XFinLabs mi-AI is a Retrieval Augmented
Generation (RAG) based AI system. RAG systems are ideally suited for
enterprise environments as it allows companies to control what data
sources their employees use. In the diagram below, an employee
uploads their data into mi-AI. (This
data can come from a variety of sources such as files, company
databases or even the web.) A unique database is then created that
only stores the information provided by the user. (This helps secure
company data as well as ensure the accuracy and relevancy of any AI
responses.) Once created, users can use the power of semantic
search to query their data as well as automate specific tasks and
operations. With XFinLabs mi-AI, a
company can have as many private AIs as they want.
A critical component of the XFinLabs mi-AI system is the ability of
a company to tailor how the AI is trained and responds. This is
often referred to as the process of giving your private AI a
personality. For example, with mi-AI
you can tell your AI that it is a domain expert in certain
technologies and should respond to queries in a professional tone,
as opposed to a more casual tone. You can even narrow its core
competency to a specific topic. Such as informing the AI model that
the data contained in the private AI database is only related to
"the industrial revolution circa 1800 to 1840". Once these prompts
have been entered into mi-AI, the next
time a person does a search, anything outside of the above domains
will either be ignored or referenced as immaterial. Because of this
flexibility and granularity, (and potential for misuse) prompt
engineering should only be carried out by company executives who can
control exactly how their AI responds based on the business they are
in.
One of the biggest challenges in leveraging AI is in understanding
how to use AI to discover trends, patterns and anomalies. Tasks that
analysts can spend countless hours, days and weeks trying to
identify. Rather than asking your private AI about employee
salaries, perhaps a better use of the tool is to ask, “are our
employees being fairly compensated based on market rates and what
competitor A and B are offering.” This is where AI excels, but it
requires users to understand the best way to first frame a question
and secondly, know where to go to find the facts. To help, XFinLabs
mi-AI provides Automated Workflows
where users can point mi-AI to pull
data from internal and external sources, searching for specific
words or phrases and then running an analysis at a specific time or
on a schedule. This feature is particularly useful when doing market
research or engaging in due diligence. With Automated Workflows
analysts are now to have their private AI churn through 10ks, read
press releases or catch mentions of potential transactions or events
that may materially affect a business, product or market.
One of the most powerful features of RAG based systems is there
ability to hyper tune the AI model. Hyper tuning affects the
underlying algorithms to obtain more accurate or relevant responses.
It is much like adjusting the knobs on a radio to get the clearest
signal. For many, the sound that comes out may be more than
adequate, but for the audiophile (in our case an analyst) who can
pick up disturbances or static, the ability to find the right
wavelength ensures crystal clear sound. For AI, this means better
results that a tailored specifically to the user and/or
organization. As expected, with this level of control comes some
responsibility. If hyper tuning a model for a company, the user has
to be well versed in the type of data that private AI model is
looking for. This is another key reason why XFinLabs
mi-AI was created. It allows a company
to create one private AI with it’s own personality and frequency
(hyper tune), while another private AI (looking at the same data)
may be configured with a different personality and level of tuning.