Image Annotation
Bounding boxes, polygons, keypoints, and segmentation masks with consistent taxonomy for CV models.
Freelance Data Annotation
Accurate, scalable annotation that keeps model training on track.
I help AI teams label image, text, audio, and video data with rigorous QA and fast turnaround—so your models learn from clean, reliable ground truth.
- Accuracy focus
- Multi-pass QA
- Turnaround
- 72h pilots
- Coverage
- 4 data types
Spot checks + consensus review.
Rapid samples for quick validation.
Image, text, audio, video.
Annotation process
Workflow built for startup velocity
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1
Labeling guidelines
Define schema, edge cases, and acceptance criteria.
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2
Iterative annotation
Batch delivery with feedback loops.
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3
Quality assurance
Double review, metrics, and audit trail.
Currently supporting:
Computer Vision
NLP Pipelines
Audio Events
Video Tracking
Prefer NDA? Happy to sign before project details.
Services
Annotation workflows built for startup speed and accuracy
Clean, reliable labeling across modalities—designed for iterative model development and fast releases.
ISO-aligned QA checks
Clear labeling guidelines
Weekly progress reports
Text Labeling
Entity extraction, intent classification, sentiment, and multi-label tagging for NLP pipelines.
Audio Transcription
Verbatim transcripts, speaker diarization, timestamps, and noise tags for ASR training.
Video Annotation
Frame-by-frame tracking, event tagging, and temporal segmentation for video analytics.
Quality Assurance
Dual-pass review, gold-standard sampling, and precision checks to keep datasets clean.
Fast Turnaround
Agile delivery schedules with clear scope tracking and rapid iteration cycles.
Ready to start?
Share your dataset goals and Ill respond with a tailored plan.
Results snapshot
Reliable annotation delivery, built for startup velocity
Process-driven labeling with measurable accuracy, fast handoffs, and flexible scope for evolving product teams.
98.7%
Annotation accuracy target
Double-pass QA with sampling audits and edge-case review.
48–72 hrs
Typical turnaround for sprints
Rapid iterations for MVPs and model checkpoints.
4 data types
Image · Text · Audio · Video
Multi-modal labeling with consistent taxonomies.
10–200k
Items per project range
Flexible staffing and clear QA gates as volume scales.
Client feedback
Trusted by teams shipping ML products
Founders and ML teams rely on meticulous labeling, transparent workflows, and consistent QA. Here’s what they highlight most.
“Flawless labeling with thoughtful edge-case notes.”
“We onboarded her for a computer vision pilot and saw precision improve immediately. She documented every ambiguous case, escalated quickly, and kept our ontology clean.”
ML Ops Lead
Autonomous robotics startup
Image annotation
“Extremely responsive and ahead of schedule.”
“We needed rapid text labeling for a launch sprint. She organized guidelines, delivered daily QA summaries, and adapted quickly to new categories.”
Product Lead
Conversational AI team
“Organized, reliable, and transparent workflow.”
“Clear daily updates, consistent labeling, and proactive flagging of data issues. The best contractor we’ve worked with for audio QA.”
Founder
Speech analytics startup
Need dependable labeling support for your next release?
Start a project inquiryABOUT
Precise annotation built for fast-moving ML teams
I help startups turn raw data into reliable training sets with consistent labels, clear definitions, and QA that scales. My workflow is designed for speed without sacrificing accuracy — every batch ships with documented guidelines, edge-case handling, and measurable quality checks.
I work seamlessly with your ML stack: taxonomy design, label schema updates, inter-annotator agreement targets, and feedback loops that reduce rework. Whether it’s image, text, audio, or video, I keep labeling predictable and model-ready.
Detail-first QA
Clear labeling guidelines
Fast turnaround
Startup-ready communication
Workflow built for consistency
Annotation guidelines, ambiguity logs, and versioned schemas keep teams aligned as datasets grow.
Quality checks that scale
Spot checks, audits, and accuracy reporting ensure label precision remains stable across batches.
Built for ML iteration
I adapt quickly to new model insights and edge cases so your team can iterate without delays.
Founder-friendly comms
Clear updates, lean reporting, and quick responses keep startups moving with confidence.