How We Work
A methodology built around measurable quality and real model lift — not hours billed.
Every engagement runs the same proven loop: scope the model objective, co-design guidelines, calibrate on a pilot, scale with measured quality, and iterate on the data that actually moves your metrics.
What we optimize for
- Inter-annotator agreement and golden-set F1, batch over batch
- Time from kickoff to first usable batch
- Coverage of the long tail and model failure modes
- Hand-off quality so your team is never locked in
Pillars of our delivery model
Vetted domain experts
Reviewers credentialed for the task. Tested before they touch your data.
Model-assisted tooling
Pre-labels, active learning, and review queues that prioritize the cases that matter.
Multi-pass QA
Independent review on a statistical sample, with adjudication on every disagreement.
Live quality dashboards
You see agreement, golden-set F1, and throughput in real time — not in a postmortem.
The DOT delivery loop
- 01
Scoping & objectives
A working session to map your model objective, target metrics, and what success looks like at each milestone.
- 02
Guidelines & calibration
We co-author labeling guidelines and run a calibration round on a small sample to align reviewers.
- 03
Pilot batch
A 1-2 week pilot to validate quality, throughput, and cost before scaling.
- 04
Production at scale
Multi-pass review, golden-set tracking, and weekly batch deliveries with full QA reports.
- 05
Iterate on model lift
We tune coverage, edge cases, and class balance based on what actually moves your eval metrics.
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.
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.
Why teams choose DOT Data Labs
Domain-expert workforce
Vetted reviewers with the credentials your task requires — clinicians, attorneys, CFAs, native linguists, or sensor-fusion specialists. Not a general-purpose crowd.
Measured quality, not promised quality
Every batch ships with agreement scores, golden-set F1, and per-class accuracy. If quality regresses, you see it before the data lands.
Security & compliance by default
SOC 2-aligned operations, signed NDAs per project, and customer-controlled deployments (VPC, on-prem, air-gapped) on request.
Senior program management
You get a named program lead who owns delivery End-to-End — not a ticket queue. Your ML team stops managing the vendor.
Built to integrate, not to lock you in
Guidelines, tools, and data are yours. We plug into your annotation tool or bring our own — whichever maximizes throughput and quality.
Real model-lift focus
Success is measured in downstream model metrics, not labels delivered. We track lift per data sprint and adjust strategy when the curve flattens.
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 work in your tool of choice (Labelbox, Scale, V7, Encord, Roboflow, CVAT, custom) or bring our own — whichever maximizes throughput and quality.
Per-batch agreement scores, golden-set F1, per-class accuracy, and reviewer-level performance. Reports ship with every batch.
Most engagements kick off within 5 business days and ship a usable pilot batch within 2 weeks.
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.