Accelerate fine-tuning and AI product development. Get from problem to production in weeks, not quarters.
Centralize training data operations, governance, and lineage across regulated AI initiatives at scale.
Discover curated datasets faster, organize finds across papers and projects, and keep a citable record of decisions.
Manage datasets across multiple AI initiatives and clients. Keep every engagement organized, billable, and audit-ready.
A real customer workflow, anonymised.
Maya is building a legal copilot fine-tuned for European jurisdictions. She needs multilingual legal QA data, jurisdiction-specific case law citations, and edge-case adversarial examples. None of it exists as a single curated dataset. Buying generic legal corpora wastes weeks of cleaning, and hiring annotators herself means writing guidelines, running QA, and chasing contractors across timezones, let alone the time to invest in setting up the right labeling infrastructure and tooling before any real work begins.
She searches the workspace catalog for legal QA datasets to anchor the model, orders multilingual case-law citation pairs as a custom dataset with credits, and references a community recipe for adapter-based legal fine-tuning. Datasets, the custom-data brief, recipe configs, and project notes all live in one workspace.
Maya goes from blank notebook to first eval run in 6 days. The custom dataset arrives in 3 weeks. Her team can focus on the model rather than on managing labelers.
Dataset Finder is the new platform from Innovatiana, the data labeling team that's delivered training data to AI builders since 2021.
Annotators embedded full-time alongside the hospital's surgeons and data scientists, categorising surgical instruments in frames pulled from operating-theatre video.
French property listings annotated for text classification and text-recognition models — the most completely reported project on Innovatiana's site, with volume, quality and delivery all published.
Sewer and pipe inspection imagery classified against EN 13508-2, the European standard for condition coding of drain and sewer systems.
Not greenfield labelling but review and repair of datasets already in use — the work that decides whether an existing training set is still worth training on.
Frame-by-frame boxes and polygons on continuous real-world driving footage, with model-assisted pre-labelling, annotator cross-validation and object-ID consistency held across frames.
Speech segmented into timestamped chunks, ASR output corrected by hand, then themes, intentions, emotions, entities and interruptions labelled and aligned back to the audio.
Every figure above is Innovatiana's own published number, linked to its source. Named clients are quoted with the job title given on innovatiana.com; the two anonymised case studies are published without a client name and without stated baselines, so they are labelled as such. Dataset Finder is the platform built on this practice.
Discover tens of thousands of curated AI datasets, order custom training data, and turn fragmented data pipelines into production-ready AI assets, all in one workspace.