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Specialized AI data annotation services

AI data annotation and fine-tuning
with experts-in-the-loop.

DaQLI accelerates sector-specific AI by delivering high-value data services.We cover the entire data lifecycle: data collection, expert annotation, validation and quality control,diagnostics, human feedback on AI models, and reliable, scalable dataset preparation.

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Multimodal approach

Text Includes but is not limited to document classification, entity extraction, handwritten text transcription, OCR validation, intent labeling, LLM evaluation, and legal, financial and medical documents.
Audio Includes but is not limited to transcription, speaker diarization, intent and emotion labeling, and multilingual & dialect annotation including Moroccan Darija and Tamazight.
Image Includes but is not limited to image classification, bounding boxes, segmentation, OCR, anomaly detection, and visual analysis for agriculture, healthcare and manufacturing.
Video Includes but is not limited to object tracking, activity recognition, trajectory annotation, robot manipulation, human-robot interaction, autonomous vehicle, and Physical AI.

About us

Reliable AI starts with reliable data.

DaQLI is an AI data annotation and fine-tuning company, founded by AI researchers to help organizations turn raw data into AI-ready datasets. We combine our own annotation platform with a network of domain experts to cover the full data lifecycle, from collection to expert annotation, validation and quality control, across text, image, audio and video.
  • Founded by AI researchers
  • End-to-end data lifecycle
  • Text, image, audio & video

Built within UM6P Foundry, the venture studio of UM6P.

Data

Your AI is only as good as your data.

As AI models become more accessible, the competitive advantage comes from the quality, relevance and reliability of the data behind them.

End-to-end data preparation
  1. Collect
  2. Structure
  3. Annotate
  4. Score
  5. Validate
  6. AI-ready
DaQLI handles the data lifecycle from raw input to reliable output. So your team can save up to 80% of the time spent preparing training data.

Our Services

DaQLI's offerings.

From self-managed workflows to fully managed data projects and expert advisory, DaQLI supports your AI data needs end-to-end.

Self-managed

Software as a Service

Run your own data workflows.

Access DaQLI annotation platform to manage data labeling projects, organize annotator workflows and review annotations from one workspace.

  • Full access to the DaQLI annotation platform
  • Multimodal tools: text, image, audio, video
  • Client, admin & annotator workspaces
  • Project, task & quality management

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Fully managed

Data Labeling as a Service

We handle the data work for you.

DaQLI manages your annotation end-to-end, from workflow setup and annotation to expert allocation, quality control and validation to deliver AI-ready data.

  • Domain experts matched to your AI project
  • Task distribution and annotation
  • Scoring, validation and quality assurance
  • Reliable, AI-ready dataset delivery

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Expert support

Consulting

Get expertise where you need it.

DaQLI experts help your team define the data your model needs, identify gaps, design training and evaluation datasets to build and improve your AI.

  • Define training & evaluation data requirements
  • Dataset audit & gap analysis
  • Annotation, validation & QA workflow design
  • Model evaluation & human feedback loops

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Expert in the loop. Data empowered.

Use cases

Specialized data annotation for
complex industries.

DaQLI combines specialized human expertise with rigorous data workflows to create reliable datasets for complex, high-stakes AI applications.

Agriculture

High-quality agronomic data for crop monitoring, disease and pest detection, phenotyping, yield estimation and precision agriculture. Supports imagery, video, sensor and geospatial data for training and evaluating agricultural AI models.

Medical

Expert-labeled data for medical imaging, pathology, handwritten prescriptions and clinical documents, biomedical signals and patient monitoring. Supports AI for diagnosis, disease detection, segmentation and clinical model evaluation.

Industry

Expert-labeled data for manufacturing, automotive, aerospace, chemicals, energy, mining, logistics and food & beverage. Supports AI for quality inspection, defect detection, maintenance, process optimization, safety monitoring and anomaly detection.

Generative AI

Build domain-specific AI for areas such as legal, finance and fraud detection through fine-tuning, human feedback and evaluation, helping models learn the terminology, rules, knowledge and workflows of each sector.

Physical AI

Multimodal data annotation for autonomous vehicles, humanoids, robotics, drones and other physical AI. Supports AI applications in perception, object tracking, trajectory prediction, sensor fusion, manipulation and human-robot interaction.

Government

Secure, expert-labeled data for public services, infrastructure, defense and sovereign AI applications. Supports AI use cases in document intelligence, geospatial analysis, public safety, fraud detection, natural disaster prediction and model evaluation.

Founders

Founded by AI researchers.

DaQLI was founded by academic researchers who combine machine learning, statistics, computer engineering and applied mathematics with hands-on industry experience. It was built in collaboration with UM6P Foundry, the deep-tech and AI-native venture studio of Mohammed VI Polytechnic University.
Co-founder & CEO Machine Learning · Data Science · Data Quality Ikram Chairi Assistant Professor at UM6P College of Computing PhD in Machine Learning, Statistics & Data Science Google Scholar
Co-founder & CTO Applied Mathematics · Industry 4.0 · AI Research Selwa El Firdoussi HDR Professor at EMINES School of Industrial Management PhD in Computer Engineering & Applied Mathematics Google Scholar
Built in collaboration with UM6P Foundry

Talk to a data expert

Let’s scope your
AI data needs.

Tell us what you are building. We will help you define the right workflow for your data, expertise requirements, quality targets and timeline.

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