Datasets · Annotation · Evaluation · Expert data
We build custom datasets, annotate at scale and provide human-in-the-loop evaluation to help teams train and fine-tune AI models across text, image, video, and audio. Every project is backed by clear specifications, measurable quality standards, and a documented chain of custody for confidence at every stage.
Free from competing interests and platform bias. Your data never passes through a competitor-owned pipeline.
Delivery discipline built in financial services and other audited environments, applied to training data.
Multi-pass review, golden-dataset seeding, and agreement scoring on every batch we ship.
Start with a pilot against a golden dataset. See measured accuracy before you commit to volume.
Generalist annotation is a commodity. We focus on work that requires human judgment, domain expertise, or broad language coverage, with human-in-the-loop, backed by our robust talent network and a community of more than 200,000 gig workers.
The full record of how people actually talk to companies, structured for models that have to handle it.
Tasks where being wrong is a compliance event, not a metric. Reviewed by people who do this work professionally.
Language coverage tied to real delivery geographies, not a marketplace of anonymous freelancers.
Transparent by design. Every deliverable comes with clear ownership, documented quality checks, and a defined output format.
Built to your specification when nothing off the shelf fits the task your model has to learn.
Trained people add the labels your model learns from, across every format you work in.
Human-in-the-loop evaluation provides the insight needed to assess model performance, identify failure modes, and improve alignment with desired outcomes.
When the task needs real professional knowledge, the annotator is a practitioner — not a generalist with a guideline document.
Where model training is heading: interactive settings in which an agent attempts a real task and a rubric judges the outcome. We are building here deliberately and at measured pace — we would rather show you a working environment than a roadmap slide.
Volume is easy to get. Judgment is not. Getting from raw capture to training-ready data means resolving ambiguity and verifying every label at a scale most teams can't run in-house
Four stages, each with its own checkpoint. Nothing moves forward until the stage before it passes.
We collect or license the raw material — from your own systems, from custom collection campaigns, or from rights-cleared third parties. Provenance is recorded at ingest, not reconstructed later.
Deduplication, quality screening, PII detection and redaction, and schema normalization. Most raw data is not worth labeling; this stage decides what is.
Trained annotators apply the agreed specification. Ambiguous cases route to a senior reviewer rather than being guessed at, and every routing decision is logged.
Independent review pass, agreement scoring against the golden dataset, then delivery in your format through your secure channel — with the quality report attached.
Most engagements reach a delivering pilot inside three to four weeks. Complex or heavily regulated scopes take longer, and we will say so at the start rather than at the end.
We walk through the task, the edge cases, and what "correct" means to you. You get a written specification and a draft rubric before anyone labels anything. If we think the task is underspecified, we say so here — that conversation is the highest-value hour in the whole engagement.
We turn the specification into annotator guidelines, a golden dataset, an escalation path, and an acceptance threshold. You approve all four in writing. This is the artifact that makes quality measurable instead of asserted.
We select annotators against the domain requirement, train them on your guidelines, and qualify them on the golden dataset before they touch production data. Anyone below threshold retrains or comes off the project.
A small batch, delivered in your real format, scored against the golden dataset. You see genuine output and measured accuracy — including where we fell short — before committing to volume or signing a longer term.
Volume ramps on an agreed cadence. Every delivery carries accuracy against the SLA, inter-annotator agreement, throughput, and an open-issues list. Guidelines get versioned as your task evolves.
Access control, data residency, secure delivery environments, and contractual IP terms are set up before stage one, not bolted on after. Sub-processor list and DPA available at diligence.
Every vendor in this market says the word "quality." These are the specific mechanisms behind ours — ask any competitor to name theirs at this level of detail.
Known-answer items are salted invisibly through live work. Annotator accuracy is measured continuously against them, not sampled at the end of a batch.
Overlapping assignments measure whether two qualified people reach the same answer. Low agreement signals an ambiguous guideline, and we fix the guideline rather than the score.
Annotate, review, adjudicate. The number of passes is set by the risk of the task, agreed with you up front, and priced transparently.
Every project has a dedicated quality owner responsible for the quality bar and human-in-the-loop oversight, with a direct line for questions, reviews, and escalations.
Every guideline change is dated, justified, and traceable to the batches it affected — so you can explain any label in your dataset months later.
Our curated workforce brings the expertise and continuity required for high-quality annotation, evaluation, and model improvement across industries and languages.
Share a sample task and your definition of correct. You get back a written specification, a rubric, a timeline, and a pilot price — usually within a week, at no cost.