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Turn raw data into AI-ready datasets. We help businesses build and train reliable AI models by delivering precise, secure, and scalable data labeling across text, images, audio, and video.
Turn raw data into AI-ready datasets. We help businesses build and train reliable AI models by delivering precise, secure, and scalable data labeling across text, images, audio, and video.
Don’t let data prep slow you down. We deliver clean, well-labeled datasets so your team can focus on building and deploying AI models faster.

As models grow, so does the demand for labeled data. We scale by combining AI-assisted pre-labeling with expert human review, which enables us to handle thousands to millions of annotations quickly.

Poor labels lead to poor predictions. We clean, validate, and refine your datasets with multi-step checks so your AI learns faster and performs better in production.
Give your AI models a fuller understanding of real-world scenarios with multi-layer annotation across text, images, audio, and video.

Eliminate data labeling errors. We apply structured workflows, human validation, and rigorous data checks to keep your datasets clean and unbiased.





From endless catalogs to customer reviews, e-commerce runs on data. By tagging product photos, reviews, and clickstreams with categories, attributes, and sentiment, we don’t just make data searchable — we train AI models that learn to predict what each shopper really wants.

AI in healthcare is only as good as the data it’s trained on. We annotate X-rays, CT scans, MRIs, and patient records so algorithms can learn to recognize conditions and support doctors in making faster, more accurate decisions.

We label transactions, contracts, and compliance docs with tags like “fraud risk,” “approval needed,” or “suspicious activity.” This helps AI catch fraud in real time, speed up approvals, and keep everything audit-ready.

Not every student learns the same way. By tagging lessons, quizzes, and video lectures with topics, difficulty levels, and goals, we prepare datasets for AI model training that adapt to each student’s needs — recommending the right content, automating grading, and creating tailored learning paths.

Enterprises sit on mountains of unstructured data — emails, reports, chat logs, and contracts. We label this data with categories, sentiment, and entities so AI models can learn to automate workflows, assist employees, and support faster business decisions.

From binge-worthy shows to viral clips, media companies need reliable datasets to power AI at scale. We annotate video frames, audio tracks, and images so your models can classify, organize, and filter content more effectively — supporting smarter content discovery.


Around 80% of AI model development is spent on data preparation. The reason is simple: models are only as good as the datasets they’re trained on. Accurate labeling not only makes AI models more reliable and valuable for business, it also speeds up deployment, lowers maintenance costs, and helps companies achieve results faster.

Our experts take the time to understand your goals. They clarify the type of labeling required and define the quality benchmarks your AI model must meet.
Next, we get your data ready for labeling. That means cleaning and organizing it, removing duplicates or irrelevant parts, and structuring it so every file is easy to annotate.
We design the right labeling workflow (e.g., choosing methods and tools) to make data annotation efficient and accurate.
Our expert annotators add the necessary tags, categories, or markers to your data, whether it’s images, text, audio, or video.
You’ll never be left in the dark. We incorporate regular checkpoints for your feedback, so the final dataset reflects your expectations and there are no surprises at the finish line.
Every dataset goes through multi-layer quality checks. You receive a ready-to-train dataset that meets both your accuracy standards.

We’ll deliver accurate and business-ready datasets that are ready for AI training.
We take care of the time-consuming labeling work so your team can focus on building AI solutions. With precise, trustworthy datasets, you can accelerate development, cut down on errors, and bring reliable models to market faster.

“Innowise’s work met all expectations. The team was efficient, prompt, and on top of their project deliverables. Customers can expect an experienced team that offers an array of business services.”
“Innowise has built an amazing application from scratch in an amazingly short time of just about 3 weeks. Their seniority and in-depth experience in this field make them valuable partners."

“When it comes to handling pressure situations, Innowise has always proven their deftness in managing these situations. They do this by having a clear understanding of our expected results to take our business towards growth and customer satisfaction.”
There is no practical difference. The terms are used interchangeably. Both mean adding tags, categories, or metadata to raw datasets so AI models can learn and make accurate predictions.
The process includes data collection, cleaning, labeling (manual or AI-assisted), quality assurance, and final dataset delivery. In some cases, continuous annotation is added to keep models updated as new data flows in.
We use a human-in-the-loop approach, multi-layer quality checks, and AI-assisted validation tools. Our annotators follow strict guidelines, and each dataset goes through QA before delivery to minimize bias and errors.
Data annotation shows up in countless ways — from spotting tumors in medical scans, guiding self-driving cars through busy streets, and speeding up insurance claims, to powering personalized shopping and catching tiny defects on factory lines.
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