Synthetic feedback contaminates the loop
Model-generated labels and cheap synthetic shortcuts can make alignment data look scalable while quietly teaching models their own mistakes.
Verified human judgment, no synthetic feedback loops.
AI DATA ENGINE
The Verified Human Data Engine for AI Teams
Turn telco-verified human signal into production-ready datasets: collection, annotation, RLHF, embodied AI data, QA, and provenance delivered from one operating layer.
800K+Verified Contributors
120M+Telco Reach
90%Cost Reduction
<48hrPilot Launch
Human data workflow
QA-backedVerified input. Reviewed output.
- 01Source
Telco Identity
KYC-verified contributors
- 02Route
Mission Routing
Jurisdiction + language match
- 03Capture
Human Signal
Text, image, audio, video
- 04Review
Quality Layers
Consensus + audit review
Output
Production readyDataset + review logs
Full Provenance Tracking
Consent-Based Sourcing
800K+ Verified Contributors
Zero Synthetic Data
Ethically Sourced
THE PROBLEM
AI data projects usually fail before the model sees them.
The bottleneck is not just volume. It is identity, context, QA, and provenance. GIG is built around those constraints from the first contribution to the final dataset.
Anonymous crowd work creates trust gaps
If you cannot prove who contributed, where they came from, or how they were qualified, enterprise AI data becomes hard to audit.
Telco KYC-backed contributor identity and provenance.
Raw data arrives without production QA
Unreviewed uploads, weak consensus, and missing review logs push hidden cleanup work back onto your ML team.
Multi-layer QA, reviewer roles, and delivery logs.
Traditional vendors move too slowly
Weeks of onboarding and enterprise pricing do not fit model teams that need pilots, iteration, and volume now.
Mission-based launch in under 48 hours.
REAL SIGNAL OPS
Human data modules without the theatre.
The job is simple: source the right people, run the task cleanly, and hand your model team a batch they can trust. The motion stays; the fake labelling jargon does not.
Source the right people
Route work to contributors who match the market, language, device, and task profile.
Verified contributors
Telco-backed identity gives you cleaner sourcing than anonymous crowd pools.
Market-fit routing
Segment by language, location, device access, and project-specific criteria.
Capture real-world tasks
Collect the kind of human signal models actually need: judgment, media, context, and motion.
Voice, image, and video
Mobile-native tasks for screenshots, photos, speech, short clips, and local context.
Physical-world demos
Motion traces, narrated actions, and controlled task capture for embodied AI teams.
Package it for model teams
Every batch needs enough QA and sourcing context to survive handoff to engineering.
Reviewer consensus
Multi-pass review, disagreement handling, and reviewer escalation where it matters.
Provenance trail
Batch manifests, acceptance notes, and source metadata your team can audit.
THE DATA LOOP
From Mission Brief to Auditable AI Dataset
A production loop designed for AI teams that need verified human signal, not anonymous crowd output with mystery provenance.
01
Define Mission
Lock the target output, acceptance rules, consent flow, markets, languages, and delivery format before a single task goes live.
02
Route Workforce
Match telco-verified contributors by jurisdiction, language, device, demographic fit, and task history.
03
Capture Signal
Run mobile-native missions for text, image, audio, video, preference, safety, and physical-world data capture.
04
Validate Quality
Apply automated checks, consensus scoring, reviewer escalation, and audit sampling before data reaches your team.
05
Prove Origin
Attach provenance logs that show who produced the signal, under what rules, and which QA layer approved it.
06
Deliver Dataset
Ship clean batches, review notes, quality metrics, and iteration paths so your model team can move immediately.
WHAT GIG RUNS
Real Data Workflows, Not AI Vendor Theatre
This is the operational layer: define the job, route verified contributors, collect real signal, review the mess, and package it for your model team.
Market Signal Collection
When you need real respondent data from a specific market, not scraped panels or synthetic personas.
You bring
- Target segment
- Task script
- Media or survey rules
GIG runs
- Route tasks to verified contributors
- Collect responses, screenshots, photos, audio, or short clips
- Filter duplicates, low-effort work, and off-brief submissions
You get back
- Accepted raw files
- Rejected-work log
- Contributor and market metadata
Proof attached
- Consent record
- Task timestamp
- QA notes
A brief in. A usable batch out.
No abstract capability menu. These are the kinds of instructions contributors receive and the files your model team gets back.
Every program is scoped around the target model, contributor profile, acceptance rules, and reviewer process.
Regional LLM evaluation
- Client brief
- Compare customer-support answers in English and Tagalog against a five-point quality rubric.
- Contributor task
- Rank two answers, flag fabricated or unsafe claims, and explain the decision in one sentence.
- Handoff
- Preference pairs, reviewer rationales, score distributions, and a disagreement report.
Egocentric video and vision labels
- Client brief
- Capture everyday actions from a first-person view and identify people, objects, and safety equipment.
- Contributor task
- Record folding laundry, narrate a simple task, or mark hard hats, hands, and tools in supplied images.
- Handoff
- Approved clips, boxes or masks, task metadata, and the QA sampling log.
Local-market ground truth
- Client brief
- Build a market cut that reflects local language, products, devices, and consumer behaviour.
- Contributor task
- Verify product images, record short voice prompts, and answer structured questions from the target market.
- Handoff
- Labeled samples, market segments, consent records, and batch-level provenance.
Need specialist field collection outside these programs? EGXO Data
Client Success
Trusted by AI leaders
Data Labeling Platform4.5/5
30K+
New unique users in 2 months
47K+
Tasks completed through GIG
We wholeheartedly recommend GIG as they have been an outstanding partner in every way. Their highly capable tech and operations teams supported a smooth launch and have consistently addressed any issues quickly as we've scaled.
Albert KhaskyDirector of Business Development, Sapien
GET STARTED
Ready to build your verified data engine?
Launch a scoped AI data pilot in under 48 hours with telco-verified contributors, multi-layer QA, and provenance logs from day one.