About Synnth
The human intelligence
behind AI that works
Synnth delivers the labeled training data that makes AI systems accurate, robust, and trustworthy — built on expert human judgment, not shortcuts.
Our mission
We exist to make AI training data worth trusting
AI models are only as good as the data they are trained on. That is the entire premise behind Synnth. We were founded on a simple observation: the AI industry was producing impressive models but quietly suffering from a data problem — training sets that were too small, too narrow, too noisy, or too cheaply annotated to produce AI that performs reliably in the real world.
We built Synnth to fix that. Not by automating annotation away, but by combining expert human judgment with structured, repeatable processes and rigorous quality assurance — across speech, text, image, and video, in 40+ languages, for AI teams building products that matter.
Every dataset we deliver carries a simple guarantee: it was annotated by a human who understood the domain, reviewed by a senior annotator who applied the rubric, and validated by a QA process that would have caught any errors that slipped through.
“The quality of an AI model’s judgment is inseparable from the quality of its training data. We take that seriously.”
What we do
Data collection & annotation across every AI modality
From sourcing raw data to delivering production-ready labeled datasets — Synnth covers the full pipeline across the four core data types that power modern AI.
Speech & Audio
ASR and TTS corpora, wake word datasets, speaker diarization, phoneme annotation, and emotion labeling — with native-speaker diversity across 40+ languages.
- ASR training data
- TTS voice corpora
- Multilingual speech
- Audio annotation
Text & NLP
RLHF preference data, instruction tuning pairs, NER annotation, sentiment labeling, and multilingual NLP corpora — built for LLMs and NLP models.
- RLHF & LLM alignment
- NER annotation
- Sentiment labeling
- Intent classification
Image & Vision
Bounding boxes, polygon segmentation, keypoints, semantic segmentation, 3D cuboids, and medical image annotation — for every computer vision use case.
- Object detection
- Semantic segmentation
- Medical imaging
- 3D annotation
Video & Motion
Multi-object tracking, action recognition labeling, temporal segmentation, pose tracking, and event detection — with frame-accurate temporal consistency QA.
- Object tracking
- Action recognition
- Temporal labeling
- Pose tracking
How we work
A process built for repeatability and trust
Every Synnth project follows the same structured pipeline, regardless of data type, language, or volume. That consistency is not bureaucratic — it is the mechanism through which we maintain quality at scale.
We begin every project by co-designing the annotation specification with your ML team. That means building task-specific ontologies, edge-case decision trees, and quality rubrics — not adopting a generic template and hoping it fits. The specification becomes the ground truth against which all annotation quality is measured.
Annotators are recruited to match the task — not just for language and availability, but for domain expertise. Medical text goes to clinicians. Legal documents to legal professionals. Financial records to finance specialists. Technical audio to domain engineers. This matching is the primary difference between labels that train good models and labels that introduce systematic noise.
Quality assurance is multi-stage and non-negotiable: inter-annotator agreement measurement, automated consistency validation, and senior reviewer sign-off before any batch leaves our pipeline. Every delivery includes a QA report — not a promise.
Scope before you annotate
Every project begins with a co-designed specification — annotation schema, quality bar, edge-case rules, and demographic quotas. Ambiguity in the spec becomes noise in the labels.
Match annotators to domain
Annotation expertise must match subject matter. A generalist cannot annotate clinical text accurately, no matter how good the guidelines. We staff by domain, not by availability.
Measure quality, don't assume it
Inter-annotator agreement is measured on every project. QA scores are reported, not claimed. Batches that fall below threshold are returned for re-annotation before delivery.
Iterate with the same team
Production runs use the same annotator cohort as the pilot. Institutional knowledge of your ontology, edge cases, and quality bar doesn't reset with every batch.
What we believe
A process built for repeatability and trust
Human judgment is irreplaceable
Automated annotation pipelines can process volume. They cannot exercise judgment. For the edge cases, the ambiguous instances, and the domain-specific decisions that make the difference between a good model and a bad one — you need expert humans. We will never automate away the parts that require understanding.
Quality is measured, not claimed
We don't report quality by asking annotators how confident they felt. We measure it against gold-standard references, inter-annotator agreement scores, and automated validation — and we share those numbers with every delivery. If a batch doesn't meet the bar, it doesn't ship.
Diversity prevents bias
Training data that doesn't represent the world produces AI that doesn't work for the world. Demographic diversity in data collection and linguistic diversity in annotation are not optional extras — they are the difference between AI that performs and AI that discriminates.
Data security is non-negotiable
Your data is not ours to use, share, or train on. It enters our environment, gets annotated, and leaves. That is the entire relationship with your data. Every engagement is covered by an NDA, encrypted in transit and at rest, and handled in access-controlled environments.
Honest estimates, not optimistic ones
We tell clients how long projects will actually take, what the realistic quality bar is for their task complexity, and what edge cases our pipeline will struggle with. Overpromising on timelines or accuracy doesn't help anyone build better AI.
Partnership over transaction
The best annotation partnerships are iterative. Ontologies evolve as models improve. Edge cases surface that weren't in the original spec. We build relationships designed for ongoing iteration — with the same annotator cohort, the same project manager, and accumulated institutional knowledge of your domain.
How it works
2,000+ expert annotators, matched to your domain
Our annotator network spans 40+ countries and covers every major data modality, domain, and language — all vetted, trained, and managed in-house.
Synnth’s annotator network is not a marketplace. Every annotator is vetted, trained on Synnth’s quality standards, and assigned to projects matched to their expertise. We do not accept just-in-time crowd workers for production annotation tasks — quality at scale requires a stable, trained workforce.
Our network includes medical professionals (radiologists, clinicians, pharmacologists), legal professionals (lawyers, paralegals, compliance specialists), financial services experts, linguists and NLP specialists, software engineers for technical annotation tasks, and domain-specific native speakers across 40+ languages.
For regulated industries — healthcare, legal, financial services — annotators work under strict NDAs, HIPAA-compliant protocols, and task-specific confidentiality agreements in addition to our standard data security practices.
Our quality commitment
Why 98.5% QA accuracy
is a measured number
Not a marketing claim — every number we publish is derived from a specific measurement methodology we apply to every project we deliver.
Inter-annotator agreement
Every project includes IAA measurement on a statistically significant calibration sample — Cohen’s kappa for two-annotator tasks, Fleiss’ kappa for three or more. We target IAA above 0.80 on standard tasks and share scores in every delivery report.
0.86 avg. IAA
Automated validation
Every annotation batch passes automated consistency checks before human QA begins — geometry validation for image and video tasks, format validation for text, and acoustic quality checks for audio. Errors caught early don’t become patterns.
Multi-stage pipeline
Senior reviewer sign-off
No batch ships without a senior reviewer passing it. Senior reviewers are experienced annotators who know the project’s edge cases, have read the spec, and are accountable for the final quality score in the delivery report.
100% reviewed
Per-class reporting
We build task-specific action ontologies, edge-case handling guides, and annotator calibration programs for your deployment domain — not generic rubrics that generate systematic errors on your corner cases.
Per-project calibration
Free revisions within scope
Quality reports break down accuracy by class, by annotator cohort, and by task type — not an aggregate that hides underperforming categories. If one object class is being systematically mislabeled, you see it in the report before it affects training.
Transparent reporting
Pre-Calibration
Every annotator cohort completes calibration samples with gold-standard answers before production begins. Annotators below threshold on calibration are re-trained or replaced. The same standard applies to every batch in the production run.
Per-project calibration
Industries
Annotation expertise across every sector and domain
Our annotators are matched to your industry — not just your data type. Domain vocabulary, regulatory context, and quality standards vary by sector, and so does our annotator matching.
Autonomous Vehicles
Lane detection, pedestrian and vehicle tracking, LiDAR annotation, traffic sign classification, and ADAS datasets across weather, lighting, and geography.
Conversational AI
Intent labeling, slot filling, dialogue annotation, RLHF preference data, and multilingual speech corpora for chatbots, virtual assistants, and voice AI.
Healthcare & Life Sciences
Medical image annotation, clinical NLP, radiology and pathology labeling, and EHR data classification — under HIPAA-ready protocols by clinical professionals.
Financial Services
Earnings call sentiment, financial NER, ESG classification, risk event detection, and regulatory document annotation by finance domain specialists.
Retail & E-Commerce
Product image classification, visual search training data, shelf annotation, review sentiment labeling, and conversational commerce datasets.
Legal & Professional Services
Contract NLP, legal entity extraction, clause classification, and document annotation by legal professionals with jurisdiction-specific domain knowledge.
Robotics & Industrial
3D point cloud annotation, bin-picking datasets, conveyor inspection, object pose estimation, and sensor fusion training data for warehouse automation.
Media & Entertainment
Content classification, music annotation, video action labeling, sentiment datasets for media AI, and multimodal training data for recommendation systems.
Agriculture & Environment
Aerial and satellite image annotation, crop health classification, weed detection, wildlife monitoring, and field mapping from drone imagery.
Ready to build AI on data you can trust?
Whether you need a small pilot batch or an ongoing production annotation pipeline — tell us what you’re building and we’ll tell you exactly how we can help.
FAQ
Common questions about video annotation
Everything you need to know before starting a video annotation project with Synnth.
💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.
What is AI video annotation and how does it differ from image annotation?
AI video annotation is the process of labeling video footage frame-by-frame or at the clip level with structured metadata — object tracks, action labels, temporal boundaries, pose trajectories, or event timestamps. The key difference from image annotation is the temporal dimension: an object’s identity, class, and boundary must be consistent not just in a single frame but across every frame of its presence in the video. This temporal consistency requirement makes video annotation significantly more complex — and quality significantly harder to maintain — than single-image annotation.
How does Synnth maintain object identity across frames during tracking annotation?
Annotators assign a persistent ID to each object at its first appearance and maintain that ID through the full clip — including when the object is occluded, partially visible, or temporarily exits frame. Occlusion frames are flagged with metadata. Re-identification when objects reappear is cross-checked against the original track to prevent ID switches. Synnth applies automated ID-switch detection across every annotation batch before human QA review, achieving less than 0.5% ID-switch rate on standard tracking tasks.
What is the difference between action recognition and activity detection annotation?
Action recognition annotation classifies what is happening in a pre-trimmed clip — a fixed-length video segment is labeled with one or more action categories. Activity detection annotation goes further: the annotator must find when an action occurs within an untrimmed video (temporal start and end frame boundaries) and classify what that action is. Activity detection is more complex and time-intensive per hour of footage. Both are supported by Synnth, and many projects require both — clip labels for recognition model training and temporal boundaries for detection model training.
What video formats does Synnth accept?
Synnth accepts MP4 (H.264 and H.265), MOV, AVI, MKV, WebM, and raw frame sequences (JPG or PNG). For very high-resolution or RAW camera formats, we confirm compatibility during scoping. Annotations are delivered in your preferred format — COCO Video JSON, MOT CSV, AVA JSON, Kinetics-style JSON, CVAT XML, ActivityNet JSON, Waymo TFRecord, nuScenes JSON, or custom schemas.
How does Synnth handle occlusion in multi-object tracking annotation?
When a tracked object is fully or partially occluded, annotators flag the affected frames with an occlusion metadata attribute and maintain the object’s persistent ID across the occluded frames based on trajectory prediction. When the object reappears, the correct original ID is re-associated and validated against the prior track. Occlusion handling quality is a primary QA metric we track and report per delivery.
Can Synnth annotate clinical or surgical video under HIPAA-ready protocols?
Yes. Healthcare video annotation projects are staffed with annotators who have clinical knowledge relevant to the specific procedure or activity being labeled. All patient-identifiable footage is handled under HIPAA-ready data handling protocols — access-controlled annotation environments, full audit trails, NDAs, and Business Associate Agreements (BAAs) where required. Annotation work is performed only within secure, non-downloadable annotation environments.
What is the turnaround time for video annotation projects?
Pilot batches of up to 10 hours of annotated video are typically delivered within 48–72 hours at full QA standards. Annotation velocity per hour of footage depends on task complexity — bounding box tracking is faster per clip than pose tracking or semantic segmentation. For ongoing production runs, we scope velocity targets during the initial consultation and provide realistic estimates based on your specific task complexity — not optimistic projections.
How is proprietary video footage kept secure during annotation?
All video is uploaded through TLS-encrypted channels and stored with AES-256 encryption at rest. Annotation work is performed within access-controlled environments — annotators stream video through our secure platform and cannot download or export raw footage files. NDAs are signed on every engagement. For footage involving identifiable individuals, all data is handled under GDPR-compliant data processing agreements and explicit participant consent documentation.
Get started
Start your video annotation project today
Tell us your use case, action taxonomy, environment, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.
- info@synnth.com
- Mon–Fri, 9am–6pm IST
- Response within 1 business day
- No setup fees
- No setup fees
- NDA available on request
- Free pilot for qualifying projects