An innate passion for AI-driven technologies, data visualization techniques and helping companies engage opportunities with data.
Watch our past projects below or get in touch with us to learn about the other ways we can help your company.
Tell Compelling Stories
Deliver uniquely interactive experiences that inspire action and emphasizes clarity.
Key Mobile Analytics in one place
The application visualizes the gamer base on a marketing quadrant, display gamer base growth (user acquisition) over time, incorporate forecasting functionalities, and allow the client to predict whether a gamer would convert to a paying customer based on their in-game activities from the first week by uploading a CSV file directly onto the QuadrantX tab.
We make a lite copy using dummy values to demonstrate the core feature sets.
A global perspective on the Covid-19 pandemic
The project is open-source and the code is also available on GitHub.
Supertype is exceptional in its service. Sets up the tracking and end-to-end analytics infrastructure for our e-commerce platform in no time.
Founder, Three Kraters Pte Ltd, and Venture Partner at TNB Aura
Automate with Software
Multiply your speed of execution and operational efficiency through carefully engineered solutions.
Automatically build quizzes from plain text files
quiz.mdand generates the corresponding quizzes to be saved its database.
For learners, Corgi offers a free-for-all buffet-style learning environment by aggregating programming and data science courses from GitHub. You can participate in any course freely, and your attempts will be rewarded with badges on your profile wall. You can bookmark courses for each access, look at others who have completed the course, and you can freely participate in any of the programming courses you find.
Empowering education professionals
Feature sets include:
- Backend Administrative Management
- Company-wide statistics
- Instructor Analytics
- Personal Accomplishments
- Survey Integration
- Instructor Reviews
Solve Predictive Problems
Incorporate predictive features into your service of applications
Complete user's sentences with Language-based AI
The language model we used is described in Large Language Models in Machine Translation by the engineers at Google.
Assisting the Hearing Impaired Community
Turn features into products
Introduce novel products, not new features.
Programmatic PDF report generation with NLP & AI
A report generation pipeline that takes as input an AppStore or Google Play Store URL and outputs a custom PDF with aggregated summary and text analysis of app user reviews in minutes.
It uses a keyword extractor routine developed in-house to handle much of the language processing tasks related to the identification and grouping of these reviews into topics (“unfriendly paywall”, “long tutorial”, “app crashes”, “poor customer service”, “very fast loading time” etc), a task known as topic detection in the natural language processing (NLP) space.
The program also uses components of Spacy and NLU by John Snow Labs to meaningfully sift through up to tens of thousands of user reviews in determining user sentiment, with full support of 20+ languages.
The PDF that is generated can be customized to include the client’s logo, as well as an ending CTA (call-to-action) slide, making it perfect for mass lead-generation and client outreach.
Enable Developers & change-makers
We publish and maintain open-source toolkits that the larger developer community can benefit from.
Build Text Embeddings Faster
Elang is a python package that helps NLP (natural language processing) researchers, Word2Vec practitioners, educators and data scientists be more productive in training language models, visualizing and experimenting with key concepts in word embeddings.
It has a number of built-in utilities to generate a corpus from Wikipedia, to cleansing and transformation, and visualizing word embeddings.
pip install elang
from elang.word2vec.builder import build_from_wikipedia build_from_wikipedia(n=3, lang="id")
Quick Start Guide:
Network Analysis From Email
A python package that provides network graphing utilities and simple header analysis features for email/mailbox data. A sample
.mbox file is provided, but you can obtain your own mailbox from your email service provider. Gmail users can export your mail data using the Google Takeout service .
pip install emailnetwork
from emailnetwork.extract import MBoxReader from emailnetwork.graph import plot_directed
reader = MBoxReader('path-to-mbox.mbox')
# plot a single directed graph the email at index 3 plot_single_directed(reader,3)
Quick Start Guide:
Open Source Education Initiatives
Our industry practitioners develop comprehensive, open-source programs for computer science education. Free forever.
- Symmetric and Asymmetric Key Cryptograpgy
- Encryption and Decryption with RSA
- Secure Hash Algorithms (SHA-256, Keccak)
- Merkle Tree and Merkle Proof
- Consensus Mechanism
- Proof of Work
- Bitcoin’s Block Generation
- Coinbase Transaction
- Mining Reward
- Ethereum’s Smart Contract
Learn Blockchain (Academy)
We created an interactive workbook that help the average reader learn about the engineering marvel of blockchain, the Bitcoin protocol, and the cryptographic ideas that made the Bitcoin protocol possible. It is created to facilitate a self-learning approach guided by interactive quizzes, live code editors, experiments and other interactive elements to help the reader acquire theoretical knowledge through experimentation.
Among the features include:
- Content authored in MDX
- GraphQL-powered and GraphiQL integration
- Dark / Light theme switcher
- Syntax Highlighting with Prism
- Directly editable through GitHub integration
- If you spot a typo or error, hit “Edit” and submit the edit directly on GitHub
- Progressive Web App (Works Online, Offline, even in Airplane mode)
- Sidebars on both sides, prev / next navigation
- Search integration with Algolia
- Google Analytics integration
- Live code editor with react-live
- Beautiful, interactive React UX components with Ant Design
- Beautiful, interactive Charts with Chart.js
- LaTeX support through react-katex
- Full integration with crypto-js, a library of crypto standards