Artificial intelligence
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Chatbot
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Robotic Process Automation
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Face Recognition
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Multi-Channel Smart Chatbot
A chatbot “conversational agent” is an artificial intelligence (AI) platform that can run a conversation (or a chat) with a user. Chatbot provides fast, straightforward and accurate answers across any application, device or channel. Chatbot interacts with customers like a human and cost little to nothing to engage with. By addressing common customer inquiries, Chatbot helps agents focus on complex use cases not repetitive responses.
Many AI chatbots offer a remarkably authentic conversational experience, in which it’s very difficult to determine whether the agent is a human or a bot. This technology surely saves your time in giving the reply to a customer, also serve the best values in B2B and B2C sectors. Many businesses have been developing their own chatbots to assist customers with online shopping. For example, instead of going to a company's website and searching through results to find what you want, you can simply ask a chatbot to recommend a certain item. You can also develop a chatbot to give live support even on the weekend, long vacations, etc.
Why Chatbots are so important for Your business?
Proactive engagement for every qualified lead
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24×7 - Always-Available
Customer Support
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Proactive Customer Interaction
Instant answers
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Reduce Cost
with speed and smart solution
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Real-time conversation |
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Artificial Intelligence (AI) Power |
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Easy implementation |
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Smart reporting and statistics |
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How does a chatbot work?
Chatbots primarily use artificial intelligence to talk to people and give relevant content or suggestions. They can function based on a set of instructions or use machine learning. A chatbot that works based on rules is usually quite limited. That is designed to respond to fixed commands. So, if a person asks it the wrong thing, the bot will not understand what the question means, and therefore, will not provide an appropriate response. The intelligence of the bot solely depends on how it is programmed.
On the other hand, a chatbot that uses machine learning works better because it has an artificial brain. The bot understands not only commands but also language. The user, therefore, does not have to use precise words to get accurate or useful responses. Moreover, the bot learns from interactions it has with users and can deal with similar situations when they arise later. It essentially becomes smarter as it talks to more people.
Robotic Process Automation
Automate Your Routine
Robotic Process Automation is a technology with artificial intelligence (AI) capabilities to manage high-volume, repeatable tasks that previously required humans to perform. The robot can intercat with any system the same way human do - from queries, validating data to making calculations - so there’s no need to change underlying business systems to automate.It enables business to reduce operating costs by executing high-volume repetitive tasks with zero error rates.
RPA is your Digital Workforce. Show your bots what to do, then let them do the work.
Why RPA is so important for Your business?
Where Can RPA Be Used?
1. Highly Manual & Repetitive Processes
2. Processes With Standard Readable Electronic Input Type
3. Changeable Processing Method or System Change
4. Rule Based Processes
5. High Volumes
6. Automation Savings – in FTEs
7. Low Exception Rate
8. Mature & Stable Processes
RPA Use Cases
IT Process
• Fraud Detection
• Password unlock and reset
• IT Onboarding
• read email ,attachment and take actions
• read helpdesk requests and take actions
• 8- banking
Supply Chain Process
• Inventory management
• Demand & supply
• Planning
• Invoice & contract
• management
Customer Service Process
• Address change
• Password reset
• Payments
• Scheduling appointments
• Order modifications
BANKING Process
• Check ATM Services is up or down , and do restart action
• Mortgage & Personal Loans Evaluation process
• Credit Card Processing
• searching in different sources
• Manual& repetitive tasks
• Account Closure Process
Human Recorses Process
• Payroll
• Onboarding & offboarding
• Benefits administration
Sales Process
• Retail Process
• Product Categorization
• Automated Returns
• Trade Promotions
• Inventory/Supply Chain Management
Automate Your Routine
Image recognition is reliant on deep learning technology, an advanced type of machine learning, and the modern wonder of artificial intelligence. Typical machine learning takes in data, pushes it through algorithms, and then makes a prediction; this gives the impression that a computer is “thinking” and coming to its own conclusion. Deep learning differs in how it’s able to determine if the conclusions are correct all on its own, given enough time.
Why Image Recognition is so important for Your business?
1- Nowadays security systems depend a lot on this technology for face recognition
2- Vital component in robotics such as self-driving vehicles
3- Used in image search engines like Google or Bing
4- Can be very beneficial in medical field as being used in disease detection such as detecting cancer through X-Ray images
5- Used in robotic navigation systems to track motion of objects or camera tracking
How Does Image Recognition Works?
The way image recognition works, typically, involves the creation of a neural network that processes the individual pixels of an image. Researchers feed these networks as many pre-labelled images as they can, to “teach” them how to recognize similar images.
Neural networks use algorithms that are layered next to each other. This makes each algorithm contingent on the outcomes of the other surrounding algorithms. This creates a process that tries to simulate the logical reasoning that we use as humans (and why we call it “artificial intelligence”). For image recognition, the kind of neural network used is called convolutional neural networks.
When we see something, our brain makes sense of it by labeling, predicting, and recognizing specific patterns. A computer using convolutional neural networks (CNNs) processes information in a similar way, but it does so by using numbers. Where we recognize patterns through our sense of sight (in conjunction with our other senses), a CNN does so by breaking images down into numbers.
A neural network will learn over time if its predictions are accurate. Like with anything else, it takes a lot of training for computers to get their predictions right; they don’t automatically know how to classify what objects are called in the real world.
For example, consider we need to detect dogs in give pictures, the developers would have fed an AI image recognition model with hundreds to thousands of pictures of dogs. The AI then develops a general idea of what a picture of a dog should have in it through the CNN. So, when you feed it an image of something, it compares every pixel of that image to every picture of a dog it is ever seen. If the input meets a minimum threshold of similar pixels, the AI model declares it a dog.
