Srishta Technology provides data labeling and annotation services for AI teams that need clean, consistent and model-ready datasets across images, videos, text, audio, documents and multimodal data.
Image
CV datasets
Text
NLP & LLM
Audio
Voice AI
Docs
OCR & extraction
Multi-format data
Images, videos, text, audio, documents and mixed datasets
Training ready
Labels aligned with model training and evaluation workflows
Quality controlled
Guidelines, pilot batches, review and correction cycles
Flexible exports
CSV, JSON, JSONL, COCO, YOLO, SRT, VTT or custom schema
Why labeling matters
Raw data does not automatically become training data. A model needs clear labels, consistent rules, useful metadata, reviewed examples and delivery formats that match the training pipeline.
We help teams convert raw files into structured datasets for supervised learning, fine-tuning, evaluation, model improvement and AI product development.
Services
Use this page as the main annotation hub, with focused service pages for each data type.
Label objects, regions, defects, products, scenes, faces, medical images and visual features for computer vision model training.
Annotate video frames, moving objects, actions, events, activities and temporal sequences for AI systems.
Prepare text datasets for NLP, LLM fine-tuning, classification, entity extraction, sentiment analysis and moderation systems.
Label speech, speakers, timestamps, intent, sentiment, emotion, sound events and multilingual audio datasets.
Annotate scanned documents, forms, invoices, receipts, contracts and records for OCR, extraction and document intelligence.
Create datasets where image, text, audio, video or document context must be labeled together for multimodal AI systems.
Low-quality labeling
Model-ready labeling
Labeling workflow
A clear workflow reduces ambiguity and keeps labeling quality consistent as the project scales.
We define label categories, edge cases, annotation rules, output format and acceptance criteria.
Files are organized, sampled, cleaned, deduplicated and mapped with metadata where required.
A small batch is labeled first to validate guidelines, ambiguity, cost and quality expectations.
Approved guidelines are used for larger batches with annotator training and progress tracking.
Reviewer checks, sampling, consensus review and correction cycles improve consistency.
Final labels are exported in the agreed format with notes, issue logs and batch-level quality summary.
Capabilities
Project-specific rules, examples, edge cases and acceptance criteria before labeling starts.
Clear label categories for objects, intents, topics, entities, defects, emotions, actions or fields.
Reviewer checks, sampling, disagreement review, corrections and quality reporting.
Delivery in CSV, JSON, JSONL, COCO, YOLO, Pascal VOC, SRT, VTT or custom schemas.
Access rules, private workspaces, redaction guidance, PII handling and controlled delivery.
File organization, metadata mapping, batch naming, split planning and version control.
Guidelines for unclear data, low-quality samples, overlap, ambiguity and out-of-scope examples.
Feedback from model errors, reviewers and edge cases can be used to improve future batches.
Model types
Detection, segmentation, classification, OCR, visual inspection and image understanding models.
Text classification, NER, sentiment, moderation, prompt-response, instruction and preference datasets.
ASR, speaker diarization, voice assistants, call analytics and emotion recognition models.
Invoice extraction, form understanding, contract analysis, receipt parsing and OCR correction models.
Preference labels, relevance judgments, ranking datasets and search-quality evaluation data.
Datasets combining images, text, video, audio and document context for advanced AI systems.
Quality process
Labeling quality depends on clear rules, trained annotators, reviewer checks and a feedback loop that catches repeated issues before they affect the full dataset.
We do not start large annotation batches without rules. Guidelines define what to label, what to ignore and how to handle unclear cases.
A pilot batch helps test the taxonomy, confirm the delivery schema and identify ambiguity before large-scale work begins.
A review layer checks samples or full batches depending on project criticality, complexity and required accuracy.
Issues found during QA are corrected and added back into guidelines so later batches become more consistent.
Use cases
Industries
Label medical images, clinical text, voice notes, scanned records, patient support data and healthcare workflow datasets.
Prepare product images, catalog data, customer reviews, support queries, inventory visuals and recommendation labels.
Annotate documents, customer conversations, claims data, fraud signals, compliance records and financial text.
Prepare industrial data, inspection images, machine records, maintenance documents and production datasets for AI models.
Label learning content, speech, text, student responses, assessment data and multilingual training examples.
Prepare internal documents, emails, support tickets, process data, audio calls and knowledge datasets for enterprise models.
Delivery formats
COCO, YOLO, Pascal VOC, LabelMe, CSV, JSON or custom schema.
CSV, JSON, JSONL, token-level labels, span annotations or prompt-response format.
CSV, JSON, JSONL, SRT, VTT, timestamped transcripts or speaker-turn schema.
Field-level JSON, table structures, OCR correction output, page-level labels or custom extraction schema.
Frame-level JSON, tracking IDs, event timelines, clip-level labels or custom temporal schemas.
Project-specific schema aligned with your internal training, validation or evaluation workflow.
Annotation stack
Dataset readiness
We can support pilots, recurring annotation batches, evaluation datasets, data cleanup, label taxonomy improvement and model feedback-based relabeling.
Validate labels, cost, timelines and model usefulness before scaling.
Prepare labeled examples for supervised learning and model development.
Create reviewed test sets for model benchmarking and quality checks.
Use model failures and edge cases to create better future annotation batches.
Related Services
Discover specialized data annotation services for building accurate AI, machine learning, computer vision, and natural language processing solutions.
Label images with bounding boxes, polygons, segmentation masks, and keypoints for computer vision models.
Explore →Annotate video frames for object tracking, action recognition, and AI-powered video analytics.
Explore →Create high-quality labeled text datasets for NLP, LLM training, classification, and sentiment analysis.
Explore →Prepare speech and audio datasets with transcription, speaker labeling, and sound event annotation.
Explore →Book a discussion
Share your data type, volume, label categories, quality expectations, timeline and delivery format. We will help you plan the right labeling workflow.
FAQ
From pilot annotation to production-scale labeling, we can help structure your data for training, evaluation, fine-tuning and model improvement.