About DOT Data Labs
We exist to make AI companies faster — by giving them high-quality data they can actually trust.
DOT Data Labs is an End-to-End data provider for AI companies. We combine domain-expert reviewers, modern annotation tooling, and senior program management so AI and ML teams can ship faster without compromising quality or compliance.
Why we exist
- Most labeling vendors optimize for hours billed, not model lift.
- Generic crowdsourced labels break on regulated and specialized domains.
- ML teams waste cycles managing vendors instead of training models.
- Quality is promised, not measured — until production fails.
- Off-the-shelf datasets rarely cover the edge cases your model actually trips on.
- Compliance and licensing are treated as paperwork instead of as engineering.
How we are different
Outcomes, not hours
We are paid to move your model metrics — not to bill annotation time.
Expert workforce
Clinicians, lawyers, linguists, and engineers. Vetted, NDA-bound, and reviewed.
Senior program leads
A named program manager owns delivery End-to-End. No ticket queues.
Transparent quality
Per-batch dashboards: agreement, golden-set F1, throughput, reviewer-level performance.
Compliance built in
GDPR & CCPA workflows, NDAs by default, and audit-ready provenance on every delivery.
End-to-End under one roof
Sourcing, labeling, QA, and delivery handled by one team — no vendor stitching required.
How we deliver
- 01
Scoping & guideline co-design
We meet with your ML and product leads to map model objectives, target metrics, and the failure modes the next training run must address. Together we draft an annotation rubric and a calibration set.
- 02
Pilot & calibration
A small batch goes through our reviewers and yours in parallel. We measure agreement, surface ambiguous cases, and lock the guidelines before we scale.
- 03
Production labeling
Domain-expert annotators with model-assisted tooling work through the queue. Per-batch quality dashboards stream to your team.
- 04
Multi-pass QA & adjudication
Independent reviewers re-label a statistical sample and adjudicate disagreements. Golden-set F1 and per-class accuracy are reported every batch.
- 05
Delivery, evaluation & iteration
Data ships in your preferred schema. We run evaluation against your held-out set, capture model-lift signals, and roll learnings into the next sprint of guidelines.
What you get
Production-ready labeled dataset
Delivered in the schema and storage of your choice (S3, GCS, Azure, on-prem) with versioned manifests.
Annotation guidelines & calibration set
A living document plus a held-out calibration set you can re-use to onboard future vendors or in-house teams.
Per-batch quality reports
Inter-annotator agreement, golden-set F1, per-class accuracy, throughput, and reviewer-level performance.
Audit trail
Per-label reviewer, timestamp, and version history — ready for regulator and customer audits.
Handover & training
Documentation, tooling access, and a working session so your team can extend the pipeline internally.
Teams we partner with
Built for production AI, not pilots
GDPR & CCPA compliant
Lawful basis, data-subject rights workflows and documented retention policies on every engagement.
Senior delivery ownership
A named senior program lead owns every engagement End-to-End — no ticket queues, no vendor relay.
Human-in-the-loop QA
Multi-pass review, gold-set calibration and consensus scoring — quality reviewed by people, not just scripts.
NDA & secure handling
NDAs by default, role-based access, EU/US data-residency options and full chain-of-custody on project assets.
What customers say about working with us
Senior people, every project
A named program manager with a decade of ML data experience owns delivery from kickoff to handoff.
Quality you can audit
Per-batch agreement, golden-set F1, and reviewer-level performance — shipped with every delivery.
Built to scale with you
From a 2-week pilot to a multi-year program, the same team and the same quality bar.
No lock-in
Your data, your guidelines, your tooling if you prefer. Hand-off documentation is part of every project.
Ready to scope your dataset?
Tell us about your model and target metrics — we'll come back with a data plan and timeline.
Frequently asked questions
We operate globally with delivery centers across multiple time zones and a distributed expert network in 40+ countries.
A core team of program managers, ML and tooling engineers, plus a vetted expert workforce of 500+ specialists scaled per project.
Foundation model labs, AV programs, healthcare AI teams, defense primes, fintechs, and Fortune 500 ML organizations.
Most engagements progress from kickoff to the first labeled batch within one to two weeks, although exact timing depends on how specialized the workforce must be.
Our pricing is meticulously structured on a per-project basis, offering full transparency through a detailed breakdown of costs.
We support deployment configurations within your controlled Virtual Private Cloud (VPC), on-premise environments, or air-gapped systems when data sensitivity mandates strict isolation.
Upon project completion, full ownership of the data and all associated intellectual property rights transfer to your organization.
Production datasets often require ongoing maintenance to ensure model efficacy. Our approach involves establishing continuous data programs that incorporate scheduled refresh cycles.