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// Le Laboratoire Kalkin

AI & Software Consultant

Gauraang Malik

Turn ideas into AI tools, apps & websites.

Custom AI assistants, ML models, data analysis, and app and website development built around your goals.

  • Python
  • pandas
  • NumPy
  • scikit-learn
  • PyTorch
  • TensorFlow
  • LangChain
  • Ollama
  • Hugging Face
  • PostgreSQL
  • Supabase
  • Docker
  • JavaScript
  • TypeScript
  • React
  • Next.js
  • Node.js
  • Vite
  • HTML
  • CSS
  • Swift
  • SwiftUI
  • Core ML
  • MLX

Talks & Events

Technical ideas made legible through workshops, talks, and community events.

Past talk · Speaker

Cracking the Formula for AI & ML

The AI Collective × McHacks · McGill University · Plotly-sponsored · October 2025

A practical map of AI beyond language models: supervised and unsupervised learning for structure, RAG for factual grounding, and agents for planning, memory, and action. A playful embedding-space example showed how relationships in meaning space can help explain one path to model hallucination.

Past workshop · Speaker

ML, LLMs, RAG & AI Agents

Simply Voting Inc. · Montréal · March 2026

A company workshop covering data and ML fundamentals, transformer architecture, retrieval-augmented generation, and the profile–memory–planning–action model of AI agents. Teams finished by sketching real automation ideas, from sales workflows to office-fridge assistance.

Featured video · Guest

How AI Could Help Buildings Manage Energy Demand

Energi Media · YouTube

A conversation about the potential for AI to help buildings manage energy demand.

Past event · Speaker

AI Dev Day India 2026

AI Dev Day India (ADDI) · India · 2026

Presented on local-LLM hallucination mitigation through agent comparison at a day of AI, quantum-computing, and real-world adoption discussions. Sessions also explored quantum systems, production AI-agent evaluation, and responsible AI practice.

Past event · Host

The AI Collective × Deck Meetup

Montréal · April 2026 · 150+ attendees

Hosted with Deck and guest speaker Artem Avdacev on what makes a product AI-native. The discussion focused on building trust through small validated steps, then using context and memory systems to improve products over time.

Past event · Host

The Rise of Everyday Data Scientists & Prompt Engineers

The AI Collective Montréal · Le Wagon · May 20, 2026 · 95 registered guests

A completed community event about how product teams, marketers, designers, founders, and operators are increasingly working with data and prompts—not only technical specialists. The LinkedIn recap captures the room, Sarra Kallel’s talk, and the event’s practical framework for clearer prompting.

Selected Work

Research systems, local AI infrastructure, data products, and visual research communication.

GitHub · Research

Multi-Agent LLM System

A local-model comparison and agent-routing framework evolved from hallucination-mitigation research. It stores responses in FAISS, studies semantic agreement with PCA and KMeans, and weighs task fit, consensus, runtime, and energy to favour the leanest capable model.

FAISSPCAKMeansOllama
GitHub · Systems

AIOS: Local AI Operating System

A work-in-progress, privacy-first desktop automation layer. It captures screen context, extracts text and interface elements, asks a local or remote model to plan, and executes approved actions through input controls, APIs, or scripts with replayable logs and memory.

OCRLocal LLMFAISSPython
GitHub · Systems

AI Interconnect

The Apple-side node of AIOS: a SwiftUI app for macOS and iOS that unifies local MLX, Apple Intelligence, and Core ML backends, while connecting securely to the AIOS hub for devices, scenes, topology, and natural-language commands.

SwiftUIMLXCore MLAIOS Hub
GitHub · Finance

Stock Market Ensemble with Sentiment Analysis

A NIFTY 50 forecasting pipeline combining Prophet, logistic regression, random forest, technical indicators, India VIX, and VADER sentiment from financial headlines, followed by a risk-controlled strategy simulation.

ProphetRandom ForestVADERNIFTY 50
MySQL · Canva · Database Design

Recruitment Database

A recruitment database design in MySQL, presented in Canva through its core entities, relationships, and database queries.

MySQLDatabase DesignERD
Canva · ML · Health

Dental Age Estimator

A machine-learning presentation exploring model evaluation for a dental-age estimation task.

ML EvaluationHealth DataScikit-Learn
Original presentation cover: Information Security: Assessing Impact of Ecosystem on Data Collection View original presentation ↗
Information Security · Survey Research

Digital Footprints & Corporate Data Collection

How do the devices and services we use relate to the advertising we see? This exploratory study examines device ecosystems and people’s reported experience of targeted ads, opening a wider discussion about how major platforms collect and interpret everyday data.

Device EcosystemsDecision TreesData Privacy

How major platforms collect data

Data can come from information you provide, activity a service records, or other businesses. Companies can use those signals to estimate interests and personalize experiences. Collection and use vary by product, region, and privacy settings.

Google
Its policy describes collecting searches, video activity, device identifiers, and location information depending on services and settings. Cookies, pixels, and partner sites using Google services can also contribute activity data. Google describes using activity to personalize recommendations and ads.
Google Privacy Policy ↗
Apple
Its policy lists account and device information, transactions, usage and diagnostic data, and location in specified circumstances. Apple describes service-specific collection and personalization choices, and states that its advertising platform does not link its data with third-party data for targeted advertising.
Apple Privacy Policy ↗
Meta
Other businesses can share activity from their websites and services, such as purchases or games played. Meta describes using this information for ad personalization and expanding its use to other content, with availability and controls varying as changes roll out.
Meta’s data-use explanation ↗

Inside the study

A Google Forms survey shared with friends and family supplied self-reported device ownership and advertising experiences. The workflow converted responses into binary features, defined ecosystem labels, handled missing values, and explored decision trees in RapidMiner, including SMOTE for class imbalance.

What the evidence supports

The survey explores patterns in reported experiences. It does not measure data captured inside a company or establish that an ecosystem causes more tracking. SMOTE changes class balance; it does not make a convenience sample representative.

Platform context draws on the linked company publications, reviewed October 28, 2026.

Portrait of Gauraang Malik
Gauraang Malik · Montréal

Founder, Le Laboratoire Kalkin

Gauraang is a Building Engineering PhD researcher, McGill graduate, and founder of Le Laboratoire Kalkin.

The work connects intelligent systems, buildings, research practice, culture, music, and creative experimentation.

Evidence boundary: research claims require evidence; philosophy and creative inquiry offer questions, metaphors, and perspective rather than scientific proof.