We use a multi‑layer quality assurance process: trained annotators work with your defined guidelines, we perform peer review and lead review, we calculate inter‑annotator agreement and error rates, and you receive a quality summary with each delivery.

We support text, image, video and audio annotation, and we have experience in varied domains (e.g., healthcare, automotive, retail) so we can adapt the label schema to your industry.

We don’t use a “one‑size‑fits‑all” price. Each dataset is unique in complexity, number of classes, edge cases, required attributes, etc. We will evaluate your dataset, define the workflow, and then provide a quote (per image, per bounding box, per hour, or contract‑based) including volume discounts.

After reviewing your dataset size and complexity, we give you the expected delivery timeline, including milestones. We scale with additional annotators and/or shifts as required, while maintaining consistency via our QA pipelines.

We execute NDAs/DPAs, use secure transfer and storage protocols, restrict access to approved personnel, and support encrypted data workflows or air‑gapped environments if needed.

Yes — we include a post‑delivery review phase and will correct any annotations that don’t meet the original specification (provided the original requirements haven’t changed). We can also support ongoing refinement if your model evolves.

We recommend running a small sample/ pilot batch of annotations (often free or at minimal cost) so you can review quality and workflow fit before scaling. If your scope changes (e.g., more classes, extra attributes, more volume) we’ll revisit the workflow and provide a change‑order quote rather than silently absorb cost or delay.