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Based in India · Serving globally

The human layer.
Behind AI.

RLHF, annotation, evaluation and data collection in 22+ Indian languages. An AI-first training platform — and your end-to-end data partner.

Start your free pilot Become an AI Trainer100 tasks · 5 days · full quality report
22+ languages60,000+ AI trainers4-layer QA★95%+accuracy0.80+ kappa

trainer review

★★★★★

“I earned ₹1.5 lakhs in 6 months on TrainPlex. The best part — AI is learning from my Hindi. मेरी Hindi से AI सीख रहा है।”

Priya Sharma Hindi Evaluator · Jaipur

case study

★★★★★

“Hindi RLHF at scale: preference ranking across 500 prompts for a leading foundation-model lab. Urban, rural and code-mixed inputs — 96% accuracy, 0.82 kappa.”

Case 01 · RLHF 500 tasks · 96% · 0.82 κ

trainer review

★★★★★

“I never imagined my Tamil skills could train AI. TrainPlex made that possible — and paid me well for it. எனது தமிழ் அறிவால் AI-க்கு பயிற்சி.”

Arun Ramasamy Tamil RLHF Reviewer · Chennai

case study

★★★★★

“Multi-language benchmark: parallel evaluation across Hindi, Tamil, Bengali, Telugu and Marathi — a per-language error typology delivered to the client, 22 metrics each.”

Case 02 · Benchmark 5 languages · 100 tasks each

trainer review

★★★★★

“Sitting in my village, I work for world-class AI companies. My Bengali is valuable now. গ্রামে বসেই বিশ্বমানের AI কোম্পানির জন্য কাজ করছি।”

Sumit Das Bengali Annotator · Birbhum

case study

★★★★★

“Hindi prompt engineering: 200 adversarial prompts spanning legal, medical, finance, education and civic topics — designed to expose LLM blindspots. 3x failure rate surfaced.”

Case 03 · Prompts 200 prompts · 5 domains

trainer review

★★★★★

“I earned ₹1.5 lakhs in 6 months on TrainPlex. The best part — AI is learning from my Hindi. मेरी Hindi से AI सीख रहा है।”

Priya Sharma Hindi Evaluator · Jaipur

case study

★★★★★

“Hindi RLHF at scale: preference ranking across 500 prompts for a leading foundation-model lab. Urban, rural and code-mixed inputs — 96% accuracy, 0.82 kappa.”

Case 01 · RLHF 500 tasks · 96% · 0.82 κ

trainer review

★★★★★

“I never imagined my Tamil skills could train AI. TrainPlex made that possible — and paid me well for it. எனது தமிழ் அறிவால் AI-க்கு பயிற்சி.”

Arun Ramasamy Tamil RLHF Reviewer · Chennai

case study

★★★★★

“Multi-language benchmark: parallel evaluation across Hindi, Tamil, Bengali, Telugu and Marathi — a per-language error typology delivered to the client, 22 metrics each.”

Case 02 · Benchmark 5 languages · 100 tasks each

trainer review

★★★★★

“Sitting in my village, I work for world-class AI companies. My Bengali is valuable now. গ্রামে বসেই বিশ্বমানের AI কোম্পানির জন্য কাজ করছি।”

Sumit Das Bengali Annotator · Birbhum

case study

★★★★★

“Hindi prompt engineering: 200 adversarial prompts spanning legal, medical, finance, education and civic topics — designed to expose LLM blindspots. 3x failure rate surfaced.”

Case 03 · Prompts 200 prompts · 5 domains
[ The framework ]

The 5 method

AI moves faster every quarter. A model is only as good as the humans who trained it. We're the human layer that closes that gap — a five-phase method built on India's languages, people and rigor. No shortcuts. No guesswork. No excuses.

Scope · Collect · Annotate · Verify · Deliver

Scope

Bad training data starts with an undefined task.

  • we don't start at the labels. we start at what the model must learn
  • guidelines written with your team — not guessed from a brief
  • clarity before annotation

Collect

If it isn't representative, it isn't data.

  • speech, image, video and text — from real people across India
  • 22+ languages, every accent, dialect and script
  • consent-first, device-verified collection

Annotate

A label is an opinion until a native speaker signs it.

  • language-native, domain-verified AI Trainers
  • RLHF, ranking, transcription, bounding, evaluation
  • every task time-stamped and traceable

Verify

Unchecked data is debt with a delivery date.

  • four independent QA layers — auto, AI, peer, expert
  • 95%+ accuracy, 0.80+ IAA kappa — measured, not promised
  • AI assists. humans decide.

Deliver

What doesn't ship on time doesn't train anything.

  • 98% on-time, in the formats your pipeline already reads
  • a full quality report with every batch
  • a partner for the next batch, not a vendor for one
SCOPECOLLECTANNOTATEVERIFYDELIVER

From a voice in a village to a model that understands it.

This is the whole journey of one piece of data on TrainPlex — collected from a real person, labelled by a native speaker, checked four times, and only then allowed to teach a model.

  1. A woman recording her voice on a phone in a village courtyard
    01 · Collect

    Real people, real signal.

    Voice, face, image and text captured from verified AI Trainers across 22+ languages — device-checked, consent recorded before anything is captured.

    60,000+verified trainers
  2. An annotator labelling Devanagari text on a dark screen
    02 · Annotate

    Labelled by someone who speaks it.

    Transcription, ranking, tagging and evaluation done by language-native trainers who can tell a fluent answer from a correct one.

    22+languages
  3. Light passing through four glass gates
    03 · Verify

    Four gates. Nothing slips.

    Automatic checks, an AI cross-read, a second trainer and a human lead. Disagreement is measured, not assumed away.

    0.80+IAA kappa
  4. A neural network of glowing nodes converging into a core
    04 · Train

    Only clean data reaches the model.

    Preference pairs, transcripts and labels ship in the formats your pipeline already reads — RLHF-ready, with the quality report attached.

    95%+accuracy
  5. A glowing crystalline data cube
    05 · Deliver

    Shipped on time, every batch.

    Weekly measured delivery, a full report with every batch, and a partner for the next one — not a vendor for this one.

    98%on-time
See how a pilot works All services

What we're collecting right now.

Real programmes running on the TrainPlex platform today — each with its own capture flow, device checks, consent record and per-item review before anything is paid for or delivered.

Run a collection with us
A woman recording a short face video with a guide frame on her phone
Video · biometric

Face video

Short in-browser face recordings with a guide frame and device gate; every clip reviewed, one person one payout.

A man recording his voice into a phone at a home desk
Audio · 48 kHz WAV

Voice & speech

Scripted and free speech across accents and states, noise-floor checked at record time.

Vintage family photographs being photographed on a wooden table
Image · sets of 20

Historical photographs

Family archives photographed as complete sets, EXIF-checked, delivered as partial-success batches.

A grid of portrait tiles with and without a face mask
Image · 20 per set

Mask image sets

Masked and unmasked portraits in fixed angles; the set is the unit, accepted before it reaches the client drive.

A woman taking a live photo on a balcony at golden hour
HEIC + MOV pairs

Live photo capture

Moving-picture pairs captured on device, paid per approved capture after the client signs off.

Portrait cards of one person across life stages, linked by a thread
Portrait series

Age progression

Consented portrait profiles across life stages, captured in-browser with automatic framing checks.

People photographing everyday objects with phones
Image · off-platform link

Everyday image collection

Street signs, shop boards and objects photographed by people across India through a partner link.

A woman recording a short face video with a guide frame on her phone
Video · biometric

Face video

Short in-browser face recordings with a guide frame and device gate; every clip reviewed, one person one payout.

A man recording his voice into a phone at a home desk
Audio · 48 kHz WAV

Voice & speech

Scripted and free speech across accents and states, noise-floor checked at record time.

Vintage family photographs being photographed on a wooden table
Image · sets of 20

Historical photographs

Family archives photographed as complete sets, EXIF-checked, delivered as partial-success batches.

A grid of portrait tiles with and without a face mask
Image · 20 per set

Mask image sets

Masked and unmasked portraits in fixed angles; the set is the unit, accepted before it reaches the client drive.

A woman taking a live photo on a balcony at golden hour
HEIC + MOV pairs

Live photo capture

Moving-picture pairs captured on device, paid per approved capture after the client signs off.

Portrait cards of one person across life stages, linked by a thread
Portrait series

Age progression

Consented portrait profiles across life stages, captured in-browser with automatic framing checks.

People photographing everyday objects with phones
Image · off-platform link

Everyday image collection

Street signs, shop boards and objects photographed by people across India through a partner link.

Four layers. One quality bar.

Every single task passes four independent checks before it ships — automatic, AI, a second trainer, and a human lead. No compromises.

Layer 01 — System

Auto QA

Time checks, pattern detection and completeness guards run on every submission the moment it lands.

100%tasks screened
time-on-task within band
no repeated pattern
all fields complete
Layer 02 — AI

AI cross-check

Claude and GPT re-read every task and flag disagreement, drift and copy-paste before a human sees it.

100%AI-validated
Layer 03 — Peer

Peer review

A second language-native AI Trainer independently verifies the label. Agreement is tracked per pair.

0.80+IAA kappa
PSAR
Layer 04 — Expert

Expert human

Final approval by our QA Lead, with the working attached — not just the number.

95%+accuracy
APPROVED
0%+
Accuracy
vs 85–90% industry
0.00+
IAA kappa
vs 0.60–0.75 industry
0%
On-time delivery
measured weekly
<0%
Rework rate
client-reported

“AI assists. Humans decide.”

What's holding your model back?

You name the gap. We map the data.
See what happens next...

211+ gaps diagnosed