Highlights from our portfolio of AI projects including marketing, quality, portfolio, financial and insurance analytics solutions demonstrating the versatility and power of domain expertise, AI and Tableau's platform

1. © ai-one inc. | August 2017
ai-one’s Analyst
Toolbox
Text Analytics
Services
Tom Marsh |
August 2017

2. © ai-one inc. | August 2017
Who We Are | The Analyst Toolbox
Our partners are enterprise and government analysts, domain
experts, and specialists in the application of the latest AI and BI
analytics and visualization tools.
We are domain experts with proprietary A.I. technology,
applications and data sources used to provide C-suite
analysts and CIO/IT with services, software and analytics
to increase productivity, speed, savings and insight.

3. © ai-one inc. | August 2017
What We Sell | Solutions
ai-one’s Analyst Toolbox is a set of tools used to create solutions
Solutions are delivered from the cloud “as a Service”
Solutions include BrainDocs, the data, metadata, knowledge framework
(agents), dashboard/UI (Tableau) or API and maintenance
Value Proposition
• Leverage existing domain knowledge and roadmaps
• Can be applied to cross functional use cases
• Are flexible, fast and scalable
• Integrates with existing enterprise applications
• Improves accuracy, consistency and productivity
• Inexpensive to implement for adhoc projects

4. © ai-one inc. | August 2017
Why We’re Better | Our Core AI Technology
NathanICE™ is our proprietary deep learning technology trained
for language. NathanICE is integrated with other technologies to
power agents that augment the speed and intelligence of our
users. Nathan understands ideas.
“Agents are autonomous software that achieve
goals by learning, adapting and reacting to the
environment.”
We use them to:
• Model ideas in the environment
• Model the user’s ideas/ontology
• Create Contextual Foundation for
additional analytics

5. © ai-one inc. | August 2017
An Agent learns every use of
every word for the idea(s) a user
trains it for… Nathan’s neural
model is a dynamic fingerprint.
Why We’re Better | Ideas, not Keywords
Humans combine words to
form ideas... Nathan
converts them into
fingerprints
Our applications compare
fingerprints to find,
score, classify similar
ideas to the one Nathan
learned.
Documents and
databases are full of
ideas
We teach an Agent
your Idea
For Nathan an
Idea is an Array

6. © ai-one inc. | August 2017
Idea Scoring: Agents Build Contextual Framework
Database/Content
Mgmt System
Enriched Data
scored and
classified by idea
Source Content

7. © ai-one inc. | August 2017
Input | Documents, CSV files or Databases
• BrainDocs works with different types of text and
any language
• Documents
• CSV Files
• Database extracts
• JSON (API only)
• Content is preprocessed and stored with each
paragraph separate and keyed to the source
• Metadata for any form of text is keyed to the
document/paragraph
• No preprocessing, stemming or clean up
required, accepts any plain text UTF 8
• Any language can be imported

8. © ai-one inc. | August 2017
Workflow
Analyst
Review &
Publish
Score &
Enrich Data
Train
Agents
Library
Creation
1. Extract the text
fields/document
paragraphs and
create library via
API or drag/drop
2. Create & train
agents
3. Score docs/data &
generate analyses
4. Export scores via
API or CSV
5. Use BI dashboard
or customize to
analyze and publish
BrainDocs | Creating an Enriched Database/Index

9. © ai-one inc. | August 2017
Extending | Extracting Attributes
The basic implementation provides for the identification of
ideas using similarity score (almost) power and all the
metadata that’s available.
Our workflow is constructed to create an indexed
database of all text at paragraph level and for each
paragraph we can add the following data:
1. Entities i.e. people, location, organizations
2. Tone including special litigious, constraining, uncertain
3. Paragraph Sentiment
4. Word Counts
5. Ngrams
6. Grade level score
7. Sequence
8. Author (interview format)

10. © ai-one inc. | August 2017
Output | Dashboard, Enriched Data, or Custom
Analytics
Figures above show 50 new classifiers trained and tested, 50,000 records
analyzed and Tableau Dashboards designed for analysis and drill down to
source text with full traceability to SQL source.
Tableau Dashboard BrainDocs ICE

11. © ai-one inc. | August 2017
Deploy & Redeploy | Supporting Implementations
No matter how good,
innovation in the lab
doesn’t pay the bills.
We support agile
processes that can
quickly develop and
pilot projects reusing
existing agent
collections
then deploy to the
business units on
standard PC class
VMs in your cloud or
ours

12. © ai-one inc. | August 2017
Production Analytics
Benefits
1. Convert strategic plans, training manuals, roadmaps and process
documents into agent collections to enable search, analysis and
continuous improvement.
2. Export of agent scores enriches metadata or SQL databases thereby
leveraging your BI investment reporting and distribution of analytics,
KPIs and dashboards.
3. Speed & Scalability – scalable for our larger SQL datasets, tested at
200,000 to 6.5 million records per day depending on record size,
using PC class hardware (single VM per DB)
4. Rapid implementation enabling faster response, rapid deployment
and ability to address issues currently not affordable and capture ROI
sooner
In one trial our system was estimated to save over 70% on
software/hardware and over 50% on labor for training and support

13. © ai-one inc. | August 2017
Flexible Intelligence | C-Suite to Operations
Financial Analyst Toolbox (BEA)
Portfolio Analyst Toolbox (NASA)
Knowledge Mgmt Toolbox
(Dreamboard)
Competitive Analyst Toolbox
(InContact)
Marketing Analyst Toolbox (FedEx)
Claims Analysis (SwissRe)
Proposal Analyst Toolbox (DoD)
Quality Analyst Toolbox (Boeing)
CI for Ecosystem Intel
BI for Internal KPIs

14. © ai-one inc. | August 2017
Some of Our Work
(most demos available)

15. © ai-one inc. | August 2017
NASA Roadmaps and Portfolio Analytics –
MSFC Advanced Concepts Office
MSFC Agent Collections
• Wireless Sensors – 45 agents
• Technology Roadmaps – 354 agents
• MSFC Strategic Vectors – 18 agents
• RS-25 Engine DB
Doc/Data Stores Analyzed
• NTRS & JANNAF
• OSAC Project Files
• TechPort
• SLS Engine Procurement DB
• Intellectual Property DBs

16. © ai-one inc. | August 2017
Research Support for ACO –Technology
Forecasting “Topic Mapping”
3000 pages of research from 20+ years were “read” and analyzed to support
development of a roadmap for wireless sensor technology in space.
“Six months
of work in 2
weeks.”
Features:
- All research in a live dashboard
selectable by author, location,
technology, etc.
- Each paragraph in the research
documents is analyzed and
scored

17. © ai-one inc. | August 2017
Features:
- Center Investments in a live
dashboard selectable by
funding, PI, Location, Beat, etc.
- Parking Lot Analysis
- Intuitive Dashboard
Value:
- Saved over 400 hours of
the center investment
manager and engineering
support’s time
- Converted from over 35
PowerPoint slides and
Excel into a live
dashboard
OSAC: Strategic Dimensions for Portfolio
Scoring

18. © ai-one inc. | August 2017
Boulder Equity Analytics - Financial
Reporting Analytics

19. © ai-one inc. | August 2017
Boulder Earnings Call Analytics - Analytics
on the Earnings Call Q&A
Boulder Earnings Call Analytics (BECT):
BECT is complete break down of Earnings Calls. Each call, analyst, company executive, question,
and answer are ranked by theme, topic, sentiment and difficulty to help you spot any changes that
will effect the market and your portfolio. The Earnings call tracker will alert you to monumental
shifts, and urgent information so you can direct your attention accordingly.

20. © ai-one inc. | August 2017
Quality Analytics – Problem: Old Classifiers
BrainDocs™ application was configured with data from the QA Team. then processed
by BrainDocs™ and displayed in the Tableau application for evaluation.
A total of over 1.5 million data points were scored with 30 agents that were trained for
the dataset.
Non-Conforming Record (NCR) codes were defined years ago and now some
codes have 1000’s of entries per month hampering corrective action.
There are over 250,000 NCRs in a SQL database that include narrative text fields
with valuable corrective action information not identified by feature codes.
NCR records must be searched and analyzed by keyword and/or analyzed with an
expensive entity analytics application, both of which are inefficient and not
scalable processes.

21. © ai-one inc. | August 2017
Challenge | Sample of NCR Narrative Text
NARRATIVE TEXT FIELD FROM ONE RECORD
LN137
This NC EPD created after inspection per NC2552587_D_010_001_ELEC for Line check
inspection of wire riding conditions.
Item 1)
X=1830, Y=34, Z=291 [RH location]
WHID:
[RH] 667Z4712-29 WHID
Wire Harnesses:
[RH] 668Z302159-9 WIRE HARNESS
[RH] 668Z303327-5 WIRE HARNESS
[RH] 668Z302187-7 WIRE HARNESS
Ceiling Panel Installation IRM?s & Assy?s:
IR831Z7100-17 IRM S47 CLG-ASSY-FLAT-AFT-PANELS
C517113-111 FLAT AFT RH CEILING ASSY
IS CONDITION: QT Verified Riding condition exist/separation violations between wiring
harnesses and A/C vents above Aft Galley ceiling panels as follow:
Below wire harnesses are riding on the C517113-111 FLAT AFT RH CEILING ASSY
[RH] 668Z302159-9 WIRE HARNESS
[RH] 668Z303327-5 WIRE HARNESS
[RH] 668Z302187-7 WIRE HARNESS
S/B No riding conditions or separation violations per D-X8X3X3X-999 requirements
QT notes: No riding condition exist c/t X=1830, Y=-34, Z=291 [LH wire harnesses to LH
C517113-109 FLAT AFT LH CEILING ASSY]
QT notes: SOI FAD2BILGNC02A is the baseline SOI where the AFT Galley ceiling panels are
installed.
Ref NC2727840 for similar issues.
Find & Classify this Non-
Conforming Condition as
Described by Inspector
This “Should Be” condition as
defined by Manufacturing
Documentation

22. © ai-one inc. | August 2017
Quality Analytics – Closed Corrective Action
Loop
Building classifiers
from Assembly
Manuals and mining
free text buried in
250,000 records
provided the
visibility to the
problems and
accelerated the
feedback to the
plant floor.

23. © ai-one inc. | August 2017
Problem Statement – matching a claim to a contract – requires machine
extraction of attributes that in a combination or in a workflow sequence
can differentiate claims to support the match.
Process:
1. We isolated and indexed the paragraphs to each claim and contract document
2. We filtered out noise by scoring/identifying 18 semantically relevant paragraphs with
BrainDocs agents
3. We then extracted the relevant entities (dates, locations, etc) ranked by count and
indexed to each paragraph/agent
4. We then used these attributes from the claim to match to the Contract/Case
The data from this process was used as an index to support a variety of
claims workflow processes, reports and additional analytics techniques.
Insurance Claim Processing - Challenge

24. © ai-one inc. | August 2017
• Preconditions: Cedant is known; Claim to be processed known and selected
• Result: Most relevant paragraphs display Contract Period and other key info
• Result: Only 3 possible date and location entities are identified
• Action: Select 1st January 2013 as key input to Contract Selection
Extracting Signal from Noise in Docs with
OCR errors

25. © ai-one inc. | August 2017
Dream Analytics – Psycometric Agents for
Startup
Classifiers built to
academic standard
framework for
psychological
analysis of dream
database with over
400,000 narratives.
Project completed in
6 weeks from initial
inquiry to support
fundraising.

26. © ai-one inc. | August 2017
FedEx Survey Analytics – Demo Project for
Survey Verbatims
Built classifiers from
552 verbatims
demonstrating
ability to create
agents, show
coverage of dataset,
drill down to each
verbatim and
classifier analytics

27. © ai-one inc. | August 2017
Tom Marsh, COO
5666 #104 La Jolla Blvd.
La Jolla, CA 92037
Ph: +18585310674
tm@ai-one.com
Twitter @tom_semantic
www.analyst-toolbox.com
www.ai-one.com
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