Data for Automotive AI
Vehicle catalogs, parts, listings, pricing, roads, and sensor data — every dataset the automotive AI stack needs.
Across the automotive industry — OEMs, marketplaces, parts distributors, dealer platforms, and autonomous-driving teams — every AI product is bottlenecked by domain-specific structured data. We deliver labeled, normalized, and continuously refreshed datasets covering vehicles, parts, listings, prices, roads, and the sensor feeds that power perception systems.
Challenges we solve
- Vehicle catalogs that mix free-text descriptions with structured specs across thousands of trims
- Parts compatibility data scattered across hundreds of supplier and aftermarket systems
- Used-car and marketplace listings that need deduplication and schema normalization at scale
- Pricing data that shifts daily and needs continuous, clean time-series capture
- Road infrastructure that changes faster than public maps can reflect it
- Long-tail perception edge cases (weather, occlusion, construction zones, rare objects)
- Multi-sensor temporal consistency for LiDAR, camera, and radar fusion at safety-grade quality
Our automotive data solution
Vehicle catalog structuring
Makes, models, trims, and specs extracted and normalized into a single clean schema you can query.
Parts & compatibility data
Aftermarket and OEM parts feeds parsed and reconciled against vehicle fitment, ready for search and recommendation engines.
Listings & pricing aggregation
New and used vehicle listings extracted across marketplaces and dealer sites — deduplicated, normalized, and refreshed continuously.
Road & infrastructure datasets
Signs, lanes, lane markings, road conditions, and roadside assets annotated for HD-map generation and routing models.
Sensor-fusion labeling
LiDAR, camera, and radar synchronized in one tool with sub-pixel and sub-decimeter accuracy for ADAS and autonomy stacks.
Driver & in-cabin behaviour
Driver-monitoring video, gaze, gesture, and attention labels for cabin-AI and safety systems.
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.
Data we deliver
Use cases
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
Yes. We extract and normalize vehicle catalogs (makes, models, trims, specs), parts data (with fitment / compatibility), and dealer or marketplace listings into a clean schema you define. Delivered as a one-off dataset or a continuously refreshed pipeline.
We aggregate new and used vehicle listings, dealer inventory, and pricing data across marketplaces and OEM sites — deduplicated against VIN where available, normalized to your taxonomy, and refreshed on the cadence you need.
We process the full range of automotive sensor formats — point clouds in PCD and LAS / LAZ, ROS bags, plus synchronized camera and radar feeds — for 3D perception, mapping, and fusion model training.
Multi-layered validation: golden sets at every stage to measure annotator performance, multi-pass review, and automated drift detection. Per-batch QA reports are part of every delivery.
Our temporal-aware annotation platform handles extended video and sensor sequences, supporting clips well beyond 30 seconds with consistent track IDs across frames.
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.