Quartz Labs — The data layer for frontier AI.

What we ship

Eight modalities and growing. One operational layer.

Built across modalities most vendors are too thin to support. Each shipped through the same coordinated network and the same operational rigor. If your modality isn't listed, ask — bespoke pipelines are the default, not the exception.

VOICE · LIVE

Multispeaker conversational audio with full transcripts in underrepresented languages.

Read about voice →

ROBOTICS · ACTIVE

Teleop demonstrations sourced through SE Asia operator networks and credentialed teams.

Read about robotics →

EXPERT EVALUATION · LIVE

Credentialed domain experts for high-stakes evaluation of frontier model outputs.

Read about eval →

MULTIMODAL & TEXT · ON REQUEST

RLHF, instruction tuning, multilingual classification, cross-modality corpora. Bespoke pipelines.

Read about multimodal →

CODE + LANGUAGE PAIRS · ON REQUEST

Non-English natural language to code mapping. Multilingual code models with regional dialect coverage.

Read about multimodal →

VISION-LANGUAGE · ON REQUEST

Image-caption pairs and visual-instruction data in underrepresented languages and regional contexts.

Read about multimodal →

AGENTIC WORKFLOW TRACES · ON REQUEST

Multi-step agent trajectories with human-verified intermediate decisions. For training and evaluating agent foundation models.

Read about multimodal →

SYNTHETIC + VERIFICATION · ON REQUEST

Human-in-loop verification, curation, and grounding of synthetic datasets. Catches the failures synthetic alone can't see.

Read about multimodal →

Deliverable

What a deliverable looks like.

Every bundle ships with multitrack data, structured timestamps, demographic metadata, and a signed consent log. Same operational rigor across every modality. Tap any tab to switch.

Sample bundle

Consent IDQLB-VOX-2026-04417

LanguageCebuano (ceb)

RegionCebu City, PH

Speakers2 · F/M

Duration00:00:12.4

Sample rate48 kHz · 16-bit

00:0000:0200:0400:0600:0800:1000:12

00.42SPK_01Maayong buntag. Asa man ta moadto karong adlawa, sa merkado o sa playa?
03.18SPK_02Adto ta sa merkado sa, dayon sa hapon sa playa. Init kaayo karon.
06.91SPK_01Sige. Magpalit pud ko og isda nga bag-o, para mag-sugba ta sa gabii.
10.04SPK_02Hala, sulayi ang tindahan ni Nang Lita. Pinakabaratuhon didto.

Demonstration trace

Trace IDQLB-RBT-2026-00892

OperatorOP_HNI_047

TaskPick & place · ALOHA

CohortHanoi

Duration00:00:23.7

OutcomeSuccess

State log

724 samples

Action log

724 deltas

Cameras

wrist · top · side

Force tactile

enabled

Sample rate

30 Hz · synced

Schema

LeRobot v0.5

Evaluation

Eval IDQLB-EVL-2026-02314

DomainMedical · Cardiology

EvaluatorMD · Board-cert · 12 yrs

Model[redacted]

Prompt

"55-year-old patient presents with intermittent chest pain on exertion, family history of MI. Recommend initial workup and triage decision."

Clinical accuracy9 / 10

Safety / triage call10 / 10

Workup completeness7 / 10

Reasoning transparency8 / 10

Guideline alignment9 / 10

Risk of harmLow

Evaluator reasoning

Model correctly escalated for stress testing and ECG, cited ACC/AHA risk stratification appropriately. Missed asking about radiation pattern and recent NSAID use, which would refine differential. Triage decision is clinically defensible.

AUDIT TRAIL · NDA-WRAPPED · PROVENANCE VERIFIABLE ON REQUEST

RLHF preference

Preference IDQLB-RLHF-2026-11203

AnnotatorRT_VN_018 · ML domain

LanguageVietnamese

Prompt

Giải thích sự khác biệt giữa học có giám sát và học không giám sát cho một học sinh trung học.

Response A

Học có giám sát giống như học toán với đáp án ở cuối sách — bạn biết đáp án đúng và mô hình học từ các ví dụ có nhãn. Học không giám sát giống như sắp xếp lại tủ sách của bạn theo chủ đề — không có đáp án "đúng", mô hình tự tìm cấu trúc trong dữ liệu.

Response B

Học có giám sát sử dụng dữ liệu được gắn nhãn. Học không giám sát sử dụng dữ liệu không được gắn nhãn. Các thuật toán phổ biến bao gồm hồi quy, phân loại, phân cụm và giảm chiều dữ liệu.

Annotator rationale

Response A preferred for the high-school audience: uses concrete analogies (math homework, bookshelf) that map to lived experience, avoids unintroduced jargon. Response B is technically accurate but reads as adult ML curriculum.

Pair shownRandomized Annotation time94s Agreement3 / 3 raters

Every deliverable ships with a consent log ID linked to a signed release. Provenance verifiable on request.

How we work.

One operational layer across every modality. From scoping call to first delivery in days, not quarters.

01 — Specify

Specify.

One scoping call. We map modalities, scope, languages or regions, quality bar, rights model, and timeline.

Indicative pricing within 48 hours

02 — Source

Source.

Through our coordinated network of universities, studios, NGOs, operator networks, credentialed experts, and dataset partners.

Native-speaker or domain-expert only

03 — Annotate

Annotate.

AI first-pass plus human review. Structured timestamps, speaker tags, demographic metadata, full audit trail.

Per-row signed consent log

04 — Ship

Ship.

Cleared dataset to your bucket on your schema. Daily drops or batched delivery. Two-week off-ramp on every engagement.

S3 · GCS · SFTP

Read the full process →

How we're different

The default vendor wasn't built for frontier data.

Off-the-shelf vendors are built for languages, modalities, and timelines the last AI cycle paid for. We're built for the cases the next one requires.

Dimension	Off-shelf vendor		Quartz Labs
Language coverage	20 major world languages	→	Underrepresented languages live, hundreds more quotable
Modalities	Voice or text, rarely both	→	Voice + robotics + expert eval + multimodal
Ethical sourcing	Mass-scraped, opaque provenance	→	Per-row signed consent log, verifiable
Pilot to quote	4-week scoping minimum	→	48-hour quote, 14-day kickoff
Commitment	6-month minimum, exclusivity	→	Two-week off-ramp, no exclusivity

Cases

Work we've shipped.

One live case to date. New cases land as each engagement matures past production milestones.

Case 01 · Voice · 2026

Seven languages, intro to first shipment in eighteen days.

A frontier voice AI lab needed multispeaker conversational audio plus human transcripts in seven Southeast Asian and minority languages. Their existing vendors couldn't quote on more than two.

FRONTIER VOICE AI LABRead the full case →

IN PRODUCTIONCase 02 · Robotics · 2026

SE Asia teleop cohorts, structured demonstration data.

Foundation-model robotics team scaling per-task demonstration coverage through our operator network. Pilot in production.

SE Asia

Operator cohorts

Multi-rig

Standard + custom

Synced

Multi-cam + tactile

FOUNDATION-MODEL ROBOTICS TEAMCase study forthcoming

IN PRODUCTIONCase 03 · Expert evaluation · 2026

Standing medical and legal evaluator networks.

Frontier AI safety team running structured evaluation across MD-credentialed and JD-credentialed networks. Multiple specialties tagged. Audit trail per evaluation.

Multi-domain

Credentialed

NDA

All evaluators

Audited

Per evaluation

FRONTIER AI SAFETY TEAMCase study forthcoming

The data layer for frontier AI.

Eight modalities and growing. One operational layer.

What a deliverable looks like.

How we work.

The default vendor wasn't built for frontier data.

The data is the bottleneck.

Work we've shipped.

Seven languages, intro to first shipment in eighteen days.

SE Asia teleop cohorts, structured demonstration data.

Standing medical and legal evaluator networks.

Ship data no one else can.