Fully-managed, end-to-end data annotation services with veteran and trained data annotators.
Acme AI works with models surrounding computer vision, natural language processing, and automatic speech recognition models in construction, banking, national security, healthcare, transportation, retail, manufacturing, acoustic healthcare, consumer electronics, agriculture, robotics, etc.
All of our projects have dedicated project and customer support for smoothing out experience and communication.
Introductory conversation with a client to understand brief and gather requirements for the data annotation project.
Develop initial annotation guidelines and setup project. Additional personnel training ascertained if the assignment calls for it.
Mobilise a demo production run, share questions with the client for clarification and fine-tune data quality parameters.
Setup project based on approved workflow in addition to creating and assigning tasks for annotators.
Delve into the annotation work and complete labeling on dataset provided for the project, either in batches or collectively.
Operationalise a single-pass or multi-pass quality check process in parallel to production to isolate inconsistencies.
Final quality assurance check to confirm 95% accuracy with project files handed over to the client as a final delivery.
Data razed 10 days after delivery or earlier based on client requirement and our data protection policies.
Tools we frequently use.
Accurate labeling is a critical part for any algorithm to perform as intended. We strive to provide pixel-accurate datasets within cost-effective packages for our clients across different industries. Following are some example of annotations done on different industries.
Geospatial detection use-cases are one of the key avenues where AI is breaking barriers. Iterations of object detection from EOS multispectral and radar imagery can be many, inclusive of land use and land cover, vegetation mapping, vehicle detection and types, oceanic vessels, urban planning, and even detecting pollution spread trajectories.
Popular Labelling Forms.
Bounding Box
Polygon/Polyline
Classification
We refer to transportation as a progenitor space for machine learning - revolving around AI iterations of autonomous vehicles, fleet management, number plate detection, smart accessories, traffic management and road infrastructure management.
Popular Labelling Forms.
Semantic/Panoptic Segmentation
Polygon/Polyline
Video/Sequence Annotation
3D Cuboid in LiDAR/ Bounding Boxes
Automation in manufacturing and industrial production lines are increasingly being explored, ranging from defect detection in consumer goods to developing computer vision systems that guide manufacturing automatons. Retail and warehousing work in a similar context with real-time feedback on customer experience or clusters, and predicting stock surplus or shortages to name a few.
Popular Labelling Forms.
Bounding Box
Polygon/Polyline
Classification
Optical Character Recognition
Document processing is a big part of banking and insurance sectors which can often become an arduous endeavor. Thankfully, NLP and computer vision can change the game and we are here to make sure that they do it right.
Popular Labelling Forms.
Optical Character Recognition
Classification
Precision medicine, diagnosis automation, remote surgeries, treatment modelling, prescription analysis and emergency medicine deliveries are just a few of the enormous wealth of medical innovations currently in the process of development and scaling in the world. Annotation on DICOM or multi-layered TIFF images paired with robust deep learning algorithms is expected to make the next generation of medical AI systems.
Popular Labelling Forms.
DICOM Annotation
Polygon/Polyline
Audio Transcription
Do a pilot before starting the project for checking quality and accuracy parameters and bottlenecks.
Detailed task guidelines for resources to minimize roadblocks once work commences.
Provide a project manager who takes ownership of the work and can be reached at any time.
Single-pass and multi-pass QA, ensuring global standard quality control prior to releasing data.
We provide fully managed annotation services. Our skilled annotators are rigorously trained and our team leads have extensive experience in managing image annotation workflows and coordinating with clients. The perks mentioned here are services we provide which are included in our standard pricing model.
All of our workforce are certified in CDC/NCHS Confidentiality Training to PIIs. This is critical for medical data and is also interoperable to banking and retail use-cases.
Our operational leaders are certified in Total Data Quality from the University of Michigan - adopting best practices to assimilate into client's use-cases and inform on possible data and bias issues.
faster work per
hour by our certified annotators
Acme AI only employ workers who pass our rigorous certificate training programme. In comparison to the average labeller, certified annotators work faster - translating to hourly cost savings.
certified workers
What started out as a team of four people in 2020 scaled to a taskforce of over 270 people in a span of two years. Our workforce grows by at least 7% every month.
on-demand workforce scaling potential
Based on requirement and business value, we can scale our on-prem and remote workforce by upto 30% of its current capacity. Examples include increasing workforce by 13% in a week's notice.
Get high-quality and pixel-accurate labelled data through us. We bring the best-in-class, scalable, and adaptive annotation and quality assurance services. Begin the final trek towards fine-tuning your supervised learning model with us.
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