End-to-end pipeline
Seven stages, one accountable owner.
- Stages
- 7, one contract
- Reach
- 150+ countries
- You approve
- The spec, first
- You own
- The delivered data
Stage by stage
What happens, and what you get.
Each stage has an owner, an explicit output, and a bar it clears before the next one starts. Nothing moves forward on a handshake.
- 01Discovery
We start from the failure, not a schema. You describe what you are training and where it breaks.
- A working session on the model, the task, and the exact conditions it fails in today
- We map the gap to a modality, a population, and the edge cases that decide accuracy
- No spec required from you yet — turning the problem into one is our job, not a prerequisite
Output — A shared problem statement and a first read on feasibility, reach, and rough timeline.
- 02Specification
The problem becomes a written, testable spec you approve before anyone is briefed.
- Modality, languages, demographics, devices, environments, volume, and edge-case coverage
- Explicit accept and reject criteria — the bar an item must clear to be billable
- Delivery format, metadata schema, and the consent and licensing basis, agreed up front
Output — A signed specification with accept/reject criteria — the contract the whole pipeline is measured against.
- 03Sourcing & staffing
We recruit and screen the exact contributors the spec calls for, wherever they are.
- Recruitment through a vetted global crowd across 150+ countries and 500+ languages and locales
- Screening against the spec — language, accent, age band, device, environment, expertise
- Thin or under-served segments get a realistic staffing timeline, not a hopeful one
Output — A screened, consented contributor pool matched to every cell of the spec.
- 04Capture
Contributors are briefed, paid hourly, and collected against the spec — with the right rig where it matters.
- Briefing and paid practice runs so people know what "good" looks like before the real take
- Specialized hardware where the project needs it — including our synchronized egocentric rig
- Contributors paid for their time, including setup and retakes, never a per-item bounty
Output — Raw captures collected to the spec, with capture-side metadata and session records.
- 05Annotation & structuring
Raw capture is labelled, transcribed, or structured into the schema your loader expects.
- Transcription, labelling, keypoints, segmentation, or event tags as the spec requires
- A second reviewer on a sampled or full pass depending on the density and stakes
- Structured into your delivery schema — not a folder of files you have to wrangle
Output — Labelled, schema-conformant data with the annotation guidelines that produced it.
- 06QA & anonymization
Automated checks plus human review against the accept criteria, with a visible reject log.
- Every item measured against the signed accept/reject criteria, not a vibe
- Faces, plates, and screens detected and irreversibly blurred, confirmed by a reviewer
- Items that miss are rejected and recaptured — you see the reject reasons, not just the passes
Output — A validated set plus the reject log and an anonymization record you can audit.
- 07Licensed delivery
Data ships in your format with the consent artefacts and a buyer-owned commercial license.
- Delivery in your format — RLDS, LeRobot, WebDataset, HDF5, zarr, Rerun, WAV, JSONL, and more
- Per-item metadata, the signed consent releases, and a data card recording jurisdictions
- A perpetual, buyer-owned license, with exclusivity where you need it collected only for you
Output — An owned dataset with a defensible provenance trail — ready to train on, and to show your counsel.
Why one owner
The failures live in the handoffs.
Most bad AI data is well collected and then wrecked between vendors. Owning the whole chain is how the standard — and the consent trail — survives to delivery.
One owner for the whole chain. Sourcing, capture, annotation, QA, and licensing are one accountable pipeline. When something is wrong you have one team to call, not a crowd platform blaming a labeling vendor blaming a tool.
The spec is enforced at every stage. Because the same team writes the spec and ships the data, the accept/reject criteria are applied end to end — not lost in a handoff between the people who collected it and the people who labelled it.
Provenance survives the whole journey. Consent is captured at sourcing and travels with the item through delivery. A stitched-together pipeline is where consent chains break; an end-to-end one is where they hold.
You get data, not a project to manage. No orchestrating vendors, reconciling formats, or chasing who owns the license. You brief once and receive an owned, structured, documented dataset.
FAQ
The pipeline, answered.
- What does end-to-end mean in data collection?
- It means one team owns every stage from the first problem statement to licensed delivery — discovery, spec, sourcing, capture, annotation, QA, anonymization, and hand-off — under a single contract. You are not assembling a crowd platform, a labelling vendor, and a QA tool and hoping they line up; the people who write the spec are the people who ship the data.
- How long does an end-to-end collection take?
- It depends on the modality and how rare the population is. Common languages, devices, and environments staff quickly; thin or under-served segments get a realistic timeline at the spec stage rather than an optimistic one. You approve a scoped estimate with reach, timeline, and price before any collection starts.
- What do I get at the end?
- A structured, schema-conformant dataset in your delivery format, the per-item metadata and signed consent releases, an anonymization record, a visible reject log, and a data card recording jurisdictions — all under a perpetual, buyer-owned commercial license.
- Can you start without a finished specification?
- Yes. Turning a problem into a written, testable spec with accept and reject criteria is stage two of the pipeline, not a prerequisite. You bring the failure; we write the spec and you approve it before anyone is briefed.
Bring the problem. We'll write the spec.
Describe the data you wish existed and you will get a scoped estimate — reach, timeline, and price — before any commitment, with one team accountable from spec to licensed delivery.