Dataset Finder

Company profile:

Startup name: Dataset Finder

Tagline: Your AI Training Data Workspace.

Elevator Pitch: Dataset Finder is an AI training data workspace that helps AI teams discover curated datasets, evaluate their quality and training readiness, organize datasets across projects, and order custom training data.

Target Market: AI Tools

How will you make money?:

How much capital have you raised?: null

Website: https://datasetfinder.co/

City/Country: France

AI-assisted summary:

Dataset Finder is a software workspace for AI teams to discover, assess, organize, and, when necessary, commission training data for machine-learning projects. It matters because it combines dataset discovery with data-annotation services and governance-oriented recordkeeping in one product, rather than functioning solely as a file repository. (Source: https://datasetfinder.co/)

The problem and target users

The company positions the problem as fragmented training-data work: teams may search multiple dataset repositories, evaluate quality and licensing manually, coordinate annotators, and track decisions across folders, spreadsheets, and collaboration tools. (Source: https://datasetfinder.co/)

Its stated audience includes AI engineers, ML teams, AI startups, researchers, consultants, and enterprise AI programs. (Source: https://datasetfinder.co/) For early-stage builders, the practical use case is reducing the time between defining a model problem and identifying usable data; for larger or regulated teams, the pitch extends to keeping provenance, project links, and documentation in a centralized inventory. (Source: https://datasetfinder.co/)

Product and solution

At the product’s core is natural-language search across what Dataset Finder describes as tens of thousands of curated AI datasets. Users can describe a prospective workload in plain language and receive ranked dataset suggestions rather than relying only on conventional keyword filters. (Source: https://datasetfinder.co/)

Dataset Finder says its catalog entries include metadata and AI-generated analysis covering areas such as licenses, labels, quality signals, bias indicators, preprocessing considerations, use cases, and fine-tuning readiness. (Source: https://datasetfinder.co/) The company also states that it does not host, store, or redistribute cataloged third-party datasets; instead, it maintains metadata and directs users to the original publisher or storage location, where the applicable license governs access. (Source: https://datasetfinder.co/pricing)

The workspace layer lets teams save datasets, group them into projects and collections, attach notes, and link external locations such as Hugging Face, S3, Git repositories, cloud storage, and internal datasets. (Source: https://datasetfinder.co/) The product additionally advertises reusable AI training “recipes,” including selected datasets, model configurations, preprocessing pipelines, training parameters, and evaluation harnesses. (Source: https://datasetfinder.co/)

When an existing dataset is insufficient, customers can request custom data collection or annotation. Dataset Finder says this service covers image, video, audio, text, multimodal, and medical-data workflows, while Innovatiana’s teams handle labeling, QA, and delivery. (Source: https://datasetfinder.co/custom-data)

Business model, pricing signals, and traction

Dataset Finder uses a freemium SaaS model: its Free tier is listed at €0 per month, Startup at €9.90 per month, and Pro at €29.90 per month. (Source: https://datasetfinder.co/pricing) Paid tiers offer expanded research allowances and workspace capabilities, while the Pro plan adds exports, compliance reports, training-data inventory features, and access to the full recipe library. (Source: https://datasetfinder.co/pricing)

Custom annotation is a separate revenue stream, priced through platform credits for typical projects and through quoted enterprise engagements for larger, security-sensitive, or data-residency-constrained work. (Source: https://datasetfinder.co/pricing) Dataset Finder is built by Innovatiana, which states that it has delivered training data since 2021, labeled more than 20 million artifacts, and served more than 200 companies; these are parent-company claims and should not be treated as standalone Dataset Finder traction. (Source: https://datasetfinder.co/)

Expert take

Dataset Finder’s strongest positioning is its attempt to connect two normally separate buying and operating motions: finding public data and producing missing proprietary data. That linkage could be especially useful for lean AI teams that need a practical path from dataset research to labeled delivery.

The main unknown is independent product adoption: public materials reviewed for this overview provide pricing and product detail, but not standalone customer, revenue, or usage metrics for Dataset Finder itself. The company will need to prove that its curation and evaluation layer is meaningfully more reliable and useful than navigating established dataset repositories directly.

Note: Information based on publicly available sources at the time of writing, and summarized by AI.