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AI data collection outsourcing: proving rights to your training data
A rights audit of 4.2 million training samples (an enterprise AI company) found only 64% provably cleared for their actual use and 310,000 biometric samples without biometric-grade consent. Vetted teams treat consent as a versioned per-sample instrument — commercial training named as the use, revocation propagated within five days, a queryable ledger. Pick any sample and ask for its consent record in minutes.
Collection Partners
Collected / Year
Delivery Hubs
With training data, unlicensed samples or an unrepresentative corpus don’t cost you a ticket — they bias the model, break coverage and create licensing risk. Collection here is a rights-and-representativeness function, judged on validation and integrity, not keystrokes per hour.
Five stages from sourcing to rights-cleared dataset — click where yours leaks.
Every stage can fail in its own way, and the failure travels — ending as a corrupted dataset or a wrong call downstream. Select a stage to see the work, the control, and the metric that governs it.
AI data-collection operations run the full collection lifecycle — collection design and sourcing, contributor recruitment, consent management, coverage and representativeness validation, and rights-cleared dataset packaging — under consent and coverage QA, measured by consent compliance and representativeness, not raw volume.
“With training data, the corpus and the model are the same conversation. A biased or unlicensed dataset doesn’t annoy anyone today — it surfaces months later as a biased model and a licensing risk. That is why consent and coverage, not raw sample volume, are the only metrics that matter here.”
A data broker vs. a collection team that protects your model and your license.
Seven dimensions, read as risk vs. rigor — what an unlicensed data broker exposes versus what a rights-cleared collection team safeguards.
Where does the value come from when samples are consented, representative and rights-cleared the first time?
From four streams a per-sample rate ignores: faster dataset creation, acquisition cost reduced, better model performance from representative data, and collection labor arbitrage. The cheapest sample is the one collected right and rights-cleared the first time — and the decision it keeps sound.
Illustration, not a client result: 30 FTEs × 1,920 paid hours at about $10.00/hour ($19,200 per FTE per year) plus a 10% transition provision, against $60,000 per FTE onshore — a modelled difference of $1.17M. The bars above are value categories to evaluate with your own data, not booked benefits.
A validated-data operation live in 8 weeks — accuracy proven before scale.
A gated stand-up. No dataset ships until consent and coverage validation are signed off and a parallel run reconciles clean against source.
Every sample carries its consent version, its permitted uses, and its revocation path. 100% or quarantine — there is no 99.8% consented, only 0.2% radioactive.
Faces and voices aren’t just PII; in a growing set of jurisdictions they’re biometric identifiers, and the consent bar is the highest in data law. A consent record that can’t answer “may this sample train a commercial model, forever, sublicensably?” is a release form, not a rights architecture.
Drafted with your counsel per jurisdiction and per modality (voice and face at biometric grade where BIPA-class statutes apply), covering what the training use actually raises: commercial model training named as the use, derivative and sublicense rights explicit, retention and deletion stated — and every sample stamps the consent VERSION it was collected under, so when the terms improve in month four, the corpus says which samples carry which rights instead of averaging them into a hope.
A contributor’s withdrawal is a right, not an inconvenience: the revocation path is published, requests route to a governed queue, affected samples quarantine from all future dataset builds within 5 days, and the deletion certificate goes to you with the affected-dataset list — because “we removed it” without naming which shipped datasets contain it is a promise about the future wearing a statement about the past. What revocation means for already-trained models is your counsel’s call; our job is making the corpus answerable.
Release, version, contributor metadata (demographic fields held under the same consent), permitted uses, revocation status — queryable, exportable, and built for the day a regulator, an acquirer’s diligence team, or a licensing counterparty asks.
The sampling plan states its cells — language × accent × age × environment — and the dashboard shows fill rates live, because a coverage gap discovered at delivery is a model gap discovered in production.
Target distributions per dimension (and the interactions that matter: the elderly-speaker-noisy-environment cell ASR actually fails on), minimum cell counts justified against model needs, hard-to-fill cells flagged at design time with recruitment strategies attached — because the expensive cells are exactly the ones convenience sampling skips, which is how scraped corpora got biased in the first place.
The coverage dashboard is the program’s heartbeat: cells filling, cells lagging, recruitment re-weighted weekly toward the gaps — the gap chased before delivery, not discovered at it.
Coverage reports state per-cell attainment against target — including the cells that missed — because a dataset delivered at “97% coverage” with the misses unnamed is a model bias with a bow on it. Where a cell can’t be filled ethically or practically, the coverage “no” ships in the documentation, and your team decides with eyes open.
The contributor is a counterparty, not a source — the supply chain, the ethics surface, and the fraud surface at once.
Contributor compensation meets stated floors per market and task type — a dataset built on exploitative rates is an ESG finding wearing a cost advantage, and underpaid contributors are exactly the ones who game the quotas. Device and identity signals, duplicate-contributor detection, spot re-verification: the contributor who is secretly four accounts fills your demographic cells with fiction — coverage fraud is the representativeness killer, and it’s caught at the ledger, not the dashboard.
Tasks involving personal narrative, health-adjacent, or emotionally loaded content run under the content-moderation welfare standard: informed task descriptions, opt-outs, support paths. And recruitment pipelines are managed like the wing’s other counterparties — documented cadences, per-market playbooks, retention economics — because the fourth follow-up that fills the hard cell is the same fourth follow-up the whole family runs on.
Every delivered corpus ships with its provenance card — because a corpus that can’t say which of its samples were manufactured is a provenance question wearing a volume number.
Collection methodology and dates, the quota frame and per-cell attainment, consent-version distribution and permitted-use summary, known limitations and unfilled cells, contributor-pool characteristics at aggregate, and the synthetic line — any synthetic or augmented samples labeled at the sample level and summarized at the corpus level, never blended silently. The datasheet is versioned with the dataset; downstream teams, auditors, and licensing counterparties read the same papers.
The collection teams we vet work under your counsel’s instruments and deliver rights you can prove. The legal determinations stay legal.
Jurisdiction analysis, biometric-statute applicability, and the revocation-vs-trained-model question are legal determinations routed with the facts attached (Section 1), never improvised by an ops team.
Vetted collection teams gather NEW data from consenting contributors; mining EXISTING sources — with its own lawfulness gate — lives at our data mining & processing page. A collection vendor quietly “supplementing” with scraped data has laundered the exact risk you hired them to retire.
Augmentation is a labeled tool (Section 4), never a quota shortcut — a manufactured sample that reports itself as collected is a provenance lie with a use case.
The consent ledger and demographic metadata run under the same ISO 27001 / SOC 2 Type II regime as the samples. Locale count and modality mix cap the span; launch-driven collection sprints ride pre-recruited contributor pools.
Indicative 2026 rates — the collection bench shown apart from the seat (contributor compensation is separate, floored, and disclosed).
The two premium rows have no commodity equivalent because a data broker staffs neither: consent is a master affidavit and coverage is whatever showed up. Rates confirmed per engagement against modalities, locales, and cell difficulty.
Four kinds of corpus, collected four different ways.
The flagship’s home: 12 languages, the accents that finally worked, three new markets shipped. The speech-AI developer is this corpus, measured.
Image/video collection at biometric-grade consent — the modality where the ledger matters most.
Commissioned corpora with provable provenance — the alternative to the scraping question, documented.
Health-adjacent, financial, and public-sector datasets: consent at its strictest, welfare governance on, papers audit-ready.
Rights audit only — 4.2M training samples, provenance-graded. The question 2026 keeps asking louder: how much of your training set can you prove you’re allowed to use?
Enterprise AI company, live corpus retained, 4.2M samples across 31 sources and 4 modalities in scope. Identity withheld under NDA.
The corpus had accreted the way corpora do: early scrapes from the move-fast era, licensed sets whose terms nobody re-read after renewal, vendor deliveries with master affidavits and no per-sample records, an acquisition’s data estate absorbed unexamined, and internal collections under consent language written before “train a commercial model” was a sentence anyone drafted for. The precipitating pressures were 2026’s: a licensing dispute in the industry news, an enterprise customer’s diligence questionnaire asking for training-data provenance, a biometric-statute demand letter one competitor over. The corpus performed beautifully; nobody could produce its papers.
A ring-fenced provenance grading — the corpus untouched, models untouched. Stratified sampling per source lineage, each sample graded on the rights ladder: provably consented (per-sample records, uses covering the actual training use), licensed-with-terms-risk (agreements located, but scope/derivative/sublicense terms diverging from actual use — the re-read nobody did, done), affidavit-only (vendor assurances with no per-sample evidence — confidence, not rights), biometrically exposed (face/voice samples in BIPA-class jurisdictions without biometric-grade releases — the highest-severity stratum, sized precisely), and unknown provenance (the early scrapes — quantified, not euphemized). Findings routed to counsel with the remediation map: re-consent where contributors are reachable, license renegotiation where terms drifted, quarantine-from-future-builds where nothing else is honest — our job is the grading and the facts; the legal calls are counsel’s.
Quality and rights are the two examinations every training corpus needs — and in 2026 the rights side is a sales blocker before it’s a legal one: the audit converts a diligence questionnaire from a scramble into an export. The second row is the severity anchor (biometric exposure is the stratum with statutory damages attached), and the quick test has a subpoena’s shadow on it: pick ten samples from your flagship model’s training set and produce their papers by Friday. If the plan is to ask the vendor — the vendor’s plan is the affidavit, and the affidavit is the finding.
How to choose an AI data collection provider in the Philippines
Three controls separate a rights-cleared collection team from a data broker — and each is demonstrable before you sign. The cost of a wrong record is never just the record: it is the corrupted dataset and the decision downstream.
“Give a prospective partner a sample batch and ask for the consent records and coverage report — signed releases, license terms, demographic breakdown. A rights-cleared collection team catches nearly all of them. A data broker sells right past them, and three months later a model is biased and a license is disputed and no one knows why.”
The corpus-fitness standard: the economics of AI data collection outsourcing.
Why samples collected is a volume vanity metric, how corpus fitness — coverage of the target distribution, label validity and clean provenance — never collection throughput, decides the true cost of a training-data operation once off-distribution samples, mislabeled data, consent gaps and re-collection are counted. Part of PITON-Global’s Executive White Paper Series, by John Maczynski and Ralf Ellspermann.
Where the AI data-collection conversation is happening.
Hidden champions: the six to ten providers you’d otherwise have missed, lined up in 48 to 72 hours. Judge for yourself. No cost, no obligation.
What AI and ML leaders ask before they outsource data collection.
The questions that decide a data-collection engagement, answered thoroughly by the principals accountable for them.
Going deeper on sourcing training data you can defend
Collection is the first stage of the training-data pipeline, and our hub for AI and machine learning companies shows how it hands off to annotation, fine-tuning and evaluation as the program grows. The page above explains how a consented, rights-cleared corpus is built. The notes below cover the decisions around it: how collection and curation fit together, how to audit what you already hold, how to protect data and models once they leave your walls, and how to decide where and whether to hand the work over.
Collection and curation
Collection and curation are two jobs. Collection brings in new samples against a sampling plan; curation decides which of them, and which of your existing data, are fit to train on. Write both into the same statement of work so that nothing is gathered without a place in the plan and nothing is kept without a reason. Our guide to sourcing the raw material for intelligent systems covers the collection side, and refining raw information into model-ready data explains filtering, de-duplication and balance checks.
Speech is the most common collection brief, and accent and noise coverage decide whether a voice model works for real users. Read how teams approach training speech recognition on real callers before you set the sampling cells. Once samples arrive, labeling usually follows on the same bench; our data annotation and labeling page covers how that work is quality-controlled.
Auditing the data you already have
Most AI companies hold more data than they can prove they may use. Before commissioning new collection, grade the existing corpus for consent, license terms and provenance, and quarantine what fails. Our note on auditing the integrity of a training-data pipeline sets out the method.
Security, privacy and intellectual property
Collected data and the models trained on it are both assets worth stealing. Ask every vendor where samples are stored, who can see them, how contributor identities are separated from the data and what happens to copies when the engagement ends. Read how to reduce risk at the infrastructure layer and how to protect IP and model security offshore for the controls to write into the contract.
Multimodal data collection: keeping modalities aligned
Multimodal models learn from data where several modalities describe the same moment: an image and its caption, a video and its transcript, speech and the scene it describes, or camera frames synchronized with other sensors. The hard part of collecting it is alignment. Every pair or bundle needs matching timestamps, consistent identifiers and metadata that records how and where it was captured, or the model learns from mismatched examples.
Collection teams design capture protocols that record all modalities together, check alignment on a sample of every batch and track consent per contributor across every modality they appear in. Coverage plans set targets for languages, accents, settings and demographics so the dataset reflects the people and places the model will serve. Once collected, multimodal sets usually move to annotation and then to evaluation, and gaps that are too rare to capture can be filled with synthetic data.
Image and video data collection in the field
Image and video collection means capturing new footage to a specification: objects, scenes, gestures, products or activities, under defined lighting, angles, distances and devices. Field teams follow written capture protocols, use approved devices with fixed settings, record session metadata and upload through secure channels. Diversity quotas for skin tones, ages, environments and conditions are tracked as collection progresses, so gaps are visible before the budget is spent.
People who appear in footage sign releases, bystanders are avoided or blurred and faces and license plates are handled under the client’s privacy rules. Quality reviewers reject blurred, mislabeled or off-spec captures early. For robotics and driving programs, collection follows the same discipline described on our robotics training data and autonomous vehicle data labeling pages.
Text and document data collection
Language models and document AI need text that is relevant, varied and legally usable. Text collection covers commissioned writing, such as prompts, questions, instructions and domain explanations from screened writers, and sourcing of real-world documents such as forms, invoices, contracts and reports. Writers follow style and diversity guidelines, and every item is checked for originality, accuracy and policy compliance before it is accepted.
Document sourcing needs extra care. Personal and confidential data is redacted or replaced, licenses and permissions are recorded and document types are balanced so the model sees enough variety. Collected text feeds LLM fine-tuning and document collections feed document processing models. Extraction from existing public sources is a different service, covered on our data mining and processing page.
Deciding where and whether to hand it over
Collection is labor-intensive and seasonal, which makes it a natural fit for an external team, but not every program should move. Our comparison of building in-house versus using a provider lays out the trade-offs, and choosing between Manila, Cebu and Clark helps you pick the site once you decide to move. For context on why AI companies already rely on Filipino teams, read how the country supports leading AI companies.
Robotics teams gathering demonstration and sensor data will find that side of the work on our robotics hub, and the autonomous vehicles hub covers driving-data programs.
AI data collection outsourcing: proving rights to your training data
A rights audit of 4.2 million training samples (an enterprise AI company) found only 64% provably cleared for their actual use and 310,000 biometric samples without biometric-grade consent. Vetted teams treat consent as a versioned per-sample instrument — commercial training named as the use, revocation propagated within five days, a queryable ledger. Pick any sample and ask for its consent record in minutes.
Collection Partners
Collected / Year
Delivery Hubs
With training data, unlicensed samples or an unrepresentative corpus don’t cost you a ticket — they bias the model, break coverage and create licensing risk. Collection here is a rights-and-representativeness function, judged on validation and integrity, not keystrokes per hour.
Five stages from sourcing to rights-cleared dataset — click where yours leaks.
Every stage can fail in its own way, and the failure travels — ending as a corrupted dataset or a wrong call downstream. Select a stage to see the work, the control, and the metric that governs it.
AI data-collection operations run the full collection lifecycle — collection design and sourcing, contributor recruitment, consent management, coverage and representativeness validation, and rights-cleared dataset packaging — under consent and coverage QA, measured by consent compliance and representativeness, not raw volume.
“With training data, the corpus and the model are the same conversation. A biased or unlicensed dataset doesn’t annoy anyone today — it surfaces months later as a biased model and a licensing risk. That is why consent and coverage, not raw sample volume, are the only metrics that matter here.”
A data broker vs. a collection team that protects your model and your license.
Seven dimensions, read as risk vs. rigor — what an unlicensed data broker exposes versus what a rights-cleared collection team safeguards.
Where does the value come from when samples are consented, representative and rights-cleared the first time?
From four streams a per-sample rate ignores: faster dataset creation, acquisition cost reduced, better model performance from representative data, and collection labor arbitrage. The cheapest sample is the one collected right and rights-cleared the first time — and the decision it keeps sound.
Illustration, not a client result: 30 FTEs × 1,920 paid hours at about $10.00/hour ($19,200 per FTE per year) plus a 10% transition provision, against $60,000 per FTE onshore — a modelled difference of $1.17M. The bars above are value categories to evaluate with your own data, not booked benefits.
A validated-data operation live in 8 weeks — accuracy proven before scale.
A gated stand-up. No dataset ships until consent and coverage validation are signed off and a parallel run reconciles clean against source.
Every sample carries its consent version, its permitted uses, and its revocation path. 100% or quarantine — there is no 99.8% consented, only 0.2% radioactive.
Faces and voices aren’t just PII; in a growing set of jurisdictions they’re biometric identifiers, and the consent bar is the highest in data law. A consent record that can’t answer “may this sample train a commercial model, forever, sublicensably?” is a release form, not a rights architecture.
Drafted with your counsel per jurisdiction and per modality (voice and face at biometric grade where BIPA-class statutes apply), covering what the training use actually raises: commercial model training named as the use, derivative and sublicense rights explicit, retention and deletion stated — and every sample stamps the consent VERSION it was collected under, so when the terms improve in month four, the corpus says which samples carry which rights instead of averaging them into a hope.
A contributor’s withdrawal is a right, not an inconvenience: the revocation path is published, requests route to a governed queue, affected samples quarantine from all future dataset builds within 5 days, and the deletion certificate goes to you with the affected-dataset list — because “we removed it” without naming which shipped datasets contain it is a promise about the future wearing a statement about the past. What revocation means for already-trained models is your counsel’s call; our job is making the corpus answerable.
Release, version, contributor metadata (demographic fields held under the same consent), permitted uses, revocation status — queryable, exportable, and built for the day a regulator, an acquirer’s diligence team, or a licensing counterparty asks.
The sampling plan states its cells — language × accent × age × environment — and the dashboard shows fill rates live, because a coverage gap discovered at delivery is a model gap discovered in production.
Target distributions per dimension (and the interactions that matter: the elderly-speaker-noisy-environment cell ASR actually fails on), minimum cell counts justified against model needs, hard-to-fill cells flagged at design time with recruitment strategies attached — because the expensive cells are exactly the ones convenience sampling skips, which is how scraped corpora got biased in the first place.
The coverage dashboard is the program’s heartbeat: cells filling, cells lagging, recruitment re-weighted weekly toward the gaps — the gap chased before delivery, not discovered at it.
Coverage reports state per-cell attainment against target — including the cells that missed — because a dataset delivered at “97% coverage” with the misses unnamed is a model bias with a bow on it. Where a cell can’t be filled ethically or practically, the coverage “no” ships in the documentation, and your team decides with eyes open.
The contributor is a counterparty, not a source — the supply chain, the ethics surface, and the fraud surface at once.
Contributor compensation meets stated floors per market and task type — a dataset built on exploitative rates is an ESG finding wearing a cost advantage, and underpaid contributors are exactly the ones who game the quotas. Device and identity signals, duplicate-contributor detection, spot re-verification: the contributor who is secretly four accounts fills your demographic cells with fiction — coverage fraud is the representativeness killer, and it’s caught at the ledger, not the dashboard.
Tasks involving personal narrative, health-adjacent, or emotionally loaded content run under the content-moderation welfare standard: informed task descriptions, opt-outs, support paths. And recruitment pipelines are managed like the wing’s other counterparties — documented cadences, per-market playbooks, retention economics — because the fourth follow-up that fills the hard cell is the same fourth follow-up the whole family runs on.
Every delivered corpus ships with its provenance card — because a corpus that can’t say which of its samples were manufactured is a provenance question wearing a volume number.
Collection methodology and dates, the quota frame and per-cell attainment, consent-version distribution and permitted-use summary, known limitations and unfilled cells, contributor-pool characteristics at aggregate, and the synthetic line — any synthetic or augmented samples labeled at the sample level and summarized at the corpus level, never blended silently. The datasheet is versioned with the dataset; downstream teams, auditors, and licensing counterparties read the same papers.
The collection teams we vet work under your counsel’s instruments and deliver rights you can prove. The legal determinations stay legal.
Jurisdiction analysis, biometric-statute applicability, and the revocation-vs-trained-model question are legal determinations routed with the facts attached (Section 1), never improvised by an ops team.
Vetted collection teams gather NEW data from consenting contributors; mining EXISTING sources — with its own lawfulness gate — lives at our data mining & processing page. A collection vendor quietly “supplementing” with scraped data has laundered the exact risk you hired them to retire.
Augmentation is a labeled tool (Section 4), never a quota shortcut — a manufactured sample that reports itself as collected is a provenance lie with a use case.
The consent ledger and demographic metadata run under the same ISO 27001 / SOC 2 Type II regime as the samples. Locale count and modality mix cap the span; launch-driven collection sprints ride pre-recruited contributor pools.
Indicative 2026 rates — the collection bench shown apart from the seat (contributor compensation is separate, floored, and disclosed).
The two premium rows have no commodity equivalent because a data broker staffs neither: consent is a master affidavit and coverage is whatever showed up. Rates confirmed per engagement against modalities, locales, and cell difficulty.
Four kinds of corpus, collected four different ways.
The flagship’s home: 12 languages, the accents that finally worked, three new markets shipped. The speech-AI developer is this corpus, measured.
Image/video collection at biometric-grade consent — the modality where the ledger matters most.
Commissioned corpora with provable provenance — the alternative to the scraping question, documented.
Health-adjacent, financial, and public-sector datasets: consent at its strictest, welfare governance on, papers audit-ready.
Rights audit only — 4.2M training samples, provenance-graded. The question 2026 keeps asking louder: how much of your training set can you prove you’re allowed to use?
Enterprise AI company, live corpus retained, 4.2M samples across 31 sources and 4 modalities in scope. Identity withheld under NDA.
The corpus had accreted the way corpora do: early scrapes from the move-fast era, licensed sets whose terms nobody re-read after renewal, vendor deliveries with master affidavits and no per-sample records, an acquisition’s data estate absorbed unexamined, and internal collections under consent language written before “train a commercial model” was a sentence anyone drafted for. The precipitating pressures were 2026’s: a licensing dispute in the industry news, an enterprise customer’s diligence questionnaire asking for training-data provenance, a biometric-statute demand letter one competitor over. The corpus performed beautifully; nobody could produce its papers.
A ring-fenced provenance grading — the corpus untouched, models untouched. Stratified sampling per source lineage, each sample graded on the rights ladder: provably consented (per-sample records, uses covering the actual training use), licensed-with-terms-risk (agreements located, but scope/derivative/sublicense terms diverging from actual use — the re-read nobody did, done), affidavit-only (vendor assurances with no per-sample evidence — confidence, not rights), biometrically exposed (face/voice samples in BIPA-class jurisdictions without biometric-grade releases — the highest-severity stratum, sized precisely), and unknown provenance (the early scrapes — quantified, not euphemized). Findings routed to counsel with the remediation map: re-consent where contributors are reachable, license renegotiation where terms drifted, quarantine-from-future-builds where nothing else is honest — our job is the grading and the facts; the legal calls are counsel’s.
Quality and rights are the two examinations every training corpus needs — and in 2026 the rights side is a sales blocker before it’s a legal one: the audit converts a diligence questionnaire from a scramble into an export. The second row is the severity anchor (biometric exposure is the stratum with statutory damages attached), and the quick test has a subpoena’s shadow on it: pick ten samples from your flagship model’s training set and produce their papers by Friday. If the plan is to ask the vendor — the vendor’s plan is the affidavit, and the affidavit is the finding.
How to choose an AI data collection provider in the Philippines
Three controls separate a rights-cleared collection team from a data broker — and each is demonstrable before you sign. The cost of a wrong record is never just the record: it is the corrupted dataset and the decision downstream.
“Give a prospective partner a sample batch and ask for the consent records and coverage report — signed releases, license terms, demographic breakdown. A rights-cleared collection team catches nearly all of them. A data broker sells right past them, and three months later a model is biased and a license is disputed and no one knows why.”
The corpus-fitness standard: the economics of AI data collection outsourcing.
Why samples collected is a volume vanity metric, how corpus fitness — coverage of the target distribution, label validity and clean provenance — never collection throughput, decides the true cost of a training-data operation once off-distribution samples, mislabeled data, consent gaps and re-collection are counted. Part of PITON-Global’s Executive White Paper Series, by John Maczynski and Ralf Ellspermann.
Where the AI data-collection conversation is happening.
What AI and ML leaders ask before they outsource data collection.
The questions that decide a data-collection engagement, answered thoroughly by the principals accountable for them.
Going deeper on sourcing training data you can defend
Collection is the first stage of the training-data pipeline, and our hub for AI and machine learning companies shows how it hands off to annotation, fine-tuning and evaluation as the program grows. The page above explains how a consented, rights-cleared corpus is built. The notes below cover the decisions around it: how collection and curation fit together, how to audit what you already hold, how to protect data and models once they leave your walls, and how to decide where and whether to hand the work over.
Collection and curation
Collection and curation are two jobs. Collection brings in new samples against a sampling plan; curation decides which of them, and which of your existing data, are fit to train on. Write both into the same statement of work so that nothing is gathered without a place in the plan and nothing is kept without a reason. Our guide to sourcing the raw material for intelligent systems covers the collection side, and refining raw information into model-ready data explains filtering, de-duplication and balance checks.
Speech is the most common collection brief, and accent and noise coverage decide whether a voice model works for real users. Read how teams approach training speech recognition on real callers before you set the sampling cells. Once samples arrive, labeling usually follows on the same bench; our data annotation and labeling page covers how that work is quality-controlled.
Auditing the data you already have
Most AI companies hold more data than they can prove they may use. Before commissioning new collection, grade the existing corpus for consent, license terms and provenance, and quarantine what fails. Our note on auditing the integrity of a training-data pipeline sets out the method.
Security, privacy and intellectual property
Collected data and the models trained on it are both assets worth stealing. Ask every vendor where samples are stored, who can see them, how contributor identities are separated from the data and what happens to copies when the engagement ends. Read how to reduce risk at the infrastructure layer and how to protect IP and model security offshore for the controls to write into the contract.
Multimodal data collection: keeping modalities aligned
Multimodal models learn from data where several modalities describe the same moment: an image and its caption, a video and its transcript, speech and the scene it describes, or camera frames synchronized with other sensors. The hard part of collecting it is alignment. Every pair or bundle needs matching timestamps, consistent identifiers and metadata that records how and where it was captured, or the model learns from mismatched examples.
Collection teams design capture protocols that record all modalities together, check alignment on a sample of every batch and track consent per contributor across every modality they appear in. Coverage plans set targets for languages, accents, settings and demographics so the dataset reflects the people and places the model will serve. Once collected, multimodal sets usually move to annotation and then to evaluation, and gaps that are too rare to capture can be filled with synthetic data.
Image and video data collection in the field
Image and video collection means capturing new footage to a specification: objects, scenes, gestures, products or activities, under defined lighting, angles, distances and devices. Field teams follow written capture protocols, use approved devices with fixed settings, record session metadata and upload through secure channels. Diversity quotas for skin tones, ages, environments and conditions are tracked as collection progresses, so gaps are visible before the budget is spent.
People who appear in footage sign releases, bystanders are avoided or blurred and faces and license plates are handled under the client’s privacy rules. Quality reviewers reject blurred, mislabeled or off-spec captures early. For robotics and driving programs, collection follows the same discipline described on our robotics training data and autonomous vehicle data labeling pages.
Text and document data collection
Language models and document AI need text that is relevant, varied and legally usable. Text collection covers commissioned writing, such as prompts, questions, instructions and domain explanations from screened writers, and sourcing of real-world documents such as forms, invoices, contracts and reports. Writers follow style and diversity guidelines, and every item is checked for originality, accuracy and policy compliance before it is accepted.
Document sourcing needs extra care. Personal and confidential data is redacted or replaced, licenses and permissions are recorded and document types are balanced so the model sees enough variety. Collected text feeds LLM fine-tuning and document collections feed document processing models. Extraction from existing public sources is a different service, covered on our data mining and processing page.
Deciding where and whether to hand it over
Collection is labor-intensive and seasonal, which makes it a natural fit for an external team, but not every program should move. Our comparison of building in-house versus using a provider lays out the trade-offs, and choosing between Manila, Cebu and Clark helps you pick the site once you decide to move. For context on why AI companies already rely on Filipino teams, read how the country supports leading AI companies.
Robotics teams gathering demonstration and sensor data will find that side of the work on our robotics hub, and the autonomous vehicles hub covers driving-data programs.