Guide for physical AI data collection companies · checked 12 November 2026
Software for physical AI data collection: partner sites, field days, kit and consented hours
Collecting real-world training data for robots is a field operation. Partner sites have to be surveyed before a team goes, capture techs need kit, consent and a working day, and every hour you deliver to a lab has to trace back to a task, a person, a device, a place and a signature. This guide covers how a deployment runs, what to record, the consent and battery rules, a worked example of recorded vs accepted hours, and the software, with a source on every rule.
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Short answer
Physical AI data collection companies usually run on tools their own engineers build (a capture app, an upload and QA pipeline, a lab delivery format), with spreadsheets, shared drives and chat groups for everything around them: partner sites, deployments, kit, consent, travel and payouts. The parts that break are the operational ones: sites signed but never activated, teams sent to sites that fail on power or a kit room, devices nobody can account for, consent that cannot be matched to a file, and orders that fall behind plan without anyone seeing it. Fieldproxy is one option for that layer: a configurable operations platform set up around your process, with an offline field app, consent and kit records tied to every session, and an API and MCP server so your own tools and Claude or ChatGPT can read and write the same records.
Art. 7(3)
GDPR: a person can withdraw consent at any time, and withdrawing must be as easy as giving it
source: GDPR Article 7, Conditions for consent (gdpr-info.eu)
Sec. 6(6)
India DPDP Act: after a withdrawal, the data fiduciary must stop processing and make its processors stop, within a reasonable time
Art. 10
EU AI Act: data governance for high-risk systems' training data covers the collection process and the origin of the data
source: EU AI Act, Article 10: Data and data governance (artificialintelligenceact.eu)
100 Wh
FAA: spare lithium-ion batteries fly in carry-on only, up to 100 Wh each without airline approval
source: FAA PackSafe, Lithium batteries in passenger baggage
Watch it end to end
A physical AI data collection business on Fieldproxy, start to paid
7 minutes of the real product, set up for a demo company (Tallyhand) with sample data.
How a job runs
One physical AI data collection job, from first call to paid
The order the work really happens in, and what has to be written down at each step so nothing is argued about later.
- 1
Turn a signed partner into an active site
The partnerships or field-sales team signs a factory, kitchen, laundry, warehouse, salon or hotel. Signing is not the same as being ready. Give every signed partner a named owner and a status (signed, survey booked, survey passed, first deployment booked, active, paused), so partners that were signed and then forgotten show up on a list instead of in someone's inbox.
- 2
Survey the site before any team is sent
Someone visits or video-calls the site and records the things that stop a field day: power at the floor where people will work, lighting good enough for the cameras, a lockable room for kit and charging, a named site contact with a phone number, which tasks and stations can be filmed, and the site's signed consent letter. A failed survey blocks the deployment until it is fixed.
- 3
Plan the deployment against a lab order
Labs order hours by task type, for example folding, dishwashing, picking or bed making. When an order is behind plan, pick a surveyed site that offers those tasks, set the dates and the target hours per task, and staff it: a site lead who is certified on the capture protocol, five or six capture techs, travel and hotels if the site is outstation.
- 4
Issue kit by serial at the depot
The depot scans out head cameras, wrist cameras, gloves, batteries, chargers and storage cards to the site lead against the deployment. Each serial now has a holder and a due-back date. Batteries over the airline limits travel separately or as cargo.
- 5
Start the day: check in, kit check, consent
Each tech checks in with a selfie and GPS at the site. The site lead confirms kit by serial. Every worker who will wear a device or appear on camera reads the notice in their own language and signs, with a photo, before their first session. A worker without consent cannot be put on a session. The app has to work with no signal on a factory floor.
- 6
Run timed sessions with quality checks
Each session records the task type, the worker, the device serials, the station and the start and stop times. The site lead spot-checks framing, lighting and lens fogging in the first minutes of each session and on a schedule after that, because a session filmed at the wrong angle is rejected later, after the team has gone home.
- 7
Offload and send the day report
At the end of the day, footage is offloaded from every device, with file counts checked against the sessions logged. The site lead files a day report: recorded hours by task against target, sessions flagged on site, devices running hot or missing, incidents, and expenses for travel, hotel and meals.
- 8
QA, then count accepted hours
Your QA team or pipeline reviews the footage and accepts or rejects each session with a reason (framing, occlusion, wrong task, sensor drift, missing consent). Accepted hours roll up to the lab order. Rejection rates by team, site lead and reason show who needs retraining.
- 9
Pay sites and contributors, invoice the lab
Partner sites are paid their per-hour fee on the hours you agreed to pay on. Contributors are paid for their sessions, and a payout is held when that person's consent is missing or incomplete. The lab is invoiced for delivered hours, and a revoked consent removes that person's sessions from the next delivery.
- 10
Return kit and close the deployment
Kit comes back to the depot and is scanned in by serial. Anything missing is chased with the person who last held it. Devices that ran hot are pulled to repair as a batch, so the same fault does not go out on the next deployment.
What to record
The fields every physical AI data collection job should capture
Partner site and survey
- Partner, site address with map pin, industry (factory, kitchen, laundry, warehouse, salon, hotel), owner on your side, status
- Survey: power at the floor, lighting, lockable kit room, named contact, filmable tasks and stations, photos, pass or fail with reasons
- Site consent letter: signed copy, signatory, date, what may be filmed, expiry
- Commercial terms: per-hour site fee basis, hours cap, payment schedule
Deployment, people and the day
- Deployment: site, lab order, task types and target hours, dates, site lead, capture techs
- Site lead certification on the protocol version in use, with date and assessor
- Daily check-in and check-out with selfie and GPS, per person
- Worker consent: name, language of the notice shown, signature, photo, date, scope, withdrawal date if any
- Day report: recorded hours by task vs target, flagged sessions, incidents, expenses with receipts
Session and file lineage
- Session ID, task type, worker, consent record, device serials, station, site, start and stop timestamps
- On-site quality flags: framing, lighting, fogging, battery, dropped frames
- Offload: file count and size per device, who offloaded, when, to which drive or bucket
- QA result: accepted or rejected, reason, reviewer, accepted duration
- Delivery: lab order, delivery batch, and whether the session was withdrawn after a revoked consent
Kit and money
- Device: serial, type (head camera, wrist camera, glove, battery, charger, card), depot, status (in depot, issued, in repair, missing, retired)
- Custody log: who held each serial from when to when, against which deployment
- Faults: overheating, mount broken, lens damage, with the batch it belongs to
- Site fees owed, contributor payouts (paid, held with reason), lab invoices per order
Rules and standards
The rules that shape the work, with their sources
| Rule | What it means on the job |
|---|---|
| GDPR: consent can be withdrawn as easily as it was given | Article 7(3) gives the data subject the right to withdraw consent at any time, says withdrawal does not make earlier processing unlawful, requires the person to be told this before consenting, and says it shall be as easy to withdraw as to give consent. A worker who signed on a tablet at the site needs an equally simple way to take it back. source: GDPR Article 7, Conditions for consent (gdpr-info.eu) |
| GDPR: erasure after consent is withdrawn | Article 17(1)(b) gives a right to erasure without undue delay where the person withdraws the consent the processing was based on and there is no other legal ground. For a data company that means knowing every session and every delivered batch that contains that person. source: GDPR Article 17, Right to erasure (gdpr-info.eu) |
| GDPR: what the worker must be told at collection | Article 13 lists what to give a person when their data is collected from them: who the controller is, the purposes, the recipients or categories of recipients, how long the data is kept, and the right to withdraw consent. If footage goes to third-party labs, the notice has to say so. source: GDPR Article 13, Information to be provided where personal data are collected from the data subject (gdpr-info.eu) |
| India DPDP Act: notice in the person's language | Section 5(3) requires the data fiduciary to give the person the option to read the notice in English or any language in the Eighth Schedule to the Constitution. Record which language each worker was shown, not only that they signed. source: Digital Personal Data Protection Act, 2023 (India), Gazette text, sections 5 and 6 (MeitY, PDF) |
| India DPDP Act: consent must be specific, and withdrawal stops processing | Section 6(1) requires consent to be free, specific, informed, unconditional and unambiguous, limited to the data needed for the stated purpose. Section 6(4) lets the person withdraw with the ease comparable to giving it, and section 6(6) requires the fiduciary to stop processing, and make its processors stop, within a reasonable time. source: Digital Personal Data Protection Act, 2023 (India), Gazette text, sections 5 and 6 (MeitY, PDF) |
| EU AI Act: training data governance for high-risk systems | Article 10 requires training, validation and testing data for high-risk AI systems to follow data governance and management practices, which cover the relevant design choices, the data collection processes and the origin of the data, and for personal data, the original purpose of collection. Labs building such systems will ask their data suppliers for that lineage. source: EU AI Act, Article 10: Data and data governance (artificialintelligenceact.eu) |
| EU AI Act: general-purpose model providers summarise training content | Article 53(1)(d) requires providers of general-purpose AI models to draw up and publish a sufficiently detailed summary of the content used for training. Lab customers may ask for provenance fields that feed that summary. source: EU AI Act, Article 53: Obligations for providers of general-purpose AI models (artificialintelligenceact.eu) |
| Spare camera batteries fly in the cabin, within limits | The FAA says spare lithium-ion batteries and power banks must be carried in carry-on baggage only, with terminals protected from short circuit, up to 100 Wh per battery; with airline approval a passenger may carry up to two spares of 101 to 160 Wh. It also says items carried for sale or distribution are prohibited, so check with the airline before a team flies a deployment's kit as luggage. source: FAA PackSafe, Lithium batteries in passenger baggage |
| Damaged or overheating batteries do not fly | The FAA says damaged or recalled batteries that could overheat must not be carried aboard an aircraft in carry-on or checked baggage unless removed or made safe. A battery pulled for overheating stays at the site or depot and goes to repair or recycling by ground. source: FAA PackSafe, Lithium batteries in passenger baggage |
| Shipping kit as cargo follows the IATA rules | Lithium batteries shipped by air, including batteries packed with or inside equipment, are dangerous goods under the IATA Dangerous Goods Regulations, with packing instructions by battery type and configuration. Larger kit moves between depots usually go as cargo, packed and labelled to those rules. source: IATA, Lithium batteries: Dangerous Goods Regulations guidance for shippers |
| UN 38.3 test summary for every battery model | Lithium cells and batteries must pass the UN 38.3 test series before transport, and since 1 January 2020 a test summary must be made available. Keep the summary on file for every battery model in your kit. source: Lithium Battery Service, UN 38.3 test series and test summary |
Rules vary by state, council and contract. Checked 12 November 2026; confirm against the source for your area before relying on it.
Numbers
A worked example: one week at one site, from recorded to deliverable hours
The battery rows come from the FAA. The rest is a worked example, not a benchmark: the team size and hours are illustrative, and your rejection rate depends on your protocol and QA bar. It shows why recorded hours, accepted hours and deliverable hours are three different numbers, and why each has to be tracked per session.
| Item | Figure | Basis |
|---|---|---|
| Team | 1 site lead and 6 capture techs, 5 days at one laundry | Worked example |
| Recorded | 6 techs x 4 hours on camera x 5 days = 120 hours | Worked example |
| Offloaded | 118 hours: one storage card failed on day 3 and lost 2 hours | Worked example |
| Rejected in QA | 14 hours (about 12%): framing on day 1 before the site lead corrected the head mounts, and two sessions with fogged lenses | Worked example |
| Accepted | 104 hours, rolled up to the lab order by task type | Worked example |
| Consent revoked later | One contributor withdraws; their 17 accepted hours come out of the next delivery | Worked example |
| Deliverable | 87 hours, each traceable to task, worker, device, site, timestamp and consent | Worked example |
| Payout held | Any session whose worker has no complete consent record: no payout until it is fixed, and not delivered | Worked example |
| Spare lithium-ion battery, cabin | Up to 100 Wh each without airline approval; carry-on only | FAA PackSafe |
| Larger spares, cabin | 101 to 160 Wh: up to two per passenger, with airline approval | FAA PackSafe |
source: FAA PackSafe, Lithium batteries in passenger baggage
Where it breaks
Where spreadsheets and generic software fall over
The partnerships team signs sites faster than operations can use them. Months later, half the signed partners have never seen a deployment and nobody owns the next step.
Give every partner an owner and a status from signed to active, and a list of partners stuck at each stage, so activation is someone's job with a date on it.
A team of seven travels to a site and finds no power at the stations, no room to lock the kit, and a contact who is on leave. The day is lost and the hotel is still paid.
A site survey with pass or fail on power, lighting, kit room, named contact and the site consent letter, and no deployment can be booked until it passes.
Worker consent forms are photos in a chat group or paper in a folder. When a lab asks for the consent behind a file, or a worker withdraws, someone spends days matching names to footage.
Consent is a record linked to the person, and every session links to that record. A withdrawal flags every session and every delivery batch that contains them.
Devices go out in bags of fifty and come back as forty-seven. Nobody can say who had the missing three, and the same overheating batch goes out again next week.
Issue and return by serial against a deployment and a person, with a custody log, a missing list, and a repair status that blocks a pulled batch from being issued.
Orders are tracked in one spreadsheet and recorded hours in another. An order falls behind plan and it shows up at month end, too late to send a team.
Accepted hours by task type roll up to each order daily, against the committed hours and the date, and a behind-plan order prompts a new deployment.
Dozens of site leads are hired in a few weeks and some lead teams before they have been trained on the current capture protocol. Their teams' rejection rates show it weeks later.
Certification per site lead per protocol version, required before they can be assigned as lead, and rejection rates by lead to show who needs a refresher.
Checklist
The physical AI data collection operations checklist
Print it, or hand it to whoever runs the office side.
- Every signed partner has an owner, a status and a next step with a date
- No deployment booked until the site survey passes and the site consent letter is on file
- Every lab order broken into hours by task type, with committed hours and a due date
- Every site lead certified on the protocol version in use before they lead a team
- Kit issued and returned by serial against a deployment and a named person
- Batteries checked against airline limits before a team flies; UN 38.3 summaries on file
- Daily check-in with selfie and GPS for every person on site
- Consent signed, with photo and the language of the notice, before a worker's first session
- Every session logs task, worker, devices, station and start and stop times
- Framing and quality spot checks in the first minutes of each session
- File counts checked against logged sessions at every offload
- Day report filed every day: hours vs target, flags, incidents, expenses
- Payouts held automatically when consent is missing; revoked consents removed from the next delivery
- Overheating or damaged devices pulled to repair as a batch and blocked from issue
Software options
What software physical AI data collection companies actually use
Judge each one on whether it fits the way your jobs run, not on the feature list. Bring a real job to every demo.
Tools your own engineers build
Data companies with an internal tech team
Most companies in this field build their own capture app, upload and QA pipeline and lab delivery format, because those are the product. The operational layer around them (sites, deployments, kit, consent, travel, payouts) often ends up in spreadsheets because the engineers are busy with the product. The question is which parts are worth building yourself.
Connecteam
Companies managing deskless and shift workers
Scheduling, a GPS time clock, forms and checklists, chat and training for frontline teams. Useful for check-in and shift rosters; check how it handles kit by serial, consent tied to each session, partner site surveys and hours against an order.
Fulcrum
Teams collecting structured data in the field
A mobile forms platform for field inspections and data collection, with offline use, photos, GPS and custom form logic. Good for site surveys and checklists; deployments, custody, payouts and order tracking would sit elsewhere.
Snipe-IT
IT and operations teams tracking hardware
Open-source asset management with check-out and check-in by serial and audit history. Fits the depot side of kit; it does not know about sites, sessions or consent.
CVAT or Label Studio
Teams reviewing and annotating video and sensor data
Open-source annotation and review tools that QA and annotation teams use on the footage itself. They sit after the field operation, and accept or reject reasons from them can feed back into the operations record.
Airtable or spreadsheets
Early teams and pilots
Fast to start, and flexible enough for a first site or two. They strain once many deployments run at once, people work offline on a factory floor, and every file has to trace to a consent record.
Questions to ask any vendor
- Can a partner site move from signed to active through a survey that blocks deployments until it passes?
- Can every session link to a task, a worker's consent record, device serials, a site and timestamps, and can a revoked consent flag every session and delivery batch that contains that person?
- Does the field app work offline on a factory floor, with check-in, consent in the worker's language, session timing and quality checks?
- Can kit be issued and returned by serial against a deployment and a person, with repair batches blocked from issue?
- Can accepted hours roll up to lab orders by task type against committed hours, and can our QA pipeline write results back through an API?
- Will it sit next to our own capture app and pipeline (API, webhooks, MCP) instead of asking us to replace them?
Bring one real physical AI data collection job. We show it running in Fieldproxy.
Get DemoHow Fieldproxy is set up for this
Physical AI data collection on Fieldproxy: built around how you already work
Fieldproxy is set up as the operations layer under your own tools: partner sites, surveys, lab orders, deployments, people, kit, consent, day reports and money, all as records your team and your engineers can read and write. Your capture app, upload pipeline, annotation and lab delivery stay yours; Fieldproxy holds who, where, with what kit and under which consent, and takes QA results back through the API. It is configured around your protocol and statuses, and is live within a day.
Module 1
Partner sites and surveys
Sites are signed faster than they are used, and a team sent to an unready site costs a day and a hotel bill.
- Partner record: industry, address with map pin, owner, status (signed, survey booked, survey passed, active, paused), filmable tasks and stations
- Survey checklist: power at the floor, lighting, lockable kit room, named contact, photos, pass or fail with reasons
- Site consent letter stored with signatory, scope and expiry; a missing or expired letter blocks deployments
- Stuck-partner list by stage and owner for the partnerships team
Module 2
Lab orders and deployment planning
Labs buy hours by task type, and a team has to be planned weeks ahead when an order slips.
- Order record: lab, task types, committed hours per task, due date, delivery format reference
- Accepted hours roll up daily per order and task; behind-plan orders are flagged
- Deployment record: site, order, tasks and target hours, dates, site lead, techs, kit list, travel
- Board of upcoming deployments by status: planned, staffed, kit issued, in field, closed
Module 3
Hiring, training and certification
Site leads are hired in waves and must be certified on the current protocol before they lead.
- Candidate and onboarding stages for site leads and capture techs
- Certification per protocol version with assessor, date and expiry
- Only certified leads can be assigned as site lead on a deployment
- Rejection rate by lead from QA results, feeding refresher training
Module 4
Field day app
The day happens on factory floors and in kitchens with poor signal, and every step has to be recorded as it happens.
- Check-in and check-out with selfie and GPS
- Kit confirmation by scanning serials
- Session start and stop with task, worker, devices and station; quality spot-check prompts
- Day report with hours vs target, incidents and expenses with receipt photos; works offline and syncs later
Module 5
Consent register
Every delivered hour has to trace to a person's consent, and a withdrawal has to reach every file.
- Notice shown in the worker's language; signature and photo captured on the device
- Consent record linked to the person and to every session they appear in
- Withdrawal recorded with date; affected sessions and delivery batches flagged for removal
- Payout hold on any session without a complete consent record
Module 6
Kit and depots
Hundreds of devices move between depots, site leads and repair, and missing ones have to be found by person.
- Device records by serial and type, with depot and status (in depot, issued, in repair, missing, retired)
- Issue and return against a deployment and a person, with custody history
- Fault log by device and batch; a batch pulled for overheating is blocked from issue
- Battery Wh and UN 38.3 summary per model, checked before a team flies
Module 7
Session lineage and accepted hours
Labs pay for accepted hours and ask where each one came from.
- Session ID shared with your capture app, so files and records join on one key
- Offload record per device with file counts against logged sessions
- QA results written back by your pipeline through the API: accepted or rejected, reason, accepted duration
- Dashboards: recorded vs accepted hours by order, site, team and lead, and rejection reasons
Module 8
Travel and expenses
Outstation deployments mean flights, trains, hotels and daily expenses for a whole team.
- Travel and hotel bookings per deployment and person
- Expense claims with receipt photos from the field app
- Approval by the operations manager before reimbursement
- Cost per accepted hour per deployment
Module 9
Site fees, payouts and lab invoices
Partners, contributors and labs are each paid or billed on different numbers.
- Site fees calculated per partner on the agreed hours basis
- Contributor payouts per session, held with a reason when consent is missing
- Lab invoices per order for delivered hours, synced to QuickBooks or Xero
- Open API, webhooks and an MCP server so your tools and Claude or ChatGPT use the same records
The AI agents, set up for physical AI data collection companies
| Agent | When | What it does | What you get |
|---|---|---|---|
| Watcher agent | A session is logged for a worker without a complete consent record, a consent is withdrawn, a device is past its due-back date, or a lab order falls behind plan | Checks sessions against the consent register, custody against due dates, and accepted hours against each order's committed hours and due date. | Payout held and the sessions flagged, or an alert to the operations lead naming the person, device or order and what is wrong. |
| Vision agent | A site lead photographs a device label, a site's signed consent letter or a survey item such as the kit room | Reads the serial and matches it to the device record, reads the letter's signatory and date, or describes what the survey photo shows. | The device attached to the deployment, the letter filed on the partner, or a survey item filled in. |
| Dispatch agent | A deployment is planned for a site and dates | Proposes a site lead certified on the current protocol and capture techs by location, language and availability. | A staffed deployment for the operations manager to confirm. |
| Voice agent | A site lead records the day report as a voice note at the end of the day | Turns the note into the day report fields: hours by task, flagged sessions, incidents, devices running hot, expenses. | A filled day report the site lead confirms before sending. |
| Scheduler agent | Every morning at 7 a.m. | Pulls yesterday's recorded and accepted hours by order and task, rejection reasons by team, kit missing or in repair, and partners stuck in onboarding. | A daily operations briefing emailed to the head of operations. |
| Command Center | Someone asks, for example, which sessions include a given worker, or which orders will miss their date at the current rate | Searches sessions, consent, deployments and orders. | The answer with links to the records. The same questions work from Claude or ChatGPT through the MCP server. |
Straight answers
Physical AI data collection: FAQ
What software do physical AI data collection companies use?
Most build their own capture app, upload and QA pipeline and lab delivery format, and run the operation around them on spreadsheets, shared drives and chat groups: partner sites, deployments, kit, consent, travel and payouts. Some add point tools such as Connecteam for shifts, Fulcrum for field forms or Snipe-IT for hardware. Others put that layer on a configurable platform such as Fieldproxy. The test is whether every delivered hour traces to a task, a worker's consent, a device, a site and a timestamp.
Does Fieldproxy replace our capture app or annotation tools?
No. Your capture hardware and app, upload pipeline, annotation and lab delivery stay yours. Fieldproxy holds the operations records around them (sites, deployments, people, kit, consent, money) and connects to your tools through an open API, webhooks and an MCP server, sharing a session ID so files and records join on one key.
How should we handle a worker who withdraws consent?
Record the withdrawal with its date, find every session that person appears in, remove those sessions from the next delivery, and tell any lab that received earlier batches according to your contract. Under GDPR Article 7(3) withdrawal must be as easy as giving consent, and under India's DPDP Act section 6(6) processing must stop within a reasonable time. That is only workable if every session links to a consent record.
What should a site survey check before a team is sent?
Power at the floor where people will work, lighting good enough for the cameras, a lockable room for kit and charging, a named site contact, which tasks and stations can be filmed, and the site's signed consent letter. If any of these fail, fix them before booking the deployment.
Why do recorded hours and accepted hours differ so much?
Footage is lost at offload, sessions are rejected in QA for framing, lighting, fogged lenses or the wrong task, and sessions are later withdrawn when consent is revoked. Track all three numbers per session so you can see where hours are lost by site, team and lead, and plan deployments on accepted hours, not recorded ones.
In which language should the consent notice be?
One the worker reads fluently. Under India's DPDP Act section 5(3), the person must have the option to read the notice in English or any language in the Eighth Schedule to the Constitution. Record the language shown, the signature and a photo, so you can show the notice was understood.
Can capture techs fly with camera batteries?
Within limits. The FAA says spare lithium-ion batteries go in carry-on only, up to 100 Wh each, and up to two spares of 101 to 160 Wh with airline approval. Damaged or overheating batteries must not fly. Check with the airline before flying a deployment's kit as luggage, and ship larger quantities as cargo under the IATA rules.
How do we track hundreds of devices across depots and teams?
By serial. Every device is issued to a named person against a deployment, returned by scan, and has a custody history and a status. A device not back by its due date shows on a missing list with the last holder, and a batch pulled for overheating is blocked from going out again until repaired.
How do we make sure a site lead is ready to lead a team?
Certify each lead on the protocol version in use, with an assessor and a date, and only allow certified leads to be assigned. Then watch QA rejection rates by lead; a lead whose team's rejections rise needs a refresher before the next deployment.
Does the field app work without signal?
Yes. Check-in, kit scans, consent, session timing, quality checks and the day report all work offline and sync when the phone is back online.
Can our engineers and AI assistants use the data?
Yes. Fieldproxy has an open API and webhooks for your pipeline, and an MCP server so Claude or ChatGPT can query and update deployments, sessions, consent and orders within the access you give it.
How long does it take to set up Fieldproxy for data collection operations?
Fieldproxy does the setup around your protocol: partner and survey stages, order and deployment records, the field app, the consent register, kit and the money side. It is live within a day, with unlimited users, so every site lead and capture tech is included.
Related guides
All sources on this page (16)
- source: GDPR Article 7, Conditions for consent (gdpr-info.eu)
- source: Digital Personal Data Protection Act, 2023 (India), Gazette text, sections 5 and 6 (MeitY, PDF)
- source: EU AI Act, Article 10: Data and data governance (artificialintelligenceact.eu)
- source: FAA PackSafe, Lithium batteries in passenger baggage
- source: GDPR Article 17, Right to erasure (gdpr-info.eu)
- source: GDPR Article 13, Information to be provided where personal data are collected from the data subject (gdpr-info.eu)
- source: EU AI Act, Article 53: Obligations for providers of general-purpose AI models (artificialintelligenceact.eu)
- source: IATA, Lithium batteries: Dangerous Goods Regulations guidance for shippers
- source: Lithium Battery Service, UN 38.3 test series and test summary
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