Research · Build · Deploy
Madusanka
Premaratne
Applied AI Researcher
Building AI systems grounded in research — privacy-preserving LLMs, semantic generalization, and on-device agentic architectures.
Privacy-First LLM Systems
Building large language models that respect user privacy isn't just ethical—it's essential for sustainable AI adoption and trustworthy systems.
Differential Privacy
Add statistical noise to provide mathematical privacy guarantees while maintaining model utility for downstream tasks.
Federated Learning
Train models across decentralized devices without exchanging raw data, keeping sensitive information local.
On-Device AI
Deploy models directly to user devices eliminating data transmission risks while ensuring privacy.
Data Minimization
Collect only essential data to reduce attack surface and privacy risks by design.
Secure Enclaves
Use hardware-based trusted execution environments to protect data during processing.
Model Watermarking
Embed detectable markers in models to track usage and prevent unauthorized distribution.
The Cost of Ignoring LLM Privacy
Privacy Violations
LLMs can memorize and leak sensitive personal data including private conversations, financial information, health records, and family secrets—leading to identity theft, blackmail, and irreparable harm.
Business Impact
Proprietary data and trade secrets become training leaks. One breach can destroy competitive advantage, trigger GDPR/CCPA fines (up to 4% of revenue), and permanently damage customer trust.
Personal & Family Harm
Private family discussions, medical consultations, and financial matters become training data—potentially exposing your most intimate details to strangers through AI responses.
Ready to build AI systems that earn trust through robust privacy protections?
What I Build
Services grounded in active research — not packaged templates. Every engagement starts with understanding the real problem.
AI Privacy & Compliance Consulting
Design and implement query-time privacy frameworks for LLM applications — semantic generalization, data minimization, and GDPR-compliant AI pipelines without sacrificing utility.
AI Knowledge & Retrieval Systems
Build multi-agent retrieval-augmented generation systems for enterprise use — from architecture design through production deployment, including on-device and edge-first configurations.
AI Search & Text Intelligence Consulting
Apply semantic generalization, embedding strategies, and NLP pipelines to solve real classification, extraction, and search problems — grounded in current research, not off-the-shelf demos.
On-Device & Data-Sovereign AI Consulting
Move inference off the cloud — design on-device agentic systems that preserve data sovereignty, reduce latency, and achieve inherent compliance. No third-party data exposure.
AI Strategy Consulting
Define your AI research agenda, evaluate model capabilities, and translate academic findings into product strategy. Research collaborations, manuscript preparation, and AI readiness assessments.
Enterprise AI Integration
Integrate LLM inference pipelines and multi-agent systems into existing products — from API design and prompt engineering through evaluation, monitoring, and responsible deployment.
Sovereign & Enterprise Tech Stack
We leverage production-grade platforms and open-source models to build secure, private, and high-performance AI solutions.
Bridging the gap between Academic Research and Enterprise Application
I am a Founder, CEO, and applied AI researcher with over a decade in enterprise software. I lead the research agenda at Knivok, focusing on privacy-preserving LLMs, query-time semantic generalization, and on-device agentic architectures.
My work translates theoretical models into production-ready software systems for clients globally, backed by first-class academic credentials from the University of Jaffna and London Metropolitan University.
Applied AI Publications & Experiments
EdgeTal
Lead Researcher & Corresponding Author
On-device agentic RAG system for privacy-preserving talent discovery — full retrieval and inference pipeline at the edge, zero candidate data transmitted to the cloud. Presented at EICON 2026 (ESOFT International Conference), Kandy — awarded Best Poster Presenter.
Gen Z Job Commitment Study
Principal Researcher
Quantitative study of 386 Gen Z employees across WSO2, IFS R&D, Knovik, and Prime One Global, testing four determinants of job commitment via SPSS correlation and chi-square analysis. Manuscript in preparation for AIRC 2026.
Book a Call
Pick the option that fits — a first conversation, a build to scope, or an organisation-wide AI readiness check.
Initial Consultation
A no-pressure first call to talk through your AI or product challenge and see if there’s a fit.
Book nowDevelopment Services
Scope a build with the Knovik engineering team — LLM systems, agentic RAG, edge AI, and integrations.
Book nowAI Readiness Assessment
A structured assessment of where your organisation stands on AI adoption, and what to do next.
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