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Our products are used to:

_reduce the uncertainty of Machine Learning models,

_prioritize enterprise data efforts,

_support experts in the ML loop,

_improve the quality of ML models, especially in multi-class settings with complex ontologies,

_reduce data footprint and compactify ML models so as to be used by the Internet of Things applications,

_improve gaming experience via more challenging and realistic AI in games,

_create intelligent advisory systems from pre-compiled building blocks.

product Label in the Loop Data & Domain Expert Support | Meta-AI

LitL automates the process of interactive labeling (tagging, augmenting) data, based on the detection of potentially relevant examples in large data sets and their ergonomic designation by domain experts operating within the organization. 
LitL is intended for use by those organizations that have large and/or rapidly growing data sets, and at the same time need tools based on Artificial Intelligence/Machine Learning that would allow them to more comprehensively capitalize on the knowledge contained in data.


01 Minority Class Detection

LitL methods allow for an improvement in minority class detection

02 Label Noise Analytics

detection of inconsistencies in labelling

03 Error Function Analytics

inspection of Machine Learning models’ erroneous cases and intelligent labelling of error severity 

04 Intelligent Category Maintenance

AI-based assistance in ontology curation

05 Labelled Data Generation

automatically labelling data based on critical data samples selected by LitL for expert review and annotation

06 Data Annotation Optimization

expert is engaged only with those data samples that are most informative for the model

07 Expert Analytics

optimal, AI-based expert-data sample matching

08 Tagging Environment

our tagging environment has been designed for the best experience and ergonomy of data annotation work but LitL can interface with any legacy annotation software.

Project information

LITL – Labeling in the Loop. Development of an automatic and semi-automatic system for marking data in large data sets based on machine learning.

Application number: POIR.01.01.01-00-0213/19
Value of the project: 8 697 250,00 zł
Donation: 6 202 540,00 zł
Beneficiary: QED Software Sp. z o. o.
Project duration: 01.01.2020 – 30.06.2022
Project realised as a part of: Działania 1.1 Poddziałania 1.1.1 Programu Operacyjnego Inteligentny Rozwój 2014-2020 współfinansowanego ze środków Europejskiego Funduszu Rozwoju Regionalnego

Project purpose:

The goal of the project is to develop and test the LITL – Labeling in the Loop system, which will ensure:

1. automation of the process of interactive marking (labeling, tagging, augmentation) of data, based on the detection of potentially relevant examples in large data sets and their ergonomic marking by field experts operating within the organization / enterprise that is the software user (client);
2. continuous self-improvement – the algorithms developed in the project will learn the specifics of user data;
3. intelligent adaptation to changing customer conditions – in the event of a change in the group of experts and thus a change in the configuration and set of competencies in the entire expert group of the client, the algorithms developed in the project will learn how to cooperate with experts in this new group;
4. minimizing the period of implementation of the project result at a new client / in the new domain – the project result will be developed and prepared for commercialization as a modular programming environment, consisting largely of universal modules, embedded within an innovative architecture integrating them with components that usually already exist in the client’s infrastructure.

The result of the project will be intended for use by those organizations and enterprises that have large and / or rapidly growing data sets, and at the same time need tools based on artificial intelligence / machine learning (AI / ML) that would allow them to fully capitalize the knowledge contained in such data.

In the project, we will develop and test AI / ML methods and algorithms implementing the abovementioned functions, and create target software with the abovementioned algorithms implemented. We will test the prototype software, preparing it for implementation in the client’s commercial activity.

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