What is data collection for AI?

Data collection for AI is the deliberate gathering of training and evaluation data — speech, images, video, text, or synchronized sensor streams — from real contributors under a defined specification, with the rights to use it recorded up front. Done well it targets exactly the languages, demographics, devices, and edge cases a model needs, rather than whatever happened to be available to scrape.

What types of data can Firsthand collect?

Audio and speech, image, video (including egocentric first-person capture), text and language, and multimodal or sensor data. Egocentric video is our core specialism; the other modalities are collected through the same spec-first, consent-documented process. If your need does not fit a neat category, our custom-collection service designs the program around it.

How is this different from buying an off-the-shelf dataset?

Off-the-shelf datasets give you whatever was collected for someone else — usually with unclear licensing and coverage that does not match your problem. Custom collection starts from your spec: the exact languages, conditions, devices, and edge cases you need, collected by paid contributors with signed consent, and delivered with a buyer-owned commercial license and a visible reject log.

Where do you collect data?

Worldwide. Sourcing runs through a vetted global crowd across 150+ countries and 500+ languages, so we can reach speakers, regions, devices, and demographics that generic datasets miss. Thin or under-served segments take longer to staff, and we give you the realistic timeline before you commit rather than promising coverage we cannot yet reach.

Solutions

Data collection for AI, built around your spec.

Firsthand started in the hardest corner of this problem — synchronized, first-person video for embodied AI. The same discipline now runs across every modality: speech, images, video, text, and multimodal sensor data, collected from a vetted global crowd, to a spec you approve, with a consent chain you can show your counsel.

Modalities: Audio · Image · Video · Text · Multimodal
Reach: 150+ countries
Languages: 500+ locales
License: Buyer-owned

A contributor speaks into a handheld audio recorder in a sunlit courtyard, an everyday real-world setting. — Collection happens where the data actually lives — real people, in real settings, sourced from a vetted global crowd.

What it is

Collected on purpose, not scraped by accident.

Data collection for AI is the deliberate gathering of training and evaluation data from real contributors, under a defined spec, with usage rights recorded before anything is gathered.

The alternative — bulk data pulled from the open web or bought off a shelf — is why teams throw away most of what they acquire: the licensing is murky, the coverage does not match the problem, and the edge cases that decide real-world accuracy are exactly what is missing. Custom collection inverts that. You name the languages, demographics, devices, environments, and failure cases; we source and capture against them.

Modalities

Five ways we collect, one standard behind them.

Each modality has its own page with the collection types, quality considerations, and answers specific to it.

A contributor wearing over-ear headphones speaks into a handheld field recorder fitted with a windscreen, in a lived-in room.

Audio & speechSpeech and audio data, recorded to your spec.Audio data collection is the process of recording human speech and other sounds from real contributors — under agreed conditions and with signed consent — so an AI system can be trained or evaluated on how people actually sound.Audio & speech collection →

A hand holds a smartphone photographing fresh produce at an outdoor market stall, the framed shot visible on screen.

ImageImage data that matches where your model runs.Image data collection is the sourcing of photographs from real contributors under a defined spec — covering the objects, conditions, devices, and diversity a computer-vision model needs — with consent recorded for any identifiable person.Image collection →

A person wearing a small head-mounted camera on an elastic strap chops vegetables at a kitchen counter.

VideoVideo of real people doing real tasks.Video data collection is the recording of moving-image footage from real contributors against a defined spec — activities, gestures, or first-person task views — with signed consent and controlled coverage of conditions.Video collection →

Hands typing on a laptop beside an open notebook filled with handwritten script, under a warm desk lamp.

Text & languageText written by the people you are modeling.Text data collection is the authoring or gathering of written language from real contributors against a defined spec — prompts, dialogue, translations, or domain text — with the rights to use it recorded up front.Text & language collection →

A multi-sensor head-mounted capture rig on a stand, with cameras, a depth sensor, and cabling, against a dark studio background.

Multimodal & sensorMultiple streams, captured on one clock.Multimodal data collection is the simultaneous capture of two or more aligned data streams — for example video, depth, audio, and motion — from the same event, time-synchronized so a model can learn the relationships between them.Multimodal & sensor collection →

A coordinator with a clipboard briefs a contributor wearing a capture device in a workshop setting.

CustomA collection program built around a need that fits no category.When the data you need does not exist yet, we design the whole program — sourcing, spec, capture, QA, and delivery — around it, anywhere in the world.Custom collection →

Egocentric first-person video is our core specialism — it has its own data catalog and capture methodology.

How we run it

The same standards, whatever the modality.

These are the commitments that make a batch usable. Several of them cost us money on purpose — they are the reason the data survives contact with a model.

Collected to a written spec: Nothing is gathered speculatively. We agree the target — languages, demographics, devices, environments, edge cases — before a single contributor is briefed.
Contributors paid hourly: People are paid for their time, including setup and retakes, not a bounty per item. A per-item rate optimises for volume and quietly wrecks quality.
Documented, informed consent: Every contributor signs a release granting the usage rights you need before collection begins. You receive the consent artefacts with the batch.
Discard rather than downgrade: If an item cannot meet the spec or a bystander cannot be de-identified, it is dropped — not shipped at a discount to pad the count.
Multi-layer QA with a visible reject log: Automated checks plus human review, and you see the reject reasons, not just the accepted items.
Buyer-owned commercial license: You receive a perpetual, buyer-owned license with a data card recording jurisdictions of capture and the consent basis.

Reach

Worldwide, without borrowing anyone's headcount.

Collection is sourced through a vetted global crowd across 150+ countries and 500+ languages. We describe that reach qualitatively on purpose — a contributor count is easy to print and impossible for you to audit, so we point you at what matters instead: whether we can reach the specific speakers, regions, and devices your project needs.

For deep segments — common languages, mainstream devices, everyday environments — collection starts quickly. For thin or under-served ones — low-resource languages, specific clinical or industrial settings, rare demographics — staffing is the bottleneck, and we quote a realistic timeline rather than a hopeful one. Either way, you get the estimate before you commit.

FAQ

Questions buyers ask first.

What is data collection for AI?: Data collection for AI is the deliberate gathering of training and evaluation data — speech, images, video, text, or synchronized sensor streams — from real contributors under a defined specification, with the rights to use it recorded up front. Done well it targets exactly the languages, demographics, devices, and edge cases a model needs, rather than whatever happened to be available to scrape.
What types of data can Firsthand collect?: Audio and speech, image, video (including egocentric first-person capture), text and language, and multimodal or sensor data. Egocentric video is our core specialism; the other modalities are collected through the same spec-first, consent-documented process. If your need does not fit a neat category, our custom-collection service designs the program around it.
How is this different from buying an off-the-shelf dataset?: Off-the-shelf datasets give you whatever was collected for someone else — usually with unclear licensing and coverage that does not match your problem. Custom collection starts from your spec: the exact languages, conditions, devices, and edge cases you need, collected by paid contributors with signed consent, and delivered with a buyer-owned commercial license and a visible reject log.
Where do you collect data?: Worldwide. Sourcing runs through a vetted global crowd across 150+ countries and 500+ languages, so we can reach speakers, regions, devices, and demographics that generic datasets miss. Thin or under-served segments take longer to staff, and we give you the realistic timeline before you commit rather than promising coverage we cannot yet reach.

Tell us what you need collected.

Bring the spec, or the problem you cannot find data for. You will get a scoped estimate — modality, reach, timeline, and price — before any commitment.

Request a collection →See egocentric data