The document proposes using an AI-powered virtual bot called Que.AI to automate lead qualification processes. Que.AI would use natural language processing and machine learning to communicate with leads, understand their responses, and determine if they are qualified yes/no leads. This would help reduce costs, remove human errors and bias, and provide faster, more accurate automation of lead management compared to current human-driven processes. The proposal outlines implementing Que.AI in three phases, starting with email automation and expanding to social media integration and API connections to other systems.

1. 1
Using
Virtual
Bot
And
Ar2ficial
Intelligence-­‐NLP
to
improve
efficiency
in
lead
qualifica2on
and
intelligent
communica2on
with
them.
Que.AI
Improve
Produc-ve
And
Efficiency
in
Consumer
Lead
Qualifica-on

2. 2
Vikram Sareen
CEO,
Founder,
Chief
Architect
Cyber
Security+
BlockChain+
Big
Data
+
Machine
Learning
Student
Somil Asthana
Chief
Data
Scien9st
Big
Data
Technology
Expert
And
Machine
Learning
Wizard
Our Team
(Innovating since 2012 serving 38 international customers and 4 OEM deals)
5 People Team,
Kuala Lumpur, Malaysia
30 people Team,
Pune, India
4 People Team,
Sydney, Australia
Vincent Chin
Head,
Business
Strategy
Industry
Expert
with
20+
years
valued
experience
in
BFSI,
Media.
3 People Team,
Baltimore, USA
Paul Dovas
Head,
Business
Development
Industry
Expert
in
digital
media,
big
data
and
ar2ficial
intelligence.
Upasana Tewari
Head,
Project
Delivery
Agile/Prince2
cer2fied
manager
with
12+
years
interna2onal
experience
1 Person Team,
Hong Kong, China

3. 3
Our
Mission
To
enable
and
bring
new
age
automa-on
and
efficiency
in
lead
qualifica-on
through
rule
based
virtual
robots
working
with
ar-ficial
intelligence
(NLP).

4. 4
Cost
Per
Lead
Even
outsourcing
is
coming
to
be
costly
now.
USD1-­‐2
per
call
adds
to
huge
costs
if
you
have
10000
leads
per
month.
Human
Driven
Human
effort
is
needed
for
calls,
emails
and
management.
Errors
and
laziness
are
human
traits.
Varied
Response
Each
lead
can
give
different
response
or
no
response
so
how
do
you
sense
out
of
that?
Till
now
human
could
understand
only.
01
02
03
Today’s Challenges
What
challenges
do
companies
face
today
in
lead
qualifica2ons?

5. 5
Ac9onable
Insights
Every
leads
is
followed
through
24x7x365
bots
with
AI-­‐NLP
intelligence
ensuring
each
ac2on,
reac2on
is
closely
tracked
and
gives
consolidated
ac2onable
insights.
Remove
Biased
And
Error
with
intelligence
Humans
are
emo2onal
that
leads
to
bias
and
error
in
decisions
making
and
impacts
performance.
Bots
and
AI
removes
human
shortcoming
and
makes
decisions
and
improves
performance.
Fast
And
Accurate
Automa9on
Every
lead
coming
in
from
different
sources
will
be
managed
without
human
laziness.
Apply
Bot
+
AI
to
automate
the
engagement
process.
The Future through Bots and AI
Automate,
Analyze,
Conclude
Conclusion
With
AI-­‐NLP
working
in
background,
Every
lead
is
religiously
followed
2ll
it
is
concluded
as
Yes
/
No
through
person’s
response.

6. 6
Que.AI
Engine
Use
Natural
Language
Processing
(NLP)
coupled
with
deep
learning
and
automa2on
virtual
robot
to
build
a
100%
automa2on
engine
for
lead
qualifica2on.
Que.AI
uses
virtual
robot
to
rule
based
automate
communica2on
with
consumer
and
AI-­‐NLP
works
to
understand
consumer’s
response.
?
Our Proposed Solution
Pudng
Our
Skills
and
Knowledge
in
Ac2on

7. 7
Que.AI Engine
How
the
Que.AI
Engine
will
look
in
ac2on
1.
Lead
Source(s)
2.
Lead
Analyzer
and
Classifier
3.
Followup
(via
Email,
SMS,
WebChat)
4.
Intelligent
Response/Followup
(using
AI-­‐NLP)
5a.
Qualified
YES
Leads
for
further
ac-on.
5b.
Visual
Reports
for
All
States
(new,
expired,
Dropped,
Yes
etc)
5c.
APIs
for
Integra-on
With
other
systems
and
partners.

8. 8
Example: Email Driven Que.AI Flow
An
example
of
how
automa2on
will
work

9. 9
1. Go
Email
Cloud
deployed
dedicated
Que.AI
engine
will
executed
email
workflow
and
give
qualified
leads
back
through
portal
access
to
your
sales
team.
3.
Go
APIs
Integrate
deeply
with
your
exis2ng
and
partner
systems/
apps
Que.AI
engine
for
deeper
automa2on
and
produc2vity
improvement
like
sales
person
KPI,
Machine
vs
Human
Analy2cs,
Trends
And
Anomaly.
2.
Go
IVR
+
Social
A. Speech
to
Text
generates
valuable
content
and
context
for
deep
learning.
Using
Text
to
learn
and
react
gives
huge
opportunity.
B. Also
real
2me
twijer
feeds,
Facebook
feeds
and
web
chat
driven
flow
to
elevate
user
interac2ons
and
react
with
predefined
assistance
or
human
interven2on.
01
02
03
Que.AI Engine Rollout
We
propose
3
Phases

10. 10
Month
1
Month
2
Month
3
Month
4
Month
5
Month
6
Month
7
Phase
1
Non
intrusive
Email
Automa2on
(1
months)
Phase
2
Go
Social
(1.5
months)
Phase
3
Go
APIs
(in
Future)
Roll Out Time
Tenta2ve
Timeline
for
Proposed
Milestones

11. 11
Automa9on
No
human,
complete
automated
self
learning
system
Ready
for
Future
Que.AI
engine
is
ready
to
be
extended
to
achieve
much
more
in
future
with
its
self
learning
ability.
Intelligent
Deep
learning
at
its
best
will
learn
from
history
and
self
learn
daily
to
build
quan2fiable
intelligence
Happy
User
and
Staff
Que.AI
is
not
replacing
human
but
making
their
mundane
repe22ve
tasks
intelligently
automated.
Cost
Saving
+
Growth
24x7x365
machine
running
with
1000s
users
per
second
handled
with
no
error
brings
huge
cost
saving
and
adding
to
growth
Value Add And Key Benefits
Que.AI
engine
brings
solid
value
proposi2on

12. 12
Please
just
in
touch
with
us
at
vikram@blue-­‐bricks.com
Thank
you