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OpenTrain AI
Data stack integrations

Bring your data stack. We bring the labeling workforce.

OpenTrain is tool-neutral. Keep using the annotation platform, evaluation tooling, warehouse, vector store, and internal review apps you already run — and hire vetted AI Trainers and Data Labelers from a network of 175,000+ into that stack. Published fees: 15% self-serve, 20% managed.

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The integration model

OpenTrain is a marketplace and matching layer — not another tool in your stack

The annotation, evaluation, and labeling work for your AI program already happens inside specific tools — an annotation platform, an eval framework, a warehouse view, a vector dashboard, or an internal review app your team built. Replacing those tools is rarely the bottleneck.

What is hard is consistently sourcing qualified humans who can sign in to that stack and do high-signal work. OpenTrain is the marketplace and matching layer for those people. We coordinate hiring, payouts, and program logistics; the work itself happens in the tools you already run.

How trainers plug into your stack

Examples of the tools and surfaces OpenTrain trainers work in

These categories are illustrative — OpenTrain talent can work in any tool your team already runs, including platforms not listed here. Each tile describes the integration pattern at the category level; tools shown are representative, not endorsements.

Annotation & labeling platforms

Hire trainers into your annotation tool of choice

Keep your projects, ontologies, and review workflows in the annotation platform you already trust. OpenTrain matches you with vetted trainers who have hands-on experience in tools like Labelbox, SuperAnnotate, V7, CVAT, Encord, and Label Studio. We handle sourcing, screening, payouts, and day-to-day communications; you invite the hired team into your existing workspace and own every project, dataset, and label that gets produced.

Representative platforms

  • Labelbox
  • SuperAnnotate
  • V7 Darwin
  • CVAT
  • Encord
  • Label Studio

LLM evaluation tools

Plug raters into the eval framework you already run

For LLM evaluation, response review, and preference data, OpenTrain pairs you with raters who can work directly inside Braintrust, HumanLoop, LangSmith, OpenAI Evals, or your own internal review UI. We coordinate vetting, calibration, payouts, and program comms; your prompts, traces, eval runs, and model outputs stay in the tool you already use to ship.

Representative tools

  • Braintrust
  • HumanLoop
  • LangSmith
  • OpenAI Evals

Data warehouses & lakehouses

Label and review data where it already lives

When the source of truth for your training data is a warehouse or lakehouse, OpenTrain trainers can work against the views, tables, and notebooks you expose to them — in Snowflake, BigQuery, Databricks, or a comparable platform. OpenTrain provides the people and the workflow scaffolding; your data, governance policies, and warehouse access controls remain entirely yours.

Representative platforms

  • Snowflake
  • BigQuery
  • Databricks

Vector & retrieval

Build retrieval-quality datasets without moving your index

For retrieval-augmented systems, OpenTrain trainers help judge query–document relevance, annotate chunks, and assemble eval sets against your existing Pinecone, Weaviate, or Qdrant index. You control the embeddings, the index, and the production retrieval pipeline; we provide the experienced humans who score and curate the data feeding it.

Representative platforms

  • Pinecone
  • Weaviate
  • Qdrant

Internal tools & general

Wire trainers into the custom workflow you already built

Many teams already have a perfectly good internal review UI, a labeling pipeline driven by custom REST APIs, an S3 + JSONL handoff, or a GitHub Actions workflow. OpenTrain trainers can sign in to that surface as named users, run the workload you've defined, and feed results back through your existing pipeline — no migration, no replatform, no new vendor dashboard for your team to learn.

Common integration shapes

  • Custom REST APIs
  • S3 + JSONL
  • GitHub Actions
  • Internal review apps
Division of labor

What OpenTrain provides, and what your stack keeps

OpenTrain handles

  • Sourcing, screening, and vetting AI Trainers and Data Labelers
  • Matching to your skill, domain, language, and tool requirements
  • Payouts, invoicing, and tax/compliance paperwork for talent
  • Workforce communications, scheduling, and program-level coordination
  • Optional managed-service program leads, QA, and reporting

Your stack keeps

  • Your annotation, evaluation, warehouse, and retrieval tools
  • Your data, embeddings, traces, and labeled outputs
  • IP ownership of every annotation, judgment, and dataset produced
  • Tooling access controls, audit logs, and security posture
  • The labeling instructions, ontology, and workspace setup you run

See related solutions:  data labeling services,  data labeling outsourcing marketplace,  LLM evaluation,  RLHF and preference data, and  managed service.

Data Stack FAQ

Common questions about the integration model

How tool-neutral hiring works through OpenTrain, what stays with your stack, and how to get started.

Bring your data stack — we'll bring the workforce

Post a self-serve project, or talk to a managed-services lead about running the entire program on top of your existing tools.

Self-Service

Post a Job, Hire Experts into Any Platform

Describe your requirements and receive a curated shortlist of domain experts matched to your project. 15% flat fee, no hidden markups.

Most popular
Managed Service

Full-Service, End-to-End

  • Recruiting & live vetting
  • Onboarding & training
  • Daily management & QA
  • Dedicated program lead