We help organizations accelerate AI adoption at every step.
At Tedlite, We beileve that a process oriented approach is paramount for a successful project. Hence we have simplified the journey as below.
Align priority AI investments on a strategic roadmap.
Our experts work with your team to identify how AI can help address the key challenges facing your business. Together, we find the unique AI investments that balance creating value today with building capabilities for tomorrow. When we’re done, your business will be prepared to take concrete steps to access compounding benefits from AI.
Assemble your technical stack for scalable AI.
Working in sprints, we assess the potential of your current data, technology, and software infrastructure to deploy AI workflows and systems through a strategic lens. With a diverse team of Applied Research Scientists, domain and design experts, we get you ready for enterprise-grade AI deployments. We work together using purpose-built tools to close gaps in data, model training, and deployment deficiencies.
Empower your people to work smarter with AI.
We help you set your people up for success. Starting with one-day AI bootcamps for Executives and teams, we build your organization’s literacy and trust to work with AI.
Establish a framework for trustworthy, explainable, and responsible AI.
If you are already ahead in your AI journey, we help you identify and mitigate new risks and bias from AI, understand debates guiding AI regulation, and synthesize policies and design principles to ensure safe, responsible AI into the future.
AI and Machine Learning (ML) technology have become a major part of the armoury for many industries whether it be private companies, financial services, healthcare, or governments.
These data-driven tools are used to make ever more important decisions that can have a far-reaching impact on individuals and societies both positive and negative.
As these solutions have evolved and become more widely used, new human rights issues have been brought to light as biases are uncovered within the decision-making systems we design. This also causes legal, ethical, and brand reputation issues for the entity involved.
Beyond Analysis Tedlite can help you assess your AI and Machine Learning solutions for bias and provide the solutions to remedy this.
A lot of things have aligned to make this an exciting time for major advancements in AI.
Let’s think of AI as an iceberg. What you see as a user is just the tip — but beneath the surface lurks a behemoth support system of data scientists and engineers, massive amounts of data, labour-intensive extraction, preparation of the data, and a huge technology infrastructure. It takes a specialized team of data scientists and developers to access the correct data, prepare the data, build the correct models, and then integrate the predictions back into an end-user experience application.
Machine learning is the core driver of AI. It’s the concept of having computers learn from data with minimal programming. Machine learning works with structured data to detect patterns that provide insight. Everyday examples are personalized recommendations from services like Amazon or Netflix. In the financial arena, machine learning predicts bad loans, finds risky applicants, and generates credit scores.
Tedlite uses various tools and platforms for successful DevOps implementation. Jenkins, TeamCity and Travis are used for Automation; Chef, Puppet Labs and Ansible for Configuration Management; OpenStack, Docker, and VMWare for Compute Virtualization; and Flocker, OpenZFS and Delphix for Data Virtualization.
Using a multitude of analytical programmes, algorithms are developed and refined within a process in accordance with your business questions. Machine learning looks at the history of your current data and detects patterns within it, and then adjusts its future actions accordingly. Its main aim is to both clean your data, and make predictions towards future data sets.
Machine learning statistical methods such as clustering, regression and classification are used in predictive analytics.
Deep learning is AI that uses complex algorithms to perform tasks in domains where it actually learns the domain with little or no human supervision. In essence, the machine learns how to learn.
While there’s lots of exciting experimentation happening with deep learning, most practical applications you’re familiar with are based on image analysis. With image analysis, a computer learns to classify random images by analyzing thousands or millions of other images and their data points. For example, consumer apps like Google Photos and Facebook use deep learning to power face recognition in photos.
NLP is AI that recognizes language and its many usage and grammar rules by finding patterns within large datasets.
One application of NLP that’s gaining traction is sentiment analysis within social media. Computers use algorithms to look for patterns in user posts across Twitter, Facebook, or other social networks to understand how customers feel about a specific brand or product.
It’s difficult to tell at this stage, but AI will enable many developments that could be terrifically beneficial if managed with enough foresight and care. For example, menial tasks could be automated, which could give rise to a society of abundance, leisure, and flourishing, free of poverty and tedium. As another example, AI could also improve our ability to understand and manipulate complex biological systems, unlocking a path to drastically improved longevity and health, and to conquering disease.