Research
Deep-tech AI research from the IIT ecosystem
Founded by professors and scientists from top IITs, our research spans NLP, Computer Vision, and Generative AI, with a focus on real-world, deployable systems.
Natural Language Processing
Our NLP work focuses on building deployable, domain-specific language models that extract intelligence from unstructured, multilingual, and multimodal data, including documents, web pages, audio, video, and scanned PDFs. We help organizations unlock insights from complex data including policy documents, regulations, citizen queries, contracts, and media content.
Question Answering
Answer domain-specific queries with citations from trusted documents.
Summarization & Briefing
Generate concise, actionable summaries from long reports or policies.
Speech-to-Text
Convert audio content into searchable transcripts.
OCR & Visual Reasoning
Digitize and understand scanned or handwritten documents using OCR + VLMs.
Named Entity Recognition
Extract structured information like names, locations, and policies.
Document Classification
Automatically tag and route documents based on content and intent.
Intent Detection
Understand user goals in conversations for accurate routing.
Multimodal Data Fusion
Integrate text, image, speech, and video for richer insights.
Sentiment Analysis
Extract opinions and trends from social, internal, and public feedback.
Computer Vision
We use state-of-the-art approaches in computer vision and deep learning to help organizations extract, analyze, and understand information from images and videos. By integrating computer vision technology into existing systems, organizations can automate processes and reduce operational costs.
Object Detection
Pedestrian detection, medical imaging, image search, and object counting.
Face Recognition
Retail crime prevention, facial biometrics, automated attendance.
Video Analytics
Patient monitoring, predictive maintenance, surveillance, autonomous vehicles.
Activity Recognition
Security, fitness tracking, fall detection, elderly care, event detection.
Emotion Recognition
Market research, e-learning, counselling, driver fatigue monitoring.
Pose Estimation
Gait analysis, animation, gaming, virtual/augmented reality.
Depth Estimation
Robotics, trajectory estimation, augmented reality, haze removal.
Semantic Segmentation
Bio-medical diagnosis, autonomous driving, satellite image processing.
Object Tracking
Sports analytics, human-computer interaction, autonomous driving.
AI Knowledge Base
Understanding our technology
What is On-Premise Generative AI?
On-premise generative AI refers to the deployment of large language models and generative AI systems entirely within an organization's own infrastructure. Unlike cloud-based AI services, on-premise deployment ensures that sensitive data never leaves the organization's network, providing complete data sovereignty and compliance with regulatory requirements.
Enterprise RAG Systems
Retrieval-Augmented Generation (RAG) systems combine the power of large language models with an organization's proprietary knowledge base. By retrieving relevant documents before generating responses, RAG systems provide accurate, citation-backed answers grounded in verified enterprise data.
Government AI Infrastructure
Government AI infrastructure encompasses the secure, sovereign computing systems, models, and deployment architectures designed specifically for public sector use cases. This includes citizen-facing chatbots, policy intelligence systems, and e-governance automation platforms that meet government security and compliance standards.
Secure LLM Deployment Architecture
Secure LLM deployment architecture refers to the design patterns and infrastructure required to run large language models in environments where data privacy, network isolation, and access control are paramount. This includes air-gapped deployments, encrypted data pipelines, and audit-ready inference systems.
Collaborate with us on research
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