Artificial intelligence has come a long way since it was first used in generic models. Now it may be used in very specific ways. There are many uses for artificial intelligence, like trading bots, medical diagnosis algorithms, and self-driving cars. However, the data needs for each use are very different. In this case, domain-specific annotation is used.
We use our comprehensive knowledge and scalable annotation processes at Intellisane AI Ltd to provide training data that is both accurate and smart in context. Our work makes it possible for AI models to act and think like experts in certain areas by annotating financial contracts, organizing medical images, and classifying LiDAR frames.
This document explains how to improve value and accuracy in five of the most difficult areas.
AI Use Cases:
Among other things, we analyze tweets and earnings calls to understand people's financial sentiments.
Getting economic indicators from reports
Classifying documents, such as contracts, filings, and prospectuses
Marking transactions to find fraudulent behavior
Our Expertise:
Annotators with backgrounds in finance and economics
We utilize our lexical knowledge to assign names to financial terms, ratios, trends, and other significant elements.
Annotators are responsible for locating and organizing time series data pertaining to fluctuations in stock prices and significant market events.
Making sure that sensitive financial information is handled safely and in accordance with strict Non-Disclosure Agreements (NDA).
Example Project:
Over the course of 4 years, we added notes to 200k financial news stories to help a hedge fund's NLP-driven trading algorithm find emotions, events, and volatility triggers.
AI Use Cases:
AI is being trained in diagnostic models for fields such as dermatology, pathology, and radiology.
Named Entity Recognition (NER) used in electronic health records (EHRs)
Data that comes from medical chatbots and voice assistants
Finding new drugs and spotting bad events
Our Expertise:
Working with licensed doctors and medical students
Pixel-level image segmentation to find tumors and draw the borders of organs
We follow best practices in de-identifying personal health information, tailored to each client’s data handling standards
Example Project:
Semantic segmentation of over 10,000 MRI scans for a neurology startup, including tumor boundary labeling with radiologist-reviewed QA.
AI Use Cases:
Using aerial images to keep an eye on the development of building
Finding workers and equipment on-site
Finding safety violations, like not wearing PPE
Recognizing structural damage and flaws
Our Expertise:
Annotators who know how to read architectural and engineering images like floor plan, drawings, etc.
Polygon and 3D bounding box labels for areas, vehicles, and machines
Labeling images in time series to keep track of development and make project predictions
Combining with video from drones, CCTV, and IoT sensors
Example Project:
Labeled over 200,000 construction site objects for a smart infrastructure platform, identifying cranes, scaffolding, workers, and hazard zones to enhance safety analytics.
AI Use Cases:
AI use cases include the identification of two- and three-dimensional objects in vehicles and aerial drones.
Information gathered by combining sensor technologies like LiDAR, cameras, and radar systems.
Setting up roads and paths for navigation
Modeling how people act and interact, such as how they use gestures and traffic signals.
Our Expertise:
Annotators have learned about LiDAR, cuboid annotation, and panoptic segmentation.
A 3D annotation platform that can grow along with frame interpolation.
Active-learning question-answering pipelines are meant to find unusual edge instances.
Adding tags for weather conditions, changes in illumination, and anything that block the view to make the model more accurate.
Example Project:
Labeled 2M data scenes for a European robotics company, helping improve pedestrian detection in crowded urban environments.
"AI is only as smart as the data it learns from. At Intellisane AI, we provide industry-grade annotation for every field, including banking, healthcare, construction, autonomy, and more. This lets AI analyze the real world with great accuracy".
AI Use Cases:
Audio-visual transcription and emotion tagging
Cross-lingual summarization (e.g., speech to text to summary)
Educational AI and tutoring bots across subjects
Our Expertise:
We now have the capability to integrate various forms of text, graphics, audio, and video seamlessly.
Teams that speak more than 60 languages
We focus on ontology alignment and taxonomy design specifically for datasets containing multiple entities.
Agile teams that can work with hybrid annotation pipelines
Example Project:
The project involved annotating multilingual video lessons with subtitle correction, scene tagging, and voice transcription to support a global EdTech company.
The data that AI uses to learn must be accurate and relevant for it to work well. Without domain-specific annotation, models:
Misinterpret context (e.g., "short" in fashion vs. finance)
Miss edge cases (e.g., rare diseases, contract clauses)
Fail in real-world deployment
Intellisane AI uses both human knowledge and annotation technology. This method makes sure that your models cover all of human knowledge, not just the most obvious patterns.
“From MRI scans to market sentiment, construction footage to autonomous vehicle data—our domain-specific annotation bridges raw data and real-world AI performance.”
From healthcare to autonomous driving, from financial modeling to legal NLP—our teams annotate with precision, care, and profound understanding. With global reach and industry-specific insight, we’re your trusted partner in producing training data that’s production-ready from day one.
Contact us at sales@intellisane.ai to schedule a free consultation or sample project.
