Research Areas

Artificial Intelligence is advancing at an unprecedented pace, with researchers delving into transformative technologies and interdisciplinary applications.

At SHALINK, key research areas include machine learning, deep learning, natural language processing, computer vision, and reinforcement learning. The institute also explores ethical AI, explainable models, AI for healthcare, smart infrastructure, and sustainable development. By integrating AI with domains like biomedical engineering, finance, and augmented reality, ACAI fosters innovation that is both impactful and inclusive. Its commitment to scalable, accessible solutions positions it as a leader in shaping the future of intelligent systems and responsible tech deployment across industries. Here are some of the key research areas at SHALINK:

Generative AI (GenAI)

Shalink’s GenAI research drives new content pipelines—text, images, 3-D assets, and code—that power RayX product features across Healthcare, Retail, E-commerce, and Digital Experience platforms.

Research Focus:
  • Foundation Architectures: Refining GANs, VAEs, diffusion and transformer models for multi-modal output.
  • Text Generation: Custom LLMs for clinical summaries, legal drafts, and marketing copy.
  • Image & 3-D Generation: Rapid AR/VR asset creation for Smart Cities, Construction and Retail try-ons.
  • Audio & Video Synthesis: Voice bots, adaptive training videos, and immersive experience content.
  • Multi-Modal Workflows: Text-to-image, image-to-text, and cross-sensor fusion for CPS and Edge AI solutions.
Explainable AI (XAI)

We embed transparency in every AI stack so defence, finance, and healthcare clients can audit, trust, and certify model behaviour

Research Focus:
  • Model Interpretability: SHAP, LIME and attention-visualisation for tabular, vision, and language models
  • Human–AI Collaboration: Interactive dashboards that surface model reasoning to field engineers and clinicians.
  • Bias & Fairness Audits: Domain-specific fairness metrics for recruitment, lending, and diagnosis.
  • Safety & Trust: Robustness testing against adversarial and real-world edge cases.
  • Policy & Regulation: GDPR-, ISO- and CDSCO-aligned governance toolkits for deployment at scale
Time-Series Analysis

From IoT sensors in Smart Cities to market feeds in Finance & Asset Management, we extract patterns and predict critical events in real time.

Research Focus:
  • Forecasting: Hybrid statistical + transformer models for energy load, fleet demand, and supply-chain signals.
  • Anomaly Detection: Early-warning systems for equipment failure, fraud, and patient vitals.
  • Causality & Decomposition: Explain drivers of change in climate, manufacturing yields, and retail foottraffic.
Reinforcement Learning (RL)

RL powers autonomous decision engines for drones, warehouse robots, and dynamic pricing.

Research Focus:
  • Policy Optimisation: Proximal Policy Optimisation (PPO), SAC and model-based RL tuned for edge hardware.
  • Multi-Agent Systems: Swarm coordination for UAV surveillance and factory logistics.
  • Sim-to-Real Transfer: Domain-randomised training pipelines that shorten field-testing cycles.
  • Hierarchical RL: Layered control for complex CPS operations (e.g., smart grids).
  • Exploration vs. Exploitation: Risk-sensitive reward shaping for finance and healthcare interventions.
Edge AI

RayX delivers low-latency intelligence on wearables, drones, and industrial gateways—cutting cloud costs and safeguarding data.

Research Focus:
  • Model Optimisation: Pruning, quantisation, and tinyML for ≤ 50 MB memory footprints.
  • Energy Efficiency: Hardware–software co-design for battery-powered agriculture and safety gear.
  • Privacy-Preserving AI: On-device inference to meet HIPAA-level privacy in telehealth.
  • Real-Time Processing: Millisecond-class detection for AR HUD overlays and autonomous navigation.
  • Federated Learning: Cross-site personalisation without sharing raw user data.
Machine Learning (ML)

The backbone of all analytics offerings—from predictive maintenance to customer lifetime-value scoring.

Research Focus:
  • Feature Engineering: AutoML pipelines that ingest vision, text, and sensor feeds.
  • Model Selection: Ensemble, tree, and probabilistic models benchmarked for speed vs. recall.
  • Hyperparameter Tuning: Bayesian and evolutionary optimisation integrated with our MLOps stack.
Deep Learning (DL)

We build custom CNNs, transformers and graph networks for unstructured data at industrial scale.

Research Focus:
  • Architecture Design: Vision transformers for defect detection; audio transformers for speech analytics.
  • Transfer Learning: Domain-adapted LLMs and vision backbones fine-tuned on client data.
  • Model Compression: Knowledge distillation for mobile AR and smart-helmet solutions.
  • Beyond Classification: Unsupervised and generative DL for anomaly detection and digital-twin simulation.
Computer Vision

Vision AI fuels Retail shelf analytics, Construction crack mapping, and Defence surveillance.

Research Focus:
  • Object Detection & Recognition: YOLOv8/9 pipelines accelerated on NVIDIA Jetson.
  • Image Segmentation: Instance & semantic segmentation for medical imaging and smart-city CCTV.
  • 3-D Vision & Motion: LiDAR + stereo fusion for autonomous inspection and UAV navigation.
  • Augmented Reality: Real-time pose tracking to lock HUDs on industrial assets.
  • Vision Transformers: Next-gen models for low-data regimes (agriculture pests, rare defects).
Natural Language Processing (NLP) & LLMs

Custom language models power conversational agents, sentiment dashboards, and multilingual knowledge bases.

Research Focus:
  • Text Classification & Sentiment: Brand monitoring and clinical triage.
  • Named-Entity Recognition: Supply-chain entity extraction, legal clause tagging.
  • Question Answering & Search: Domain Q&A for e-commerce, smart manuals, and field maintenance.
  • Fine-Tuned LLMs: Instruction-tuned models hosted securely for enterprise clients; function-calling for workflow automation.
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