Archive for the ‘Use Cases/Case Studies’ Category

ISC Consulting Powers Pytheas AI with BrainDocs

Friday, June 24th, 2016

We are pleased to publish ISC’s submission under the DIUx program.  The new  “Defense Innovation Unit Experimental (DIUx) serves as a bridge between those in the U.S. military executing on some of our nation’s toughest security challenges and companies operating at the cutting edge of technology.” Powered by ai-one’s Nathan ICE artificial intelligence core for language, ISC’s Pytheas AI will provide ISC with the technology to assist researchers and help our governments keep us safe.  Some proprietary sections have been deleted in the version below.

ISC White Paper for DIUx Technology Area of Interest: Knowledge Management

By Jeremy Toor, ISC Consulting Group

Executive Summary

ISC/ai-one develops a prototype using Pytheas artificial intelligence (Pytheas AI) which will provide automated intelligent information management, or knowledge management (KM) of multiple data sources.  Pytheas AI fingerprints the flow of data from almost any source including chat, email, message traffic, and other data.   This AI core then supports the user in a publish/subscribe architecture, with building knowledge from the fingerprinted data through queries and intuitive alerts that understand the difference and importance of contextual situations.

The abundant quantity of data that is available to users, analysts, and commanders today can make it challenging to build a concise and accurate picture from which dynamic assessments can be made. Both Command and Control (C2) and intelligence systems are largely data-centric. Users that are required to make strategic and tactical decisions will benefit from a task-centric user experience that is able to manage information as it is created and presented, and distil many sources of data into a manageable data flow.  This user experience, facilitated by Pytheas AI will deliver an KM Engine that can accelerate the decision making process.

Through Pytheas AI the user will be presented with data that has gone through automated processes to be categorized, tagged, and ranked according to its value in the current context of operations.  Pytheas AI will give the user flexibility to tailor their focus area and pull information from a wide breadth of sources as they build situational awareness and confidence to take action.


ISC/ai-one proposes a three phase project.  Phase 1 will include one-week for initial installation, configuration and user training. Phase 2 will include a five-month period to support data ingestion, intelligent agent training and dashboard customization. Phase 3 will include a three-week evaluation and close out.


Pytheas AI is built with an artificial intelligence core to collect, organize and analyze language to uncover key links and patterns within large volumes of unstructured text.  The application empowers analysts to find the relationships necessary to discover, manage, process and exploit data.  Key features and attributes of Pytheas include:

  • Discovery of Concepts through the use of Intelligent Agents
  • Agent collections can be built from existing plans, roadmaps and strategy documents
  • DoD analysts can use common KM collections or build and share concept agents
  • Agents provide classification for query and tagging of documents
  • Application core is language independent
  • Fast and lightweight running on PC class machines or VMs

Pytheas AI is built upon ai-one’s BrainDocs software application (with NathanICE API core) which is a commercially ready and viable technology that has been applied to several use-cases similar to the requirements in the technology area of interest, knowledge management, that DIUx is seeking.  Our prototype for KM is ready for demonstration using sample data.

Pytheas uses the ability of ai-one’s proprietary NathanICE API to discern patterns in the words and associations that are central to the meaning of all or a portion of a text document (in the same way as the brain).  Nathan extracts these keywords and associations, filtering out the noise to create a proprietary fingerprint array of the concept that can be used in many ways.

Pytheas uses the fingerprint of a trained concept to find (rank) similar concepts within a corpus of information (documents, websites, databases) and returns paragraph-level results sorted by “similarity”. These results support a variety of workflows in enterprise compliance, classification, search and knowledge management.  Agent similarity scores are exported to Excel or your database to support analytics and BI tools. This can be done by the analyst for small ad hoc studies.  Agents can also be used to code years of legacy data without additional training.

Users employ agents in Pytheas AI to organize text based on contextual ideas and metadata dimensions, improving accuracy, consistency and saving substantial amounts of time in this tedious process.

The Basic Elements of Pytheas

Documents – Pytheas is capable of analyzing any form of unstructured text. In fact, our technology works best with semantically-rich content written in your business vernacular without external taxonomies or ontologies.  Working at the paragraph level it has been used on everything from text messages to database fields to long documents always with full traceability to source.

Conceptual Fingerprints – This is the “secret sauce” of our discovery capabilities. Pytheas uses the Nathan API keywords and associations to create semantic “fingerprints” of concepts. Because one concept can be written in multiple ways, our algorithm does not rely on word counts, natural language processing (NLP) or latent semantic analysis (LSA) when identifying and fingerprinting concepts.

Intelligent Agents – Pytheas agents examine and compare the conceptual fingerprints to find traces of concepts buried within your data. Our premise is that analyst is the expert and needs to be able to train their own army of software agents to “read” documents and deliver the relevant paragraph. Used as a collection, the scores from a collection of agents set the context for a user’s query.

Paragraph Level Concept Discovery – Pytheas provides the ability to categorize and display concept results at the paragraph-level. Users do not need to hunt through documents trying to find a concept that a search engine claims to be present. Our system will return the paragraph(s) that closely match a concept, sort and group the concepts by similarity to one another. Paragraphs can be evaluated and traced back to their source document for reporting and distribution.

Topic Mapper Entity and Sentiment

Figure 1. Topic Mapper Entity and Sentiment in SEC Filings

Ease of Integration – Pytheas application can be used with conventional desktop tools for ad hoc projects.  For workflow automation a Restful API provides developers an easy method to process documents and export results to SQL or other DBs for reporting and visualizations.

Optional Entity Extraction and Sentiment (Figure 1 above) – Complementing paragraph level concept detection is the ability to extract entities and/or score for sentiment so this information can be added to visualizations and follow on workflows.  Clients can use their own technology for this purpose or add custom analytics to further refine the insight for social network analysis, tagging existing file headers or streamlining the flow of information into the analyst.

Defense Utility

The immediate benefit to DoD is increased productivity, consistent analysis and more effective information management.  The long-term benefit is an ability to perform quicker, more informed decisions.

Operational users of this prototype include any person that has to search through data.  This includes anyone using SharePoint and other common organizational databases.  Analysts who must sift through massive amounts of data in order to discover relevant information will save countless hours through the employment of our prototype.   Through the employment of a similar use case at NASA, our customer was able to complete a typical six-week project in one-week!

Company and Relevant Use Case

Lead by ISC, personnel from ISC Consulting and ai-one inc. will execute the project.

ISC Consulting Group is a Service Disabled Veteran-Owned Small Business (SDVOSB). We are headquartered in Sierra Vista, Arizona, with operational offices at Ft. Huachuca, AZ; Orlando, FL; Ft. Gordon, GA; and Northern Virginia. ISC provides a full-spectrum of services, products & solutions supporting the DOD Intelligence Community and key commercial clients with advanced capabilities in Instructional Solutions, Cyber Security, Command and Control planning and operations, Intelligence operations, Information Technology, and Data Analytics through Artificial Intelligence products and services.

ai-one inc. is the developer of a proprietary core technology that emulates the complex pattern recognition functions of the human brain that can detect the key features and contextual meaning of text, time-series and visual data.  This technology will enable DIUx to score and analyze any piece of textual content and discover information by concept, bringing the dimension of AI understanding to knowledge management. This technology automatically generates a lightweight ontology that easily detects all relationships among data elements; solving the immediate problems facing the DIUx knowledge management based process and schedule.

Existing Customers

ISC has served several clients with Pytheas technology, including NASA Marshall Space Flight Center (MSFC).   Currently, Pytheas is being used by MSFC’s Advanced Concepts Office (ACO) under a Cooperative Agreement to assist in technology roadmap development and separately by the Office of Strategic Analysis and Communication (OSAC) to manage and report on their portfolio of project investments (similar to SBIR grants).   For example, the roadmap project is described below:

Overview of the NASA Advanced Concepts TAPP Pilot Project

The Advance Concepts Office (ACO) at MSFC, NASA is developing and refining methods and processes for performing Information Based Decisions for Strategic Technology Investments.  This system is currently referred to as TAPP, Technology Alignment & Prioritization Process.   This process supports the evaluation of the technologies for investment by NASA and MSFC to insure alignment with NASA mission plans, technology area priorities and strategic knowledge gaps.

TAPP creates an interactive system for exploring the almost mind boggling complexity of planning for multiple missions using over 400 technologies (many still in basic research) and hundreds of interrelated elements/sub-elements over 30-year planning horizons.

Pytheas provides NASA the capability to have data mining agents parse and score unstructured content against the nearly 400 technologies identified in the 15 Technology Roadmaps.  This ability to score proposals with agents allows ACO to perform statistical analysis within the Information Based Decision framework for Strategic Investments.

The immediate benefit to ACO is increased productivity and consistent analysis. The long-term benefit is an ability to perform quicker, more informed technology assessments, feasibility analysis, and concept studies that align with NASA evolving strategic goals and multiple mission objectives.


Given a six-month prototype build period, ISC/ai-one will demonstrate to DIUx that ISC/ai-one’s Pytheas AI application will enable the organization to save critical time and human capital in the implementation and operation of knowledge management systems.  Pytheas will empower the IC to rapidly and effectively sort through vast volumes of text data in order to gain knowledge and position decision makers with the right information to achieve stated organizational analytical research outcomes.

Personal AI Helps Convert Social CRM for Recruiting

Thursday, June 26th, 2014

Given the need for more effective content marketing and better quality lead generation, why aren’t the tools better?  Certainly there are lots of applications, SaaS products and services available for all parts of the marketing and sales process.   With BrainBrowser we provide a tool that can understand the content from marketing and match it to bloggers, LinkedIn connections, Twitter followers and find candidates in places you would never look.

Since about one-third of the 7,500+ queries by our testers were using BrainBrowser to search for people, a key objective is to add features to manage the results and integrate them into your workflow.  If you find someone relevant to your work or a potential recruit, you should be able to connect with them right from the list, follow them on Twitter or share lists of candidates with collaborators.

BrainBrowser with Nimble Popup

As a recruiting professional your task is to find the candidates and conversations on the web where conversions will be maximized and get there first.  BrainBrowser does this for you, creating a list of people, companies and sites that match the content of your position and company description.

As a sales professional, you want to use content, either from your marketing department or content you find and create on your own, to engage your network and to identify the people that are talking about and responsible for buying/influencing a purchase.

In our research (using BrainBrowser) we discovered Nimble and a new category of Social CRM vendors with applications driving social selling (check out Gerry Moran’s post for background on content and social selling).  We were immediately hooked and started using Nimble as our company CRM but quickly found it worked well for managing lists of candidates.

Nimble, a new social CRM application, has made integration easy and I’m recommending it to everyone.  All you need to do is sign up for the trial (its only $15 per month if you like it) and install the plug in in your Chrome browser.  You’ll then be able to highlight the name of the person on the list in BrainBrowser, right click, select the Nimble Search and a popup will display the person’s social media pages in LinkedIn, Twitter, Google+ etc.  Click Save and you’ve added them to your Nimble Contacts where you can then view their social media messages, profile and decide whether to connect or follow.   Tag them and you’ve creating a recruiting hot list you can track in Nimble.

Here’s a video clip I tweeted to CEO Jon Ferrara demonstrating how/why we love it.  This was in response to his video clip to Larry Nipon following up on my referral.

Let me know how you like it.  They do a great job but if you have any questions on the difference between CRM and Social CRM, and how we’re using it for recruiting.  Be sure to add @ai_one or @tom_semantic if you tweet about this and sign up to request a login for BrainBrowser.

As of today, there are only 22 slots left for FREE registrations under the Alpha test program.  Participation gets you a year free on the platform.  Email or tweet @tom_semantic to sign up.

Self-Aware, Self-Defending Adaptive Network Appliance Software (SASDANAS)

Thursday, January 12th, 2012

On November 29, 2011, our consulting partner Ariston Consulting submitted a proposal to the US Air Force to develop a new form of defense for cyber assets using machine learning for cyber awareness and resilience.  This proposal was partially developed by ai-one in an effort to bring the most advanced machine learning technologies to the Air Force at the lowest possible cost. 

Our proposal (below) was in response to BAA Number  AFRL-PK-11-0001 as a Rapid Innovation Funding program. Our proposal met all four operational criteria yet was rejected on January 6, 2012 due to our lack of prior history with the US Air Force. The AF simply preferred to do business with a company that they knew rather than a new vendor.

However, on December 20, 2011 the Air Force released a request to build a system very similar to what we proposed to build below under the contract BAA-RIK-12-03. Both projects were issued by the Department of the Air Force, Air Force Materiel Command, AFRL – Rome Research Site, AFRL/Information Directorate, 26 Electronic Parkway, Rome, NY, 13441-4514.

We are not accusing the Air Force of any wrong doing nor is there any evidence that they copied and pasted our ideas into another BAA. Quite to the contrary, the Air Force is a big place and we are not the only people thinking of ways for networks to defend themselves using autonomic machine learning technologies. However, we feel that our technology can be deployed at very minimal cost compared to the budget provided in the BAA issued a month after we proposed a smaller, more rapid solution.

We think it is valuable to share this information with the public for several reasons:

  1. To publish our findings in a public forum to prevent any other party from obtaining a patent for cyber security applications or network defense applications using the approach described herein.
  2. To encourage major defense contractors to contact Ariston Consulting and to use ai-one’s biologically inspired intelligence in cyber security applications.
  3. To encourage the Air Force to consider reducing the budget allocated for BAA-RIK-12-03 by 90%. There is simply no business reason to spend 10-times what we proposed.

Title:     SASDANAS: A network that protects itself from cyber attacks.

BAA Number:  AFRL-PK-11-0001

Firm:         Ariston Consulting LLC

P.O. Box 1721

Sierra Vista, AZ 85636

Phone: (520) 378-6112


Duration of Effort:         24 months

Estimated Cost of Effort:          $2,800,000

Self Certification of Applicant:   Service-Disabled Veteran-Owned Small Business (SDVOSB)

Air Force Need Area:  02. Cyberspace Superiority and Mission Assurance

Air Force Primary User:  24th Air Force Wing, San Antonio, TX

Programs/Platforms for Proposed Technology:

DoD-Reimbursed IR&D:  NO

Proposed Approach Relate to Prior DoD-Funded SBIR or STTR:  NO

Foreign Participants for Effort:  NO

Funded by DoD or Another Federal Agency: NO

Percentage of Effort

by Offerer:                    60%

by Others:                    40%

 Preferred Funding Instrument:    Contract

Technical POC:     Jonathan Woodruff, CEO, Ariston Consulting

Phone: 520.378.6112



Business POC:        Steve Mecham, COO, Ariston Consulting

Phone: 520.378.6112



Project Description/Objective:  SASDANAS: A network that protects itself from cyber attacks.

Ariston Consulting LLC proposes to develop a Self-Aware, Self-Defending Adaptive Network Appliance Software (SASDANAS) system that acts as an intelligent agent to monitor network activity, content and behavior to augment the capacity of human analysts to identify and counteract all forms of cyber threats.

Ariston Consulting is a Service-Disabled Veteran-Owned Small Business (SDVOSB) based in Sierra Vista, AZ, provides advanced technology testing and engineering solutions. Expertise and experience in providing non-personal scientific and engineering services to test Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) systems in support of the US Air Force (USAF), US Army, and DISA.

SASDANAS is an intelligent agent that learns and understands the threat level posed by every byte-pattern across a network. The software system uses a new form of machine learning to monitor every detail of a network to identify and isolate cyber security threats – including malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. It enables the Air Force to make all cyber assets self-aware, self-protecting and adaptive to any external or internal threat. The approach eliminates the opportunity for zero-day attacks because it detects all anomalous packet behavior and content. Furthermore, SASDANAS provides the Air Force with a first-mover advantage as the system learns through use and thus becomes more intelligent over time.

SASDANAS is a 64-bit multithread, massively parallel application that is deployable through a REpresentational state transfer (REST) architecture. Each instance of SASDANAS may be deployed in series and/or in parallel. This architecture provides the USAF the greatest degree of flexibility when deploying into field operations. This approach enables the USAF to use SANDANAS in either: a) moving-windows approach to read every packet as it flows across the network; or, b) identifying threats by capturing an image of the topology of network at byte- or packet-level of detail to understand the behavior and content of network. Each instance of SASDANAS will have the capacity to understand up to 18 exabytes of data at a time. Speed of SASDANAS is dependent on available memory and processing capacity. When deployed in parallel, SASDANAS has the theoretical capacity to monitor the activity of the entire Internet.

Unlike current approaches to cyber security, SASDANA uses a new technology called a HoloSemantic DataSpace (HSDS) to detect, classify and store every byte pattern. The HSDS is thus able to recognize every packet’s behavior and content to determine if the byte-pattern conforms to expectations or is anomalous and therefore subject to further scrutiny to determine if it is a threat. The HSDS is an adaptive, associative network that detects the relationship of every byte that is fed into the system. Thus, the HSDS is capable of identifying both known threat patterns while concurrently identifying and isolating anomalous patterns that may signify a zero-day attack or non-compliant use of the network (e.g., sabotage).

The HSDS is a newly discovered form of neuronal network that mimics the neurophysiology of the neocortex. It is commercially trademarked as a “biologically inspired intelligence” and operates similar to a human brain. It learns autonomically by detecting byte-patterns at the moment of stimulation. The HSDS stores each unique byte pattern only once regardless of how many times it encounters that specific pattern. It registers and adjusts the semiotic value for each byte pattern each time it is stimulated – adjusting the size of the net automatically. It determines the semiotic value for each byte pattern with the following dimensions, each of which may have many values: time of stimulation, place of stimulation, syntax of surrounding byte patterns, and packet payload and addressing. Thus, the HSDS creates an n-dimensional representation of the semiotic value of every byte-pattern; thereby capturing every detail within the complexity of data.

The HSDS technology is commercially available from ai-one inc. since June 2011. It is currently in use at Orange (France Telecom) and more than 40 additional installation sites around the world. The commercial version of the HSDS is offered in three versions: Topic-Mapper to analyze human languages, graphalizer to analyze sensor data, and Ultra-Match to analyze visual images. The technology has been used by The Federal Criminal Police Office of Germany (Bundeskriminalamt or BKA) to build a crime scene analysis tool for the Swiss Federal Department of Justice and Police (Eidgenössische Justiz- und Polizeidepartement or EJPD). The commercial versions of HSDS have a technology readiness level (TRL) of 9. The TRL for the proposed customization of current HSDS COTS technology is 7. Ariston Consulting will license ai-one’s technology to create a new software application to meet the unique needs of protecting USAF cyber assets.  The HSDS differs from current forms of neural networks, machine learning and artificial intelligence technologies in the following ways:

Transparency – HSDS generates a lightweight ontology (LWO) that adjusts dynamically with each passing byte (and/or packet). The LWO describes the relationship of every byte within the network. The LWO is machine generated, machine curated and accessible by humans.

Benefit: Humans can see how SASDANAS interprets the value and threat level of every packet.


Autonomic:  HSDS learns without any human intervention. It does not require any prior conditions or neighborhood functions. Rather, it automatically generates computational and data cells within the network as needed immediately upon network stimulation – just like the human brain.

Benefit: SASDANAS is objective and subject to cognitive biases that may distort threat detection.


Speed, Accuracy, Sensitivity: HSDS captures every detail regardless of the degree of complexity. In incremental learning situations, the proposed 64-bit architecture is expected to be at least 105 faster than latent Dirichlet allocation (LDA) or vectoring approaches such as COStf-idf.

Benefit: SASDANAS is very fast and accurate – even by neural net standards.


Trainability: The system can be trained and untrained by humans. It is aware of which patterns are learned through training and which patterns have been taught from humans.

Benefit: SASDANAS eliminates the risk of overtraining. It is flexible.


Compatible with Existing Technologies: The system is deployable using industry standard approaches as a cloud-based application.

Benefit: SASDANAS reduces the cost of maintaining and protecting cyber assets while extending their functionality.

Ariston Consulting proposes to build SASDANAS as a software proof-of-concept for further development as a hardware solution called Self-Aware, Self-Defending Adaptive Network Appliance Chipsets (SASDANACS). Based on preliminary tests of the core commercial technology, Ariston estimates that the hardware version will operate at least 10,000 times faster than the software version. This speed, combined with an estimated capacity of 18 exabytes per instance, enables the hardware version to monitor and protect cyber assets at wire-speed and at Internet scale.

SASDANA is deployable at any layer with network (from switch layers 1 through 7) and is compatible with known specifications for Wireless Network After Next (WNAN) as described in unclassified DARPA and AFRL reports. Its architecture provides the AF with a wide range of deployment options.


Ariston Consulting LLC will adapt commercial-off-the-shelf (COTS) HSDS software from ai-one inc. to build SASDANA. Ariston Consulting has secured rights to license and modify technologies owned by ai-one inc.for the purpose of creating custom applications for agencies of the United States Government, including the Department of Defense.

Critical Need/JUPM Challenge Area Addressed:

02. Cyberspace Superiority and Mission Assurance

Benefits to the Warfighter:

Cyber security – Networks monitor and defend themselves.

Force leverage – SASDANA drastically increases the analytical capacity of human analysis.

Morale – SASDANA makes network security analysis and counter measures more interesting by eliminating mundane tasks.

Funding/Cost:              $2,800,000.

Program Plan:

a)     Period of Performance:  Not more than 24 months from commencement of contract for Phase 1.

i)      Ariston Consulting shall report progress on technical design, engineering and prototype development every 30 days throughout the project.

b)    Schedule – Total of 24 months:

i)      Detailed technical specification including use and test cases:  3 months

ii)     Technical development of software using Agile methodology: 12 months

iii)    Software testing: 3 months

iv)    Software revisions: 3 months

v)     Preparation and submission of final technical report: 3 months

c)     Deliverables:

i)      Scientific and Technical Reports every three (3) months, Final Report at conclusion

ii)     Funds and Man-hour Expenditure Report every three (3) months, Final Report at conclusion

iii)    Contract Status Report (CFSR)

iv)    Status Report

v)     Presentation Materials

vi)    Software: As proposed, on CD-ROM

d)    Metrics/Measure of Success:

i)      Ability to detect known malware compared to industry standard technology (e.g., McAfee).

ii)     Ability to detect unknown malware threat imposed by AFRL Red Team.

iii)    Ability to detect anomalous behavior of a packet within a network.

e)     Facilities/Equipment:

i)      All development will be completed at an Ariston consulting controlled Top Secret (TS) facility.

f)     Risk:

i)      Technical risk of SASDANAS is minimal as the technology currently is available for commercial use by ai-one inc. Ariston Consulting will mitigate risk by employing ai-one engineers to train Ariston staff, transfer knowledge and provide guidance based on commercial experience.

g)    Proposed Transition Plan:

i)      Technical data: Unlimited rights granted to USAF.

ii)     Non-commercial software (NCS): Unlimited rights granted for each additional instance of SASDANAS software shall be sold to the US Government.

iii)    NCS Documentation: Unlimited rights granted to USAF.

iv)    Commercial computer software rights: Not applicable. SASDANAS will be a modified version of ai-one technology that will not be commercially available.

v)     There are no restrictions on the use of a licensed instance of SASDANAS for use within the United States Air Force. The Air Force may deploy SASDANAS at its own discretion, in any manner it so chooses.

vi)    SASDANA’s application program interface (API) may be accessed by any entity authorized by the USAF.

h)     Other Key Participants:

i)      Commercial supplier of HSDS technology, software development kit and technical training:

ai-one inc. (a Delaware C-corporation)

Atten: Olin Hyde, Vice President

5711 La Jolla Blvd., La Jolla, CA 92037

Phone: 1-858-381-5897/Email:

Use Case: Passenger Name List (PNL) for Secure Flight Program

Wednesday, December 14th, 2011

Case Study Summary:

The Passenger Name List application was developed by ai-one for one of the largest airline ground handling services company in the world.

The PNL Matcher is being used by airlines at the JFK, FRA, and ZHR airports to efficiently and accurately match a PNL (Passenger Name List) with the different suspect lists (no-fly list) supplied by official sources such as the U.S. Department of Homeland Security (DHS) Secure Flight Program.

This application uses the core ai-one™ technology in a limited but very effective way.  The challenge in this area is the need to comply quickly with new U.S. DHS requirements to effectively screen passengers before boarding a flight.

The challenge for such an application is, when a ticketing agent creates a ticket for a passenger from a country that does not use the western alphabetic character set and phonetically spells the name.  Since the spelling could be very different from different agents, the software has to be intelligent enough to find and match suspect passengers to the DHS list.  Additionally phonetic use of characters is varies from country to country but must meet U.S. requirements for quality.

PNL Matcher for Swissport's Secure Flight Program

 PNL Benefits:

  • Fast, very accurate response to a ticket agent
  • Phonetic spelled names can be matched with compliance to all regulations
  • Easy to add new lists
  • Works in any language or character set (language agnostic)


Visualize all associations within written text


Custom implementation of Topic-Mapper API


Solutions installed runs since its implementation in 2007 productive

Swissport International

Rico Barandun, Product Manager

Application areas:

  • Pattern Matching
  • Security

Target Industries

  • Transportation
  • Homeland security
  • Law enforcement

ai-one Use Case: Enhance OCR of Credit Card Receipts using Machine Learning API

Wednesday, December 14th, 2011

OCR Correction using ai-one machine learning API

Use Case Summary:

The BON Matcher is an ai-one implementation enabling a leading swiss retail store to analyze all scanned credit card receipts.

After the scan process, all credit card receipts are analyzed and matched against patterns using a-one’s API.

Our solution corrects the errors of the optical character recognition (OCR) system when it fails to recognize 100% of the elements.

This was an early validation of our technology. It  affirmed ai-one’s superiority over alternative artificial intelligence-based solutions as a much faster, better quality, and less expensive solution. The retail chain saved substantial operating costs by automating this process and was able to reduce its workforce by 15 people.

The project was finished after 3 months of development time and is still being used for more than 80 stores.

The feature of the technology used in this application is commonly used in document archiving systems where users need to search for documents that have been scanned with many character errors.


  • Improved OCR performance from 80% to 98% in less than a week after implementation.
  • Enhancing OCR recognition in a separate, low-cost post processing process
  • Faster data availability
  • Additional fraud detection possibilities


Customize software development


Solution in place. Successful since 2006 launch.


Swiss Data Safe AG

Application areas:

  • OCR recognition
  • Numerical series matching
  • Data management / Archiving

Target Industries:

  • Information management
  • Retail


OCR Correction Workflow Using Machine Learning API