2. ai-one Core Technology: With Nathan we create intelligent
agents and applications.
“Agents are autonomous software
that achieve goals by learning,
adapting and reacting to the
environment.”
We use them in two ways:
• Model the environment with Nathan
using unstructured data.
• Model the user with Nathan’s ability
to learn the user preferences.
3. Agent learns every use of every
word for the idea(s) a user trains
it for… Nathan’s neural model is a
dynamic fingerprint.
Detect Ideas, not Keywords using Fingerprints
Humans combine words to
form ideas... Nathan converts
them into static fingerprints
Applications compare
fingerprints to find, score,
classify similar ideas to
the one he learned.
Documents, email
chat, social media
Idea AgentFingerprints
4. Idea Detection: Score and Classify Solution
Content Management
System –
Data Warehouse
Enriched
Data scored
and classified
by idea(s)
Source Content
6. The Analyst Toolbox
BrainDocsICE (released) - uses intelligent agents for finding, classifying
and organizing content by concept “idea”
BrainBrowser (Fall 2016) – enables users to analyze a document and
“find something like this” on the web to find & build sources
BrainView (Fall 2016) – visualize learned associations in content
(sources: social media, documents, user comments and review notes)
to explore patterns and sentiment
10. Product and Use Cases
BrainDocs Application
• Cloud or on-premise
• API to automate workflows
• Integrates with Tableau and
other BI tools
• BrainDocs ICE available now
Technology:
• Nathan API for language
• Fast and scalable
• Runs on standard PC class VM
Solutions
• Compliance and Audit Tool
• Qualitative Portfolio Metrix
• Strategic Planning Support
• Classification of free text in operations
SQL databases
• Marketing Survey coding
• Competitive Intelligence
• Research, search and curation
11. Customer Feedback
Aerospace company completed a detailed technical
evaluation of BrainDocs (prototype version). Some
excerpts from the report:
• "In manual reviewing of the results and given the rankings it was
determined that the BrainDocs engine was very accurate with its
analysis."
• "The “brain” itself seems to work great and is able to order documents
by their relevance. "
• "The evaluation of BrainDocs found that the technology is very capable
of its core feature (concept based text interpretation and search)."
• Regarding our color coding of relevance: “It was found that, documents
placed in the green category contained concepts almost identical to what
was described in the agent. Documents placed in the yellow category
contained concepts relevant to the agent, such as the idea of a plane
crash, death and injury, the date, and location. Documents placed in the
gray category may have had similar keywords, such as "plane" and
“airline” but did not contain the same developed concepts as those in
the green and yellow categories.”
12. ai-one | Quick Facts
Mission: Embed intelligence in every computing device.
Business model: Technology licensing, consulting and development
Quick Facts
• Founded 2003 as R&D company to commercialize discovery in neural -
biologically inspired computing
• US parent C-Corporation established 2009, private
• Offices in Berlin, Zurich, La Jolla (San Diego, CA)
• 9 staff on core technology team; 34 staff in joint ventures
• Customers and partners in 14 countries around the world.
13. Thank you.
Tom Marsh, COO
ai-one inc.
5666 #104 La Jolla Blvd.
La Jolla, CA 92037
Ph: +18585310674
tm@ai-one.com
Follow us on Twitter @ai_one
Website www.ai-one.com
www.analyst-toolbox.com