Sonasoft-AI-solution
                     The Sonasoft 
AI Solution
© 2019 Sonasoft Corp.  All Rights Reserved.
Introduction
AI is a disruptor, transforming business processes, bringing new opportunities, and 
helping companies leverage the vast oceans of data they collect. There are myriad use 
cases, from stock control and forecasting through to identifying efficiency savings in 
your back office processes. The problem is, AI projects need a team of highly skilled 
data scientists and engineers, along with access to significant computing power. Even 
then, a typical project will take around nine months to complete. This is where 
Sonasoft comes in. We offer a complete AI solution, including skilled data scientists to 
assess your requirements, a world-class AI platform that creates models autonomously 
and almost 20 years experience in data storage and processing. The upshot is that we 
can solve all your AI needs in just 4 weeks instead of 9 months.
An AI 101 
Artificial intelligence is the term for any computer system that is able to 
emulate some aspect of intelligence. Merriam-Webster defines intelligence 
as:
the ability to learn or understand or to 
deal with new or trying situations.  
Also, the skilled use of reason.
At Sonasoft, we use  a slightly different definition. Intelligence is knowing 
what to do next or in future, based on recently gained knowledge 
(knowledge of the present) and our experience (knowledge of the past).
Human intelligence is defined as a “general” intelligence. That is, we are 
able to apply our existing knowledge to solve completely new or abstract 
problems. We do this constantly, usually unconsciously. Most artificial 
intelligence is “narrow”. Typically, this means it only has a very specific 
application. As a simple example, a computer can be taught to recognize 
pictures of cats. But that same computer can’t recognize dogs, unless it is 
retrained. 
Most AI is based on the concept of machine learning. Here, the computer is 
taught to recognize certain patterns in data. It then applies this learning to 
spot the pattern in new data. There are 3 forms of ML. 
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Supervised learning uses known, labelled data for training. E.g. you show 
a computer thousands of labelled photos of animals and teach it to identify 
the ones that are cats. 
Unsupervised learning uses unlabelled data. The computer simply tries 
to identify any interesting patterns within the data. Typically, this might be 
used to identify clusters of similar data.
Reinforcement learning uses unlabelled data, but each time the computer 
identifies something correctly, it is “rewarded”. This is rather like how a 
human infant starts to learn.  
What AI can do for your business
There are numerous ways in which AI can transform businesses. Broadly, use cases fall 
into 3 classes: Anomaly detection, forecasting and planning, and knowledge discovery. 
There are other use cases, but they are less widely used. Let’s look at each of these in 
turn and see what it takes to create a project.
Anomaly detection
This is about finding anomalies or outliers in historical or real-time data. Anomaly 
detection has applications in many industries. Here’s just a selection:
Finance. Anomaly detection can be used to identify 
fraudulent financial transactions instantly. It does this 
by recognizing unusual patterns in spending behavior. 
An AI system is able to spot the difference between 
someone traveling with their credit card and someone 
using stolen credit card details.
DevOps. Using AI anomaly detection, you can identify 
an impending failure before it impacts your customers. 
Often, failures are presaged by changes in behavior in 
your backend. E.g. database queries taking a bit longer 
to return.
Cybersecurity. The biggest cybersecurity attacks 
happen when a hacker gets access to your system 
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by stealing a password. Typically, they will then try to access sensitive data. Anomaly 
detection can help spot unusual behavior like this.
Forecasting and planning
Many businesses rely on accurately forecasting future demand. This allows them to plan 
resource allocation, forecast profits, and streamline their business. Forecasting like this 
requires analyzing and modeling historical data and then extrapolating the model into 
the future. This can also be done in reverse. Given a future requirement, when do you 
need to get resources into place to meet it. Concrete examples of this include:
Just-in-time manufacturing. Here, it is critical to ensure all the required parts are 
manufactured and delivered at exactly the right time. This approach to manufacturing 
was pioneered by Japanese motor manufacturers and is so effective it allows one of 
Nissan’s car plants to produce a new vehicle every 2 minutes.
Sales forecasting. Businesses need to predict how much stock they need. This sort of 
forecasting is incredibly difficult as you need to predict both what products will be in 
demand and when the demand will come. AI forecasting can offer real insights based 
on past consumer behavior, current hype, and even factors such as long-range weather 
forecasts.
Knowledge discovery
One of the more unusual uses cases for AI is knowledge discovery. Here, you teach an 
AI to discover new patterns in data and to identify new knowledge. 
eDiscovery. Electronic discovery or eDiscovery is used to analyze and find new 
information relating typically to civil litigation, patent cases, etc. It involves analyzing 
both the data itself and the meta-information related to it. eDiscovery may also be used 
during cases relating to data protection e.g. breaches of HIPAA. 
Patent discovery. The traditional approach to patents is through invention. But 
increasingly, there’s an industry in analyzing existing patents to identify areas where 
there’s a missing patent. The systems can even suggest exactly what the new patent 
should be and identify targets for selling it.
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A typical AI project timeline
All the above forms of AI are applications of machine learning (ML). Creating an ML 
project is time-consuming and requires expertise. To give an example we will look at the 
steps needed to train a supervised learning model. 
1 Scope the problem. The first step is to define the problem and understand if you 
have suitable data to work with. This stage will take several weeks to complete.
2 Get the data. Now, you need to process your data to get your data into a form 
where it can be analyzed. This can be especially problematic with historical data 
since often the formats are different or could have changed over time. Again, this 
process will take weeks.
3 Migrate to the cloud. While it is possible to run AI models locally on-premises 
or on GPU-enabled laptops, this is an inefficient approach. So, you really need to 
move the data into the cloud. This is because most real-world ML models need the 
enormous computational and storage resources of the cloud. If your team includes 
people with suitable expertise, this can probably be done in just days. Otherwise, 
it could take much longer.
4 Clean up the data. At this stage, you need to pre-process the data. This includes 
cleaning, filtering, and potentially manually labeling the data for supervised 
learning. This whole process is slow, and many enterprises tell us that preparing 
data for AI is one of their big challenges.
5 Select ML models. Having done this, your data scientists can start trying to find 
suitable ML models to analyze the data. Choosing the correct model is key, and it 
is often based on experience and gut instinct. 
6 Train and verify the model. The next stage is to start training the model. For 
this, you need good quality training data. Typically, this means you need to 
further process the data you are analyzing. Most ML models involve 10-20 control 
parameters (called hyper parameters) which need to be iteratively fine-tuned to 
arrive at best accuracy. Today AutoML promises  “Automated Hyper Parameter 
Tuning”. However, these parameters are also dependent on other choices made in 
step 4, like embedding type, scaler used to normalize data, shape of data, etc. This 
will take days to do, and typically, you will have to repeat the process many times. 
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7 Validate the model is fit for purpose. Finally, you will have a trained model and 
can test whether it is suitable for the job needed. Validation takes days to weeks, 
depending on the sort of task involved. 
Overall, this process will take 6-9 months to complete (from defining the problem 
to ending up with a working model). Worse still, this model will be so specialized, it 
will only be applicable for a single function. If you want to create a new model to do 
something else, you will have to start from scratch.
The Sonasoft approach
Sonasoft are experts in leveraging AI to extract meaningful business insights from 
your data. We offer a complete package and are able to condense your 6-9 month AI 
project down to just days. Our complete package consists of three key elements.
Data science
Getting your data into a suitable form for AI is often the hardest part of any 
transformation journey. But our large data science team are experts in migrating data 
into the cloud. We can also share our knowledge and advise you on what data you 
need to collect to achieve your requirements. Overall, our knowledge and expertise can 
save you months of wasted effort as well as saving you a fortune in hiring your own 
team.
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NuGene
NuGene is our proprietary AI engine. NuGene differs from most AI engines in 2 key 
ways. Firstly, it is an autonomous AI for creating machine learning models from your 
data with no human input. Secondly, it is a universal narrow machine learning platform. 
By that we mean it isn’t limited to just using a few ML models. NuGene understands 
all the latest forms of machine learning, and will always find the ideal one for your 
application. 
Raw Data Learn  Test Train Predict
with Expert for causation
NuGene – the first Universal Narrow AI
NuGene was developed as an AI to create functional narrow AIs, hence the 
term “universal narrow AI”. NuGene will take your data and your desired 
outcome and then start to create and test machine learning models. 
Importantly, NuGene wants raw data. This means that it isn’t being biased 
by the assumptions made during data preprocessing.
Like humans, NuGene observes cause and effect based on identified 
anomalies and lists a variety of causal hypotheses. Because it handles raw 
data, NuGene is free to find and test any potential patterns in the data. 
It then uses multiple unsupervised learning techniques to establish the 
hypotheses. NuGene rigorously validates its hypotheses to differentiate 
between correlation and real causality, allowing it to truly learn from the 
data. Finally, it is able to take these hypotheses and construct detailed ML 
models.
NuGene can also generate charts and graphs to illustrate its understanding 
of the data. This allows human experts to validate the model and intervene 
to steer it in the right direction. This avoids the issue where NuGene may 
lack some critical data or insight that the human experts know. However, 
you should be aware that this risks including bias. 
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Data expertise
Sometimes, you may know what you want to achieve with AI but lack the appropriate 
data. In such cases, we can help you set up the data gathering you need. As your data 
is collected, NuGene will start to process it to see if it can see anything significant. Once 
it has enough data, NuGene will generate the model you need.
The Sonasoft difference
Sonasoft’s integrated AI solution stands out for four main reasons.
Industry and application agnostic. Our AI solution can be applied to any use case, in 
any industry, with any sort of data. This helps us to stand out against the opposition. 
The flexibility of our approach is down to the expertise we have in data science, 
coupled with the unique way NuGene analyzes your raw data.
Broad learning, not narrow feature extraction. Most data scientists talk about “feature 
extraction”. They set out with the goal of feature engineering your data to identify a 
specific feature. This narrow approach can mean many important features are missed, 
and severely limits the learning potential of any model. By contrast, NuGene looks at 
the bigger picture and will find any interesting patterns in the data. 
Causality, not just correlation. One of the 
unique differences with NuGene is that it 
understands the mantra “correlation does Real Causality Causal Hypotheses; 
Real Corellation
not imply causation”. Having identified a 
possible correlation, NuGene will then test the 
hypothesis thoroughly before deciding if it is 
Real Causality
true causation. 
Time series are critical. The central insight for Sonasoft’s AI experts was the realization 
that all data is time-dependent, something all our competitors miss. Take, for example, 
data for predicting bank loan defaults. Without an understanding of the wider temporal 
aspects like the macroeconomic climate, this data is useless. You can’t compare a loan 
default in 2009 with a potential default in 2019. In NuGene, all data is entered as a time 
series. And the data isn’t just numeric. NuGene also understands free text, images, 
sound, and structured data.
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Conclusions
Sonasoft’s integrated solutions allow any business to leverage AI in just a 
few weeks. Moreover, our AI models are not limited to solving one problem. 
This makes our offering unique, and will save you months or even years of 
development effort. Add in the efficiency savings the AI models themselves 
bring and you get a truly transformative impact on your business.
© 2019 Sonasoft Corp. and/or its affiliates. All rights 
reserved. This publication may not be reproduced 
or distributed in any form without Sonasoft’s prior 
written permission. While the information contained 
+1 (408) 708-4000 in this publication has been obtained from sources 
believed to be reliable, Sonasoft disclaims all 
[email protected] warranties as to the accuracy, completeness or 
adequacy of such information.
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