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ThinQmate
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Multi-pass QA on every batchPilot in days, not weeks

All your data labelled, checked, and delivered

Stop cleaning up after crowd platforms. Match with a trained annotation team, put your guideline in writing, and get batches back with the accuracy number attached.

1M+labels delivered with QA
99%+target gold-set accuracy

  • Image annotation

    • Bounding boxes
    • Polygons & masks
    • Semantic segmentation
    • Keypoints
    • Classification
    COCOYOLOPascal VOC

  • Video annotation

    Object trackingEvent taggingInterpolation QA

  • Datasets across every domain

    RetailAutonomyHealthcareAgritech

  • Text & NLP

    • Named entity recognition
    • Intent & sentiment
    • Relevance ranking
    • RLHF preference data
    • Moderation
    JSONLCustom schema

  • 3D & LiDAR

    3D cuboidsPoint-cloud segmentationSensor fusion

  • Audio & speech

    TranscriptionDiarizationEvent tagging

  • Document & OCR

    • Layout regions
    • Key-value extraction
    • Table structure
    • Handwriting transcription
    InvoicesFormsContracts

  • Medical imaging

    DICOMSegmentationClinician review

  • Evaluation & QA

    • Gold-set spot checks
    • Consensus scoring
    • Second-pass review
    • Model-output audits
    2+ QA passes

We solve the data problems that stall your model.

Forget anonymous crowd platforms
No more re-labelling failed batches
Stop debugging your training data
Say bye to unmanaged QA
Skip weeks of annotator onboarding
Done with guideline drift mid-project
2xreview passes minimum on every delivery
48htypical pilot batch turnaround
10+annotation types across six data modalities

Why hire annotators if a vetted team can start this week?

Talk to a lead

Annotation lead

7+ years

AutonomyRetailRobotics

Senior QA reviewer

6+ years

Gold setsConsensusAudits

CV data engineer

8+ years

PipelinesCOCO/YOLOTooling

Medical imaging annotator

5+ years

DICOMSegmentation

NLP annotation specialist

6+ years

NERRLHFModeration

LiDAR & 3D annotator

5+ years

CuboidsSensor fusion

Why teams choose ThinQmate

25+

AI teams served

Vetted talent
Top 4%

of applicants make our annotation teams

40%Application & language screen
18%Domain test task
8%Calibration on gold sets
4%Lead approval
24h

from scoping call to matched team

A senior lead scopes your taxonomy and staffs the pilot within a day.

150+

annotation projects delivered

30+

annotation skill sets

From pixel masks to RLHF rankings.

Pricing
Flat per-label pricing
scoped with your pilot

Get your data labelled without headache

Raw data became datasets models ship on.

Retail shelf detection dataset
RetailDetection
ADAS perception labels, camera + LiDAR
AutonomySensor fusion
Surgical tool segmentation
HealthcareSegmentation
Invoice key-value extraction ground truth
DocumentsOCR
Support-chat intent & RLHF preference data
NLPRLHF
Crop & weed detection from drone imagery
AgritechDetection

One team. Every annotation capability.

Six data modalities, ten-plus annotation types, one QA standard. Every service runs on written guidelines and ships with accuracy reported against gold sets.

Computer vision

Image annotation

  • Bounding boxes
  • Polygons
  • Semantic segmentation
  • Instance masks
  • Keypoints
  • Classification

Video annotation

  • Object tracking
  • Frame classification
  • Event tagging
  • Action recognition
  • Interpolation QA

3D & LiDAR

  • 3D cuboids
  • Point-cloud segmentation
  • Sensor fusion
  • Lane & drivable area

NLP & text

Text annotation

  • Named entity recognition
  • Intent & sentiment
  • Relevance judgements
  • Content moderation

LLM data

  • RLHF preference rankings
  • Prompt-response grading
  • Red-team & eval sets
  • Instruction data

Audio & speech

Speech data

  • Transcription
  • Speaker diarization
  • Intent labelling

Sound events

  • Audio event tagging
  • Environment classification
  • Quality screening

Documents

Document & OCR

  • Layout regions
  • Key-value extraction
  • Table structure
  • Handwriting transcription

Evaluation & audits

  • Held-out eval sets
  • Edge-case suites
  • Failure-mode audits
  • Model output review

How it works

Skip the sourcing pain and the QA roulette. Move straight to labelled data you can train on.

01Scope & guidelines

Book a call. A senior lead turns your taxonomy, edge cases, and acceptance criteria into a written labelling guideline: the contract every annotator works to.

02Pilot batch

A small calibrated batch labelled against gold examples. You review it, we tune the guideline, and nobody scales until the pilot passes.

03Scale with QA built in

Trained teams ramp to full volume with consensus scoring, second-pass review, and gold-set spot checks running on every delivery.

04Iterate with your model

Your error analysis feeds new guideline revisions and targeted re-labelling, batch after batch. Delivered in COCO, YOLO, Pascal VOC, JSONL, or your schema.

Only the sharpest annotators make your project.

Every ThinQmate team member passes a four-stage screen before touching client data.

40%

Application & language screen

Manual review of experience, domain background, and written English.

18%

Domain test task

A multi-step labelling task in the candidate's modality, scored against gold answers.

8%

Calibration on gold sets

Live calibration rounds until agreement with senior reviewers holds steady.

4%

Lead approval

A project lead signs off before anyone joins a client team, under NDA.

You are one call away from training data you can trust.

Tell us what you are training. We will scope a pilot batch, put a guideline in writing, and show you the accuracy number before you commit to volume.

FAQ

Everything teams ask before a pilot. Still curious? Book a 15-minute intro call.

Book a call
What is ThinQmate?

ThinQmate is a data labelling and annotation studio. You bring raw data and a goal; we bring trained annotators, written guidelines, multi-pass QA, and a lead who owns the accuracy number. Image, video, text, audio, 3D and documents.

How is this different from a crowdsourcing platform?

Crowd platforms give you a tool and anonymous workers; you own the quality problem. ThinQmate is managed: a dedicated team learns your domain, works to a written guideline, and ships batches through multi-pass QA with accuracy reported against gold sets.

How do engagements start?

With a scoping call and a pilot. We turn your taxonomy into a labelling guideline, run a small calibrated batch, and you judge the output before anything scales. Pilots typically turn around within days, not weeks.

What tools and formats do you work in?

Yours or ours. We work in the major annotation platforms as well as client-hosted tooling, and we deliver in the formats your training pipeline expects: COCO, YOLO, Pascal VOC, JSONL, or a custom schema.

How do you keep our data secure?

NDA-backed teams, least-privilege project-scoped access, logged handling, and deletion on your schedule. For sensitive data we can work entirely inside your environment, so nothing leaves the accounts you control.

Can you also build the model, not just the data?

Yes. ThinQmate also does data science, AI engineering, and MLOps. Teams often start with annotation and extend into model development or evaluation with the same partner.