— How we work

From request to delivery,
structured all the way down.

Six stages, defensible at every step. We treat data collection as a research operation — not a marketplace transaction — so that what lands in your training pipeline is grounded, consented, and built to hold up under audit.

Stages
6
Quality score
95.2%
Avg. turnaround
4 weeks
The Workflow

Six stages, six hand-offs.

01

Define requirements

We start with a working session that turns a fuzzy 'we need this kind of data' into a written brief — data specifications, demographic targets, edge-case coverage, volume curves, and clear quality thresholds your team will sign off on.

Spec doc · acceptance criteria
02

Sourcing & gating

We tap our verified expert pool through quest-platform integrations and supplier-managed networks. Entry is gated by proof-of-experience, skill evaluations, and performance history — so the people on your task have the right context the moment they start.

10,000+ active contributors
03

Data collection

Tasks are structured for consistency, with real-time monitoring of instruction adherence. Quality signals surface at collection — not weeks later in review — so we catch drift while contributors can still re-shoot.

Ego · exo · dexterity · audio
04

Quality control

Three concentric layers of QC: automated heuristics catch the obvious failures, peer reviewers validate edge cases, and an independent audit verifies statistical quality on a sampled basis. Nothing ships without all three.

95.2% accept rate
05

Tech stack & integrations

API-first architecture with platform integrations for the tools your team already uses. Hosting on your cloud or ours; delivery in any of seven formats; pre-signed URLs, S3, or sFTP — your call.

API-first · webhook-driven
06

Dataset delivery

Regular delivery cadences with detailed reporting and insights — what arrived, what was rejected and why, what the quality envelope looks like over time. Lands clean in your ML pipeline; we stay on call for follow-up batches.

Weekly · biweekly · custom
Why this pipeline

The boring word for it is defensible.

Typical pipeline

Speed first, questions later

  • Anonymous crowd workers, no gating
  • QC happens after delivery, in your pipeline
  • Rights status: "best effort"
  • Reporting: a CSV, if you ask
DataDensity

Defensible at every stage

  • Verified contributors, multi-stage gating
  • Three-layer QC, before anything ships
  • Rights cleared, readable license
  • Live quality dashboard + raw audit logs
Quality Control

Three layers. Nothing slips.

Layer 01 · Auto

Automated heuristics

Bitrate, framing, audio level, language ID, duplicate detection, sensor sanity checks. Runs on upload — feedback in seconds.

Layer 02 · Peer

Peer review

Trusted contributors validate edge cases — language nuance, scene labels, cultural context. The judgements that need humans.

Layer 03 · Audit

Independent audit

An audit team outside the production line samples every batch and verifies statistical quality against the brief.

Tech Stack

Plug into the tools your team already runs.

API-first

REST + webhooks. Roll your own pipeline or hook into ours.

Flexible hosting

Your AWS, GCP, Azure — or sit on ours. Your call.

Multiple formats

Parquet, JSONL, raw media + sidecars. Seven ways out.

SOC 2 path

Audit logs, RBAC, data-retention controls included by default.

FAQ

Frequently Asked Questions

What kinds of data do you collect?

Multimodal training data — video (ego and exocentric), audio and voice, image, and sensor streams. Most volume today is in speech (22+ languages, 4,400+ hours/month) and egocentric video (cooking, industrial, domestic, tutorials).

If your spec isn't on the catalogue, we'll commission it: distributed contributors, custom prompts, your QC criteria.

Where do contributors come from?

10,000+ verified contributors across India today, with capacity to expand into specific regions, languages, or demographics as your collection requires. Every contributor is ID-checked and consented before they upload.

How are rights handled?

Every file ships with explicit commercial consent, license terms, and a verifiable contributor chain. Standard licenses are designed to be readable in one sitting. Custom terms available for enterprise contracts.

What's turnaround for a custom collection?

Sample within 5 business days. Full collections typically deliver in 2–6 weeks depending on volume, language coverage, and annotation complexity.

Do you handle annotation and QC?

Yes — multi-stage QC funnel from upload through to training-ready output. Annotation tiers cover transcription, phoneme alignment, emotion tagging, object/action labels, and custom schemas.

How do I become a contributor?

Open the contributor app, complete identity verification, and start with onboarding tasks. Payouts in INR or USDC within 2–7 days. Work from anywhere, no prior experience.

How do we get started?

Email hello@datadensity.com with what you're training. We'll come back within two business days with a sample, a quote, and a collection plan.