In an AI project's lifecycle, about 80% of the time is devoted to gathering, organizing, and annotating data; and with the rise of a data-centric approach to AI, this number is only increasing. We here at Biz-Tech Analytics understand that your time is valuable and scarce. Our team is here to annotate and label your data, so you can focus on the hard parts of your AI project.
We provide accurate and scalable, human-in-the-loop text and image data annotation solutions, at a rapid pace, to help you iterate faster and stay one step ahead of the curve. We understand that our customers are working on revolutionary ideas, which makes their data-related needs unique, so we don't try to sell you a cookie-cutter solution, but rather tailor our solutions to your needs.
Our goal is to create value for the teams that we work with by providing highly accurate and cost-effective labeled data, to help them build and enhance cutting-edge AI/ML applications that will have disruptive impacts on their fields.
We have successfully set up partnerships with data annotation and generation companies all over the globe.
Together with our partners, we have successfully completed projects in the technology, retail, manufacturing, surveillance, travel and tourism industries. These projects include image annotation, classification & segmentation; text transcription & generation; and video classification & annotation work.
You connect with our experts so we can learn more about your data-related needs, and you can learn more about our process.
You provide some samples to our team with instructions, which we will annotate and send back to you, for your approval. This step is absolutely free and is to help us understand your needs, while you get acquainted with our process and our work.
We create workflows, end-to-end execution plans, select annotation tools based on the type of annotation required, and assign potential labelers and managers.
After you are satisfied with the annotations for your samples, our process, and the execution plan, we can discuss and finalize the project terms and get the real work started!
Securely share your data with our team in the format of your choice. We will deliver your labeled data to you, in the format of your choice, according to your timeline.
We iteratively collect feedback from you to improve the quality and accuracy of your labeled data. We also provide operational transparency and requested metrics with regard to quality control.
We provide both a Fully-Managed Data Annotation Service and Data Annotation Resources to work alongside your in-house team.
We have a range of payment models designed to suit your annotation needs - big or small. You can choose from one of our payment models listed below.
We recommend this for teams looking to add data annotation resources to work alongside their existing team and for bigger projects. You can pay hourly, weekly, or monthly, depending on your needs!
Pay per instance of data or per annotation. This works best for teams that have smaller datasets or just want to try us out.
This works best for teams that have a pre-defined dataset and are willing to commit to working with our team for the full project. This payment model allows us to offer discounts depending on the volume of the dataset.
All projects start with a free trial, during which you provide some samples to our team with instructions, which we will annotate and send back to you for your approval. Only once you have approved the sample annotations and we have refined the instruction set, do you start utilizing your paid plan.
With the rise of the Data-Centric approach to AI/ML in the last year, high-quality labeled data is more important than ever. Improving the quality of labels on a dataset has shown unparalleled performance gains with the same model in a variety of use-cases.
Here at Biz-Tech Analytics, we ensure the quality of your labeled data is top-tier, and offer guaranteed precision and recall percentages, as per your requirements. Our team has processes and safeguards in place to ensure your data is labeled consistently and accurately. In addition to our own Quality Control measures, we are agile and flexible enough to incorporate your inputs into our workflow.
In the beginning, we ask you to submit a small collection of samples with instructions, which are annotated by us and approved by you with a degree of back and forth. Annotation instructions that we get from you are further refined based on this exchange and the sample annotations are included as a part of the instructions.
We have 2 labelers that annotate each instance of data. Both annotation outputs are compared in an automated way and the disagreements are reviewed manually by a reviewer in a visual way.
In case of a disagreement, feedback is given to both annotators. Instructions are also revised and labeling standards are further defined with examples of edge cases and other confusing examples.
At the time of submission of a set of labeled data, a project manager randomly selects a certain percentage of the labeled data for sampling and makes any final changes to this data. Depending on the precision and recall rate the whole cycle may start again.
Additional long-term analytics on the labels is done to assess the strength and weaknesses of the labelers and reviewers to guide management and training decisions.