Enhance accuracy with AI data annotation outsourcing from $1,000 a month, all in

Sourcefit’s AI data annotation rates are $1,000 a month for entry level, $1,355 for mid level and $1,710 for senior annotators, per full-time employee working from home. Office-based staff add $100 a month. Each rate is all in: salary and benefits at cost plus the $395 management fee. There are no setup, recruitment or exit fees. Where the work suits it, output-based pricing is also available, built on the same transparent framework: we show how the unit price is calculated, so you know exactly what you are paying for.

No. The fee is already inside every rate on this page and on every quote. A $1,000 rate is about $605 of salary and benefits at cost plus the $395 fee. You will never see the fee added to a rate you were quoted.

Legal employment and compliance with an HR team on the ground, recruitment, an i5 computer with dual monitors and headset, a 24/7 IT help desk, a secure managed network, our security certifications and insurance, training and quality, client success management and the Knit workforce platform. For office-based staff it also covers the seat, office network and utilities.

Most AI data annotation roles are recruited, interviewed and onboarded in two to four weeks. Domain-specialist annotators, such as medical or legal, can take longer. You interview and choose the annotators.

That is your choice. Most clients manage the team directly: the annotators report to your managers and work inside your processes and tools, with Sourcefit handling employment, HR, equipment, IT and quality support behind them. If you would rather not manage day to day, our Sightline model builds an autonomous team with its own Sourcefit team lead and quality layer that delivers against agreed SLAs, while still reporting directly to you and working to your KPIs, not ours. Sightline is quoted separately.

Yes. Annotators work in your labeling platforms and pipelines, such as Label Studio, CVAT, Labelbox, SageMaker Ground Truth or your own tooling, under your access rules. You keep ownership of every account, and access is revoked the day someone leaves.

Sourcefit holds ISO 27001:2022 and ISO 27701 certification directly, is PCI DSS validated, has a SOC 2 report, signs HIPAA business associate agreements where needed and operates GDPR-aligned data handling. Home-based staff work on company-managed computers over a VPN with traffic routed through Cloudflare.

The team is yours. If you decide to move them to your own entity or take them with you, they go with you. There is no transfer fee, buyout or conversion charge.

Sourcefit’s standard is a dedicated annotator at a flat monthly rate, with salary at cost and the management fee shown separately, which works out far below per-label platform pricing once rework and platform commissions are counted. Output-based pricing per label or per task is available for well-defined, high-volume work, built on a transparent framework with the calculation shown so you can see how the unit price was built.

Data type (3D point clouds and pixel segmentation cost more than image tagging), task complexity, the expertise required (medical or legal annotators earn more than generalists), accuracy requirements that need multiple labelers per item, and rework on rejected batches. Tool seats are normally yours. The management fee is the same for every annotator; the salary line carries the difference.

Crowd platforms give the lowest unit cost and variable quality from rotating workers; consensus checks cannot add domain knowledge. A dedicated team is trained on your guidelines, calibrated continuously, works in secure facilities and produces less rework. Many clients use crowd for simple volume and a dedicated team for anything that touches model quality.

Keep guideline ownership, edge-case decisions and final QA in house; outsource the volume. Small, highly sensitive datasets may stay internal. The dedicated-team model keeps you in control of both because the annotators are your staff working in your tools.

Against a gold set you own, with inter-annotator agreement, precision and recall on audited samples, rework rates and how fast guideline changes are applied. QA is multi-stage: independent labeling, adjudication and expert review where the task needs it, not majority voting alone. You audit against your own gold set rather than taking our numbers on trust.

Yes. A pilot on your actual data, including easy, hard and stress cases, sets the accuracy benchmark and the throughput assumptions before the team scales.

We recruit to the task: generalists for tagging and transcription, and annotators with relevant credentials for medical, legal, financial, geospatial or audio work. You see candidate profiles and test them on your taxonomy before choosing.

Yes. Annotators work inside your platform and taxonomy (Label Studio, CVAT, Labelbox, SageMaker Ground Truth or your own tooling) and deliver standards-based exports such as JSON or COCO with annotator-level metadata. No tool migration is required.

Role-based access to your systems only, company-managed computers over a VPN, ISO 27001:2022 and ISO 27701 controls, GDPR-aligned handling, HIPAA business associate agreements for PHI, and deletion and transfer terms in the contract. Data never leaves your environment.

Yes. New annotators are calibrated against the same gold set before they touch production, so agreement holds as the team grows, and you can scale back without penalty because there is no lock-in.

Yes, in the loop. Models pre-label and humans validate, correct and adjudicate; unsupervised AI labeling develops systematic errors that only humans catch. Demand is shifting from simple tagging to judgment-heavy work: edge cases, evaluation sets and preference data. Our teams work AI-assisted queues in your tools.