Whether you're labeling training data, building supervised fine-tuning (SFT) datasets, or evaluating model outputs, the question that decides your model's fate is the same: can you trust the human judgments feeding it? Kili is built to make that quality measurable, provable, and scalable.
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Across labeling, SFT, RLHF, and evaluation, it's the same job: producing human judgments you can trust.
Configure multi-stage review with named steps, set the sampling rate between each step, and enforce step separation so no one reviews their own work. Then measure quality several independent ways — gold-standard testing catches accuracy drift, consensus measures agreement on subjective judgments (critical for RLHF and evaluation), review scoring captures human QA, and human-model agreement benchmarks labels against model predictions.
Operations grow from a handful of experts to hundreds of contributors across dozens of concurrent projects.
Kili is built for that: an organization-level user pool deployed across projects with role-based access, automatic workload distribution so no one collides, asset locking to prevent conflicts, queue prioritization, and per-contributor and per-project analytics so managers can see who's fast, who's accurate, and who needs coaching — without the platform slowing down as volume climbs.
Choose the deployment model per project and per contract: managed SaaS to start; Azure Marketplace as a private managed app in your own subscription; a hybrid "On-Premise Data" mode where the platform runs in the cloud but raw data never leaves your storage; or full on-premise on your own Kubernetes/Docker with no internet or root access required after install.
Each tier is contractually defined, so legal and security teams get clear data-residency boundaries in writing — and the same quality and scale capabilities travel with you wherever it runs.
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Kili vs Dataloop — both cover core annotation well. The differences are in how quality is enforced, what the data actually is, and where the platform can run.
| Kili | Dataloop | |
|---|---|---|
| Data types | Image, video, text, PDF, geospatial, audio | Image, video, text, PDF, geospatial, audio |
| Geospatial & sensor imagery | NITF, JP2, GeoTIFF, multispectral & hyperspectral multi-layer, RPC affine transformation, resampling, CRS control, GeoJSON export | COG, GeoTIFF, OSM, XYZ tiles; lat/long annotations with EPSG:4326 export. Sensor-imagery formats not documented |
| Compliance | SOC 2 Type II, ISO 27001:2022, HIPAA, GDPR | SOC 2 Type II, ISO 27001, ISO 27701, GDPR — HIPAA not listed |
| Jurisdiction | European company with a US subsidiary. SaaS hosting choice in Europe or the US | US / Israel |
| Multi-stage review | Named review stages, sampling rate set between each stage, enforced step separation so no one reviews their own work, per-step send-back rules | Labeling tasks chained to QA tasks with issue/note loops |
| Quality signals | Gold standard, consensus, review scoring, at item / contributor / project level | Consensus, honeypot and qualification tasks |
| What a labeler can see | Assigned assets only, or queue mode with no visibility into the dataset; labeler identities can be anonymised | Pulling and distribution allocation methods; labeler anonymisation not documented |
| Deployment | SaaS, hybrid mode where raw data never leaves your storage, and full on-premise | SaaS, hybrid mode where raw data never leaves your storage, and full on-premise |
| Air-gapped operation | No internet or root access required after install | Not publicly documented |
| Version stability | LTS releases twice a year with a one-year support window, so accredited environments stay on a fixed, patched version | Continuous release; LTS track not documented |
| Customer data | Contractual zero-data-retention: no customer data is ever used to train models | Not publicly documented |
Last verified against Dataloop's public documentation, July 2026. Something out of date? Let us know.
Switching platforms usually stalls on one question: what happens to the data we've already labeled? We'll walk your existing projects, ontologies, and labels with you, map them onto Kili, and give you a written migration plan before any commitment. Most teams find the annotations transfer intact and only the review configuration needs rebuilding.
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When expert-driven work has to scale. The judgments come from specialists — radiologists reading scans, linguists judging model outputs, domain experts ranking responses — and the challenge is holding that same rigor across a large, growing workforce instead of letting standards drift. Kili's infrastructure lets expert-grade quality scale with the operation.
When one organization runs many projects at once. Dozens of parallel workstreams, each with its own team, data type, and security requirements — some far stricter than others — and every project needs to stay cleanly separated rather than sharing data by default. Kili keeps each workstream isolated and lets deployment adapt project by project.
For medical-imaging labels, physical AI data, or safety-critical evaluation, "the pipeline ran" isn't good enough — you need a defensible record of how each judgment was checked. Kili produces auditable quality evidence per contributor and per project.
When data cannot touch third-party servers, full on-premise with no post-install internet dependency, a hybrid mode where raw data never leaves your storage, and three contractually-defined hosting tiers give legal teams what they need in writing.
Kili Technology is a complete data suite that supports all data types and handles specialized formats for domain-specific requirements.
Whether you’re a small startup, a growing mid-sized business, or a large enterprise, we’re here to make things simple and support you at every step.
Stay up to date with fresh content from our team — tutorials, use cases, and ideas to help you grow.
We listen closely to our users — and build with their feedback in mind. Their success is what drives us forward.
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