#1 AI data platform on G2 · rated 4.9 by AI teams

The AI data bottleneck ends here.

SuperAnnotate has every infrastructure component AI teams need to create, evaluate, and improve data in any modality, until every dataset is training-ready.

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The data engine

items moving through the pipeline right now

01

Multimodal AI

Any modality in: RL environments, RLHF, image, video, audio, LiDAR, custom.

 02

Agentic AI

Your models pre-label, judge rubrics, and score pass rates.

 03

Data Management

Workflows, roles, quality gates, and dashboards you compose.

 04

Data Orchestration

Triggers route every item between models and human experts.

In raw data, any modality, any use case Out training-ready dataset ✓ 18,412 items

4.9 / 5

The best-rated software platform for AI data on G2.

Ranked the #1 data annotation platform on G2 – chosen by frontier labs and enterprises for usability, support, and results that show up in the model.

  • Leader · Data Labeling
  • Best Usability
  • Easiest to Use
  • Momentum Leader
  • Best Support
  • Most Implementable
  • They stand out for their data quality, attention to detail, and fantastic communication.
    Databricks Chief Neural Network Scientist superannotate.com
  • It keeps everything organized and makes collaboration so much easier.
    Verified user Data labeling, small business G2 review
  • The SuperAnnotate customer support is exceptional with a very quick response time.
    Verified user Image annotation user G2 review
  • 2× faster time to model Twelve Labs
  • ⅓ of the annotation time Körber
  • +10% higher F1 score GumGum

01 multimodal ai

Any modality. Any use case. One editor.

From RLHF preference data and SFT to image, video, and audio annotation to RL environments with custom verifiers and rubric evaluations – if a frontier team needs the data, SuperAnnotate has the interface for it.

superannotate · multimodal editor any modality

env browser_shopping_v2 · task “buy cheapest 27″ monitor under $250”

agent → search(“27 inch monitor”)

agent → sort_by(price_asc) · filter(size=27)

agent → add_to_cart(item_id=8841, $219.99)

agent → checkout() ✓ episode complete in 6 steps

Custom verifiers

price ≤ budget constraint

correct screen size selected

no unsafe tool calls

optimal path (2 redundant steps)

reward0.87

builder · drag & drop editor you design it

Components

  • ⌘ Model output
  • ☰ Rich text field
  • ★ Rating scale
  • ▣ Bounding box
  • ♫ Audio player
  • ⇄ Code diff

Your annotation UI

  • component Side-by-side model output
  • component 5-point rubric rating
  • component Rationale text field
  • logic Show follow-up if score < 3
agentic assistant ai builds it

Build a side-by-side RLHF review UI with a 5-point rubric, rationale field, and expert-review routing.

  • Generating UI layout
  • Wiring rubric logic & validations
  • Connecting data source & roles

✓ Experience ready in 1m 42s – minutes, not days.

  • Every frontier use case

    RLHF preference ranking, SFT authoring, agent trace review, RAG grading, and RL environments with custom verifiers and rubric evaluations.

  • Native media tooling

    Pixel-accurate image tools, frame-level video tracking, and waveform-level audio transcription and tagging.

  • Drag-and-drop custom forms

    If a modality doesn’t exist yet, build its editor yourself – compose any interface from form components, media players, and custom code.

  • Agentic assistant

    Describe the experience you need and the assistant assembles the UI, logic, and workflow – in minutes, rather than days.

02 agentic ai

Connect any model. Put it to work everywhere.

Bring your own models or any frontier API and drop them into annotation flows, evaluation flows, and rubric verification – or run them inside RL environments to score pass/fail rates across multiple models automatically.

  • Model-agnostic by design

    One connection layer for GPT, Claude, Gemini, Llama, Mistral, Qwen, or your fine-tunes – swap models without rebuilding a single flow.

  • AI in the labeling loop

    Pre-label, co-pilot, and interactively assist annotators; let judges grade rubrics and route low-confidence items to humans.

  • Arena-style comparisons

    Run blind, side-by-side model battles on your own data and rubrics – not someone else’s benchmark.

  • Automated pass/fail scoring

    Point multiple models at the same RL environment or eval set and get verifier-computed pass rates per model, per rubric.

model hub · connections 6 models live
GPTClaudeGeminiLlamaMistralyour fine-tune Your workflows label · eval · verify
arena · blind comparison human votes

PROMPTSummarize this 40-page clinical trial protocol for a review board.

Model A Randomized, double-blind phase III study of 480 patients evaluating…
Model B This protocol describes a phase III trial. Key endpoints include…

Cast a vote – identities reveal after. Elo updates live

rl environment · pass rates auto-verified
Model A 87%
Model B 84%
Model C 79%
baseline-v1 41%

verifier: rubric_v3 · 1,200 episodes pass = all checks green

03 data management

Manage the data. And everyone touching it.

Not just data management – user management, complex annotation workflows, quality management, and project management in one place, with automations powered by Agentic AI and Orchestration, and fully custom dashboards on top.

project · frontier-sft-q3 / dashboard pm view
Items completed today 12,480 ▲ 8.2%
Acceptance rate 96.4% ▲ 1.1
Active contributors 214 ▲ 12

Throughput vs quality · 14 days items accept %

Quality gates

Inter-annotator agreement0.91

LLM-judge rubric ≥ 488%

Expert spot-check pass97%

Edge-case escalations3%

Workflow · live annotate → ai check → expert review → done

Annotate 3

AI check 2

Expert review 2

Done 1

  • User & role management

    Admins, PMs, QA leads, annotators, and vendors – role-based access down to individual UI components.

  • Complex annotation workflows

    Multi-stage pipelines with consensus, review layers, disagreements, and automated routing between them.

  • Quality management

    Agreement scores, honeypots, LLM-judge gates, and expert audits – enforced automatically with Agentic AI and Orchestration.

  • Projects & custom dashboards

    Track progress, cost, and performance per project, contributor, or vendor – on dashboards you compose yourself.

04 data orchestration

Set a trigger on anything. Automate everything after.

Every action on the platform can fire a workflow. Mix AI and human collaboration – pre-label with models and verify with humans, create with humans and evaluate with models, even score human performance against different AI judges. Combined with Agentic AI and Data Management, this is the piece that automates producing the highest-quality datasets.

orchestrate · pipeline_quality_loop running
TRIGGER item.status → completed AI pre-label draft annotations LLM judge rubric_v3 · score 1–5 Router confidence ≥ 0.9 ? Auto-approve yes → skip human Expert review no → human verifies Export → training set
  • WHENitem completed RUNLLM judge on rubric_v3 THENif score < 4, send to expert review
  • WHENdataset imported RUNmodel pre-labeling on every item THENassign humans to verify low-confidence items
  • WHENannotator submits 50 items RUNthree AI judges to score their work THENupdate performance dashboard & routing weights

Make every dataset training-ready.

See how frontier labs and enterprises use SuperAnnotate to turn the AI data bottleneck into their fastest-moving pipeline.

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