applications in healthcare, including pharmacovigilance, drug safety monitoring, adverse event detection, and personalized medicine. Our model offers a scalable and efficient solution to this challenge, enabling healthcare organizations to analyze large volumes of clinical text data and extract actionable insights to support clinical decision-making and improve patient outcomes.
This demonstration showcases a streamlined process wherein a given input text, sourced from the clinical domain such as an Electronic Health Record (EHR), undergoes rapid analysis to identify instances of drug mentions and their associated adverse effects as well as diseases within a matter of seconds. Leveraging advanced natural language processing (NLP) techniques and state-of-the-art models trained specifically for the clinical domain, the system swiftly parses through the input text, recognizing and categorizing drug entities, adverse effects or diseases. By harnessing the power of machine learning algorithms and pre-trained language models fine-tuned for named entity recognition (NER) tasks, these demos exemplify the capability to automate and expedite the extraction of vital medical information from unstructured clinical text. This accelerated process not only enhances the efficiency of clinical data analysis and decision-making but also empowers healthcare professionals with timely insights into medication-related aspects of patient care, ultimately contributing to improved patient outcomes and safety.
Information Extraction
as a Sequence Labeling Task in NLP
Named Entity Recognition (NER)
for Clinical Text
BIO Schema for NER
Begin | Marks the beginning of a named entity.
Inside | Continues the named entity recognition.
Outside | No named entity present.
Input Text Sequence
“Patient takes Aspirin”
Labels: [O, O, B-Drug]
Applications of NER in Healthcare
Information Retrieval | Question-Answer Resource | Clinical Decision Support Systems
How Does The Tool Actually Work?
LLMOps Pipeline ✨
The Large Language Model pipeline involves three steps as follows:
Data preprocessing
To process data from natural language to the BIO scheme we used a custom script in Python. This process takes each entity in the training datasets together with the character where they start and search in order of appearance in the text to assign the corresponding label in each case (Drug, Adverse Effect or Disease). Additionally, we implemented a simple sentence tokenization with the period (“.”) token.
Fine-tuning
The fine-tuning process for both tools was carried out in two steps, as follows: we fine-tuned one of the most powerful Large Language Models (BERT). A virtual machine with a GPU Tesla T4 with 27.3 gigabytes of available RAM was used during the training process.
Post-processing
As a post-processing we treated the subword tokenization inherent to the model used. Additionally, when the models extract two or more entities separately, but they are contiguous, we group them into a unique entity.
Healthcare & The
Benefits Of
Artificial Intelligence ✨
Predictive Analytics
AI can analyze large amounts of data to predict disease outbreaks, identify at-risk patients, and suggest preventive measures. This proactive approach can lead to improved patient outcomes and reduced hospital readmissions.
Drug Development
AI can analyze large quantities of data in a process less prone to error and with no burnout risk.
Personalized Medicine
AI enables personalized treatment plans by analyzing genetic, lifestyle, and environmental data, improving treatment effectiveness and patient satisfaction.
Cost Reduction
AI can automate certain tasks and processes, helping streamline operations and improve efficiency.
Medical Research
AI can accelerate medical research by identifying potential antidotes and treatment options, enabling rapid response to complex global health challenges.
Time Efficiency
AI can improve the efficiency of medical tasks, such as imaging scans, thereby reducing the waiting time.
Medical Image Analysis
AI can reveal images of the internal aspects of a body through a noninvasive process of imaging, which helps in diagnosing and treating disease.
Fraud Detection
AI can analyze financial and claims data, helping healthcare organizations maintain regulatory compliance and allocate resources more efficiently to benefit patient care.
Led By Leaders
Of AI Engineering
We are excited to share our newest AI service offering, which is specifically designed to help clients rapidly accelerate their AI initiatives, enabling them to stay ahead in the highly competitive tech landscape. Whether you’re starting from scratch or looking to enhance your existing AI capabilities, our expert team is here to guide you through every step of the process.
Our AI development team is led by Dr. Antonio Tamayo, who has a Ph.D. in Computer Science and is an AI and Data Scientist leading expert.
Powerfully Engineered.
Committed To Excellence ⚡︎
What sets Sonatafy Technology apart is our commitment to delivering measurable progress. Our approach is hands-on and results-oriented, focusing on creating AI solutions that drive real business outcomes. Whether it’s streamlining operations, enhancing customer experiences, or unlocking new growth opportunities, our goal is to empower your organization to achieve its full AI potential.
If you’re looking to kickstart your AI journey or take your current efforts to the next level, let’s connect. Discover how Sonatafy Technology can help you turn AI ambitions into tangible successes.
Matthew Hensrud
Senior Director of Platform Engineering
“The Sonatafy team has continually impressed us with the quality of their engineers — we have found excellent engineering leaders in their contractors who have helped tremendously. They really are an integral part of our team, and we’re very thankful for Sonatafy’s professional leadership in this space. I heartily recommend them to augment anyone’s teams or projects.”
Chris Maresca
Chief Operating Officer
“We have been using Sonatafy for software team augmentation. Their vetting process is extremely through and has saved us a huge amount of time. All of the candidates presented have been outstanding and have fit into our team perfectly.”
Dave Wattel
Co-Founder
“The Sonatafy team consists of members who are dedicated, personable, and attentive. They will search tirelessly to match the right talent to meet your skills and budgetary requirements. Regardless of your situation, you cannot go wrong with Sonatafy.”
Jason Tuschen
Chief Executive Officer
“The entire team at Sonatafy greatly surpassed our expectations. We require very specific skill sets and the team did an incredible job of screening and selecting top – notch candidates. Sonatafy’s attention to detail, professionalism, open communication, and collaboration with us ensured that we found highly skilled talent that fit seamlessly into our company’s culture.”
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Meet Our Developers
Review real engineer CVs of current and past Sonatafy Technology nearshore developers. We have a wide range of different positions and skills thanks to our talented engineers. Learn More.
AI Custom Resource Directory
Social Media Sentiment Analysis ✨
Developed By Sonatafy Technology
Sentiment Analysis Detection Tool
This demo is trained for the Sentiment Analysis task. Sentiment Analysis helps by scanning the text and identifying the emotional tone as positive, negative, or neutral.
So, when you use this demo for detecting sentiments, you’re essentially uploading a piece of text, and the app quickly sifts through it, determining whether the emotions expressed are positive (like “happy” or “excited”), negative (like “sad” or “angry”), or neutral.
It’s like having a helpful assistant that can instantly pick out the emotional tone from a bunch of text! Try It Out!
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