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Innowise is an international full-cycle software development company founded in 2007. We are a team of IT professionals developing software for other professionals worldwide.
Innowise is an international full-cycle software development company founded in 2007. We are a team of IT professionals developing software for other professionals worldwide.
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.
When businesses need AI data annotation outsourcing
Launching a new AI project
Scaling existing AI solutions
Improving AI model accuracy
Integrating multi-modal data
Solving quality control issues
Launching a new AI project
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.
Scaling existing AI solutions
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.
Improving AI model accuracy
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.
Integrating multi-modal data
Give your AI models a fuller understanding of real-world scenarios with multi-layer annotation across text, images, audio, and video.
Solving quality control issues
Eliminate data labeling errors. We apply structured workflows, human validation, and rigorous data checks to keep your datasets clean and unbiased.
Launching a new AI project
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.
Scaling existing AI solutions
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.
Improving AI model accuracy
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.
Integrating multi-modal data
Give your AI models a fuller understanding of real-world scenarios with multi-layer annotation across text, images, audio, and video.
Solving quality control issues
Eliminate data labeling errors. We apply structured workflows, human validation, and rigorous data checks to keep your datasets clean and unbiased.
We offer all types of AI data annotation & labeling services
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.
Data preparation
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.
Workflow customization
We design the right labeling workflow (e.g., choosing methods and tools) to make data annotation efficient and accurate.
Annotation & labeling
Our expert annotators add the necessary tags, categories, or markers to your data, whether it’s images, text, audio, or video.
Feedback cycle
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.
Evaluation
Every dataset goes through multi-layer quality checks. You receive a ready-to-train dataset that meets both your accuracy standards.
Why trust AI data annotation services to Innowise?
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.
What our customers think
Alice BodnarCOOAtlas Guides
“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.”
IndustrySoftware
Team size8 specialists
Duration24+ months
ServicesMobile development
Johannes SchweiferCEOCoreLedger AG
“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."
IndustryIT services
Team size6 specialists
Duration17+ months
ServicesMobile app development
Dominik MärklDirectorOneStop Pro
“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.”
IndustryConstruction
Team size7 specialists
Duration36+ months
ServicesCustom software development
FAQ
What is the difference between AI data annotation and data labelling?
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.
What steps are involved in data annotation for AI and ML models?
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.
How does Innowise ensure the accuracy of data labeling?
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.
What are some use cases of AI data annotation?
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.
Feel free to book a call and get all the answers you need.