Egocentric data collection services, from spec to licensed delivery.

An egocentric data collection service covers the whole distance between "we need first-person human data for X" and a licensed, QA-passed batch sitting in your loader. That means turning a described gap into a written skill spec, running a small pilot against that spec, recruiting and capturing at the scale the program needs, reviewing every batch against the spec’s accept criteria, documenting contributor consent, anonymizing anything personally identifying, and delivering the result in a training-ready format under a commercial license.

It is a different shape of engagement than buying a dataset. A dataset is a fixed artifact, captured for whatever task distribution its original buyer needed. A service is an ongoing capability: you bring the gap in your current data, and the engagement is scoped, piloted, and scaled specifically to close it, then can be re-briefed as new gaps show up during training.

Teams training robot foundation models and embodied AI systems hire egocentric data collection services when their current corpus, public or previously collected, does not cover the tasks, objects, or environments their model is now weak on. That spans pretraining programs that need broader skill and environment diversity, mid-training programs that need a smaller dataset closely aligned to one target embodiment, and post-training or fine-tuning programs chasing a specific failure mode a policy keeps hitting in evaluation.

What these buyers share is a known, specific gap rather than a general need for "more data." A service engagement is built around closing that gap on a written spec, which is why it fits a team that can describe what their model is missing far better than it fits a team that only wants a large generic corpus off the shelf.

The engagement starts with describing the gap: which tasks, objects, environments, or embodiments the current corpus does not cover. That becomes a written spec covering capture tier, annotation depth, geographic and demographic reach, and the accept criteria a batch has to clear, quoted for reach, timeline, and price before capture begins.

Most programs start with a Spec Pilot, a small, fast first batch that proves the spec and QA bar hold on real footage before volume scales. From there the collection runs through the same seven-stage pipeline used across every Firsthand program: contributor recruiting, capture, per-batch QA, anonymization, and licensed delivery, with rejected items recaptured at the provider’s cost. A median of 48 hours from signed spec to first validated batch applies where coverage is deep; where it is thin and contributors need to be recruited from scratch, a realistic timeline runs three to four weeks.

On-demand top-ups run against the same accept criteria afterward, so a program can keep closing new gaps that show up during training without renegotiating the spec from zero each time.

You bring the thing only you know: which tasks, objects, and environments your model is actually missing, and sign-off at each stage, spec, pilot, and scale decision. The service handles everything operational underneath that: turning the gap into a written spec with accept criteria, recruiting and briefing contributors, running capture against the spec, per-batch QA and rejected-item recapture, documenting contributor consent, anonymizing faces, plates, and screens, and delivering the finished data in your training pipeline’s format under a commercial license.

That division is what makes the engagement scale without turning into a second full-time job for your team. You are reviewing a pilot batch and signing off on volume, not managing contributor recruiting or chasing down consent paperwork.

Every batch is reviewed against the accept criteria written into the spec before it ships, not sampled after the fact. Footage that does not clear the bar is rejected and recaptured at the provider’s cost rather than shipped with a caveat, so the accept criteria set at the start of the engagement are the same bar every batch is held to at scale.

The Spec Pilot exists specifically so a buyer can check stream quality, annotation fidelity, and format compatibility with their own training pipeline on a small batch before volume ramps, rather than discovering a mismatch after a large capture run is already underway.

Every contributor signs a documented release as part of the consent chain, and that record is delivered alongside the data rather than kept as a one-time assurance. Faces, plates, and screens are detected and irreversibly blurred at ingest, and a human reviewer confirms the anonymization before anything ships, with the anonymization record included in delivery.

The finished data ships under a perpetual, buyer-owned commercial license, with exclusivity available so a given collection is never resold. That combination, a documented consent chain plus a license actually written for commercial training, is the piece several public egocentric corpora do not clear, since many ship research-only or without stated license terms at all.

Public egocentric corpora prove scale is achievable: EgoVerse reports roughly 1,362 hours across about 80,000 episodes, and Egocentric-10K is a 10,000-hour egocentric corpus released under an Apache 2.0 license. Those are useful as a research baseline or a starting prior, but they were captured for someone else’s task distribution, so they will always be missing exactly the tasks, objects, or environments a given model needs next.

A custom service closes that coverage gap directly, scoped to a written spec across the tasks a model is actually missing, rather than captured for general usefulness. It also tends to close a licensing gap: several large public corpora do not carry a license written for commercial training, while a custom collection ships under a perpetual, buyer-owned commercial license by default.

Neither path makes the other obsolete. A broad public corpus or a large pretraining-scale service collection is the right tool for building a general prior; a smaller, closely scoped custom collection is the right tool for filling a specific, known gap in coverage or clearing commercial licensing terms a public corpus cannot.

Once a spec is signed, a first validated batch lands in a median of 48 hours where coverage is deep, meaning enough contributors are already active in the needed environments and demographics. Where coverage is thin and contributors need to be recruited into a new region or population first, a realistic timeline is three to four weeks rather than days.

The Spec Pilot is what makes that first number meaningful: it is a small batch sized to prove the spec and QA bar quickly, so a buyer is validating the engagement on a fast, bounded first delivery rather than waiting on the full scaled volume before seeing anything.

Score candidates on measured sync error and method (not just "aligned"), annotation depth (3D hand pose, metric depth, and frame-accurate action segments, not just RGB), commercial license terms, a documented per-participant consent chain, a stated reject policy, billing per validated hour rather than per raw hour, realistic time-to-first-batch numbers for both deep and thin coverage, and whether the provider publishes an honest coverage histogram showing where the data is thin.

The most reliable way to compare vendors is to give each the same written skill spec and request a small sample against it, then score the delivered data on that checklist rather than a demo reel. The full evaluation checklist and a neutral vendor scorecard are linked below.

Start with the specific gap in your current data, not a target hour count: which tasks, objects, or environments is your model still weak on. That gap becomes the written spec, tasks, environments, regions, capture tier, annotation depth, and accept criteria, quoted for reach, timeline, and price up front.

A Spec Pilot proves that spec on a small, fast first batch before any commitment to volume, so stream quality, annotation fidelity, and format compatibility can be checked before scale. From there, the same pipeline scales the collection to the size the program needs, with on-demand top-ups available afterward as new gaps surface during training.