Use Cases

We deliver high-quality training datasets with integrated and secure data pipelines. Proven experience with AI Applications across multiple industries


Transcription for Health Care

Speech recognition technology in health tech allows efficient and accurate documentation, reducing administrative burdens for healthcare professionals while enhancing patient care through real-time updates and decision support. Other examples of use cases speech recognition for Health Care Industry are:

  • Classification of health-related diagnoses in different languages 
  • Integration with EHR systems
  • Clinical decision support
  • Telemedicine and remote care
  • Dictation & transcription of doctor-patient conversations & diagnoses


Nottingham Business School

Nottingham Business School empowers business professionals and students with cutting-edge education and skills by focusing on excellence and innovation.

Enlabeler in partnership with collaborated with Nottingham Business School on a project transcribing interviews consultants had with victims of gender based violence. The interviews were conducted in multiple interchanging languages between English, Afrikaans and other African languages like isiZulu and Venda. Our humans-in-the-loop solution brought together the right experts and processes to efficiently handle the transcription and classification of sensitive content. 

Our language experts accurately transcribed instances where victims switched languages during emotional moments, capturing the full depth of the survivors’ experiences. With a deep understanding of language nuances we were able to obtain comprehensive and insightful information that surpassed expectations.

“The generation of speech data was handled with great care and professionalism. It allowed us to get much more out of the project than anticipated.”

Mollie Painter (PhD)
Professor of Ethics and Organisation


Financial Services

The application of machine learning and computer vision for the financial services industry can greatly improve the speed, accuracy and efficiency of services to consumers. Combining the use of entity extraction and optical character recognition, large volumes of documents can be classified to streamline the automation of KYC processes or applications for loans and credit. Identifying spending habits of consumers will allow machine learning models to provide more accurate credit scores to better assess the risk profile of consumers and even identify fraud transactions.

Other applications that could be used for financial services are:

  • Regulation Compliance
  • Contract and documents management 
  • Claims management

Fintech example


A Financial Comparison Service Provider

Our client, a financial comparison service provider, required an automated method to review large amounts of transactional statements to provide quick responses to their customers for evaluating credit scores and eligibility for various credit applications (e.g. personal loans, credit cards etc.). The increase in speed of response would result in a higher uptake of their services and reduce the resources required to manually review documentation.

Enlabeler collaborated with CapeAI, who was responsible for building the machine learning model that would automate the evaluation of the financial statements. Enlabeler provided the training dataset from 40,000 transactions, the data classification required identifying the transaction type and its source category. A team of 8 labelers with a background in finance or accounting was employed to complete the project in a short time span of 2.5 weeks. 

8 domain experts with accounting or economics background

Categorize 40,000+ transactions

Completed in 2.5 weeks


Health Care

The health care industry can leverage machine learning to better serve their patients by  using it to identify and diagnose patients, support medical research and development, run system analyses and so much more. The right data annotation partner can handle medical imagery and videos securely, accurately extract and annotate large amounts of high quality training data for robust ML models – choose Enlabeler. Other examples of use cases in the health-tech space are:

  • Medical image labeling such as SCANs, CTs and MRIs 
  • Point annotation for monitoring better poses for performance of sports players
  • Counting of cells and tissues for defect detection 
  • Developing and mappings for natural language to medical terms


Decoded Health

Decoded Health is developing a Clinical Hyper-automation Platform which will allow doctors and physicians to reach and help more patients. They have high volumes of unstructured medical data that require high quality label classification to feed their AI-driven solution. Decoded Health also wanted to partner with an organization that aligns with their values of making a social impact.

Enlabeler’s humans-in-the-loop solution brought together the right people and processes to efficiently create the training data. Medically trained and qualified individuals were sourced to project manage, identify and classify medical text as well as provide quality control. 

The partnership between Decoded Health and Enlabeler is an innovative way to improve doctor-patient relationship and create more opportunities in the medical technology field.

5 medical professionals providing classifications

High volumes 14200 classifications completed since March 2022 (on going) 

8 successful projects passed

Assisted in helping to cover 90% of patient diagnosis to accelerate their platform deployment 

“Enlabeler has been a key partner in helping Decoded Health execute its vision for health care. With quality and responsiveness, Enlabeler has speedily supplied us with a trove of labeled health data that powers our platform’s inferential capabilities, enabling physicians to help more patients and touch more lives.”

Brent K Sugimoto
Chief Medical Officer


Media & Entertainment

Transcription and translation used for subtitling in movies and TV productions helps reach a wider audience. Meaningful stories can be told to an audience in their native language, spreading the message and enjoyment of these media productions. There is also the added benefit of SEO making these videos more searchable if published online. Other examples that the media and entertainment industry can benefit are:

  • Brand and customer sentiment analysis 
  • Virtual chatbot with multilingual capacity 
  • Content recommendation and creation 
  • Chaptering for YouTube videos


A Leading Entertainment Company

A popular South African telenovela TV show wanted to extend its reach into the African-Portuguese and Swahili speaking market . Viewership from these communities are limited due to lack of professional resources and infrastructure to do subtitling. The client needed a feasible solution for creating a framework to translate and transcribe English into various African languages dialects that connect with its audience

Enlabeler offered a fully managed team of translators and transcribers that specialized in these under-resourced African languages. The team provided translation and transcription in multiple African languages and dialects that was specific to the region the TV show was to be aired. Quality control was essential to ensure that the right dialect was used to engage and tell a compelling story to the viewers. 

450 hours of video content was processed

3 native African languages were used across translations and transcriptions


Energy and Conservation

As our energy needs as a species expands so does our need for control, monitoring and reporting on the state of our planet. Cataloging and annotating data from multiple geographical data sources help improve predictions of specialized geological cases. Combined further with human energy consumption data, predictive energy models can optimize our energy efficiency whilst maintaining sustainability of the planet.

Other examples that infrastructure can benefit are: 

  • Cloud detection for better weather forecasting
  • Monitoring bodies of water to predict potential drought
  • Conservation of wild life, forestry and water sources


International Non-profit Environmental Organisation

Our client is on a mission to clean up the ocean by developing scalable technology to make these efforts more efficient. They required a hands-on outsourced team that could be activated timeously to annotate large volumes of images. The data output would assist their machine learning model to locate polluted areas and deploy cleanup solutions efficiently.

Enlabeler assembled a team of 20 annotators that could be easily activated to provide a quick turnaround. The team diligently reviewed 6000 aerial and crowd sourced mobile images of the ocean, coastline and rivers to identify waste. The output of the data had an 89% accuracy rate on the model and allowed the deployment of cleanups at 5 different locations.

Total of 6000 images reviewed

140k of annotations 

89% accuracy on the model 

20 jobs created 



The use of machine learning for infrastructure makes planning, constructing and maintaining the assets more feasible and mitigates potential risk. Annotated datasets from aerial images of existing landmarks to identify physical objects such as trees, electricity poles and transmission lines can aid in automated inspections and monitor safety.  Other examples that infrastructure can benefit are: 

  • Traffic surveillance 
  • Infrastructure planning and management 
  • Inspection and safety monitoring of equipment 
  • Predictive maintenance



The future of road infrastructure is moving towards digitalisation for better management and maintenance. Fugro required large volumes of data to be processed with labeling assets on the road networks – something that was not developed in the region yet. 

Enlabeler was able to process the high volume in a short time span. A dedicated project manager worked closely with the Fugro and labeling team made sure that the requirements were met to standards and for an efficient delivery. The project was completed with on-premise data and at the best market value rate for a project as specialized as road asset management.

8,300+ images completed in 4 weeks 

200+ images processed daily



The use of data labeling for computer vision models in agriculture can increase produce throughput and workflow efficiency for precision growing. Training datasets to accurately recognise produce or identify plant defects can provide prompt yield estimates to assist with farming operations. Other examples that agritech can benefit are: 

  • Identify underperforming farmland areas
  • Fruit and produce counting
  • Yield and growth estimates
  • Classification of crop health status


Leading South African Agritech Company

Our client, a leading agritech company, had ±381,000 acres of aerial data to process. The client required a local partner to output a high turnaround of labeled and annotated aerial imagery. As part of the client’s ethos to assist local farmers and communities, they wanted to collaborate with an organization that has a mission to make an impact on the local community. 

With Enlabeler’s goal to expand Africa’s digital talent ecosystem, we accessed untapped talent and provided 40 jobs to youth that were unemployed. The team achieved a 95% turnaround time within 24 hours from the initial intake of aerial data. The team was required to accurately detect objects and segments of the produce and agriculture to determine yield and crop estimation.

95% turnaround time within 24hrs

36k acres processed per month

40 jobs created


Agritech for Livestock

An extension of the agritech industry is livestock management where big data can be used to maintain the well being of the animals to optimize growth cycles and resolve day-to-day operational challenges. Supply chain management can also be improved by predicting produce yield and quality for supply and demand forecasting. Other areas where livestock monitoring would be beneficial are:

  • Detection of health anomalies or contagious diseases
  • Alerts for equipment malfunction and health status of animals 
  • Predict behaviors of different animals



Serket is a leader in digital solutions for pig farming to assist with the livestock management process. They wanted to predict the behavior of pigs in the pen by creating a model that looked at the actions and posture of each pig. Serket had adopted a new tool to allow them to input the data and collaborate easier. They had hours of surveillance videos they had recorded without any labeled information. 

Enlabeler’s agnostic approach means that we can focus on the technical and advanced annotations, regardless of the platform. Our team was trained and set up on the client’s tool and could seamlessly follow the guiding principles that outlined their training model. We were able to produce a quick and accurate turnaround with rapid results from the output.

300+ videos equates to 9,000+ images processed

51 300 pigs annotated 

23 unique jobs created 

We scaled 75% more for the second iteration

“At Serket we are building a system to recognise and analyse the behavior of pigs. We always receive good quality data from Enlabeler. They are known for high quality adaptability and excellent communication.”

Eniko Santa
Animal Scientist