By Data Type
- Text data for documents, emails, articles, and messages.
- Image data for photos, diagrams, screenshots, and X-rays.
- Audio and video for speech clips, surveillance, and calls.
- Structured records from forms, tables, and databases.
SecureAnno is a Human-in-the-Loop data labeling partner for AI-driven organizations. We provide NLP Tagging, Legal Data Labeling, and AI-assisted operations workflows, ensuring high accuracy where automation alone is not enough. By combining generative AI with expert human validation, we help teams build smarter, faster, and more reliable AI systems.
Complainant Suresh Mehta filed FIR No. 0342/2024 at Koramangala Police Station, Bengaluru, against ABC Constructions Pvt. Ltd. for property fraud at Plot No. 14, Whitefield Main Road, Bengaluru - 560066. The case was heard by Justice Anand Rao on 12th April 2024.
SecureAnno is a Bengaluru-based Human-in-the-Loop data labeling and operations execution company, delivering high-quality ground truth for next-generation AI systems.
We specialize in complex, domain-specific datasets across legal, finance, healthcare, retail, geospatial, and enterprise AI ecosystems.
Our teams blend human intelligence with strict quality frameworks to deliver precise, scalable, and reliable ground truth for mission-critical AI applications.
To become India's most reliable Human-in-the-Loop annotation partner - enabling legal-tech, gov-tech, and AI teams to build accurate, unbiased models trained on contextually rich, professionally labeled Indian data.
SecureAnno is also being shaped as a women empowerment platform across India, creating flexible digital work opportunities for women who bring native-language fluency, regional context, and domain understanding into AI training and review workflows.
SecureAnno supports the full annotation lifecycle across document-heavy, image-heavy, and operations-heavy workflows. That means more than labels: we provide domain context, QA rigor, and dependable execution support.
Four pillars that set SecureAnno apart from generic data labeling vendors — and make us the preferred HITL partner for AI teams building on Indian data.
We combine generative AI pre-labeling with expert human validation. AI handles the repetitive patterns, humans handle the judgment calls — delivering speed without sacrificing accuracy.
Our annotators aren't generic crowdworkers. They're trained legal professionals, paralegal researchers, and regional language experts who understand Indian FIRs, court orders, and property documents at a structural level.
Whether you need 1,000 records or 500,000 — our pipeline scales without delay. Bengaluru-based operations keep costs competitive while maintaining output quality that exceeds offshore benchmarks.
Every batch goes through multi-layer quality control — automated rule checks, inter-annotator agreement scoring, and senior human review. We guarantee 99%+ accuracy on sensitive legal and compliance data.
Upload your raw data - Fintech, Legaltech, Proptech, or ops workflows - and define your annotation requirements. We finalize a labeling schema together.
Our trained domain annotators label every record using specialized tooling, guided by your schema and our internal quality guidelines.
Automated rule checks plus senior human review ensure inter-annotator agreement above 95% and catch edge cases before delivery.
Receive your labeled dataset in JSON, CSV, CoNLL, GSheet, or any format you need. We iterate based on your model's performance feedback, at no extra cost.
HITL annotation is the invisible backbone of India's most accurate AI systems. Here's everything you need to know.
Human-in-the-Loop (HITL) annotation means a trained human expert labels, validates, or corrects data at each stage of the AI pipeline - not just at the start. This is critical for sensitive domains like legal records, government documents, and property data where automated labeling alone is not reliable enough.
Indian legal documents use domain-specific language, regional script variations, legacy formats, and cited sections of law that require genuine legal understanding - not generic text labelers. That's why SecureAnno employs trained legal professionals for these tasks.
India has 22 official languages, hundreds of transliteration styles, and address formats that include landmarks, khasra numbers, tehsil names, and PIN codes - often in the same field. Accurate parsing requires trained human judgment, not just regex rules.
Research consistently shows that model accuracy improves more from higher-quality labels than from larger noisy datasets. Expert HITL annotation with strong QA pipelines outperforms cheap crowdsourcing - especially for specialized Indian-language documents.
Reinforcement Learning from Human Feedback (RLHF) - the technique behind top LLMs - requires humans to rank and rate AI outputs. SecureAnno supports RLHF pipelines with trained annotators who evaluate output quality for domain expertise AI models.
Our annotators handle Hindi, Tamil, Telugu, Marathi, Bengali, Kannada, Malayalam, and 12+ other Indian languages with native fluency - enabling truly accurate multi-lingual labeling for Bharat's diverse AI ecosystem.
Ready to get started? Fill out the form and our team will reach out within 24 hours to discuss your project requirements and share a custom quote.