Cracked Conversations: What to Do When Chatbots Aren’t Enough

Enterprise Search to Compliment Your Chatbot ExperienceBy: Robert Smith, Sales Engineer and John Finneran, Product Marketing

Conversational AI, or chatbot, vendors, are everywhere, deafening customers with the promise of AI-Powered solutions for their customer service needs.  According to Capterra, 158 companies currently offer chatbot software.  In Forrester’s evaluation of the emerging market for conversational AI for customer service for Q2 2019, the analyst firm identified the 14 most significant providers in the category – [24]7.ai, Avaamo, Cognigy, eGain, Indenta Technologies, Interactions, IPsoft, Kore.ai, LogMeIn, Nuance Communications, Omilia, Saleforce and Verint.

This makes understanding what works best to improve customer experience hard.

Chatbots work best guiding users along straightforward, well-defined conversational paths.  If a customer asks new, unpredicted questions the typical chatbot gets confused. More complex questions require complementary solutions.  

Sinequa offers one such complementary solution – Enterprise Search that can work with chatbots to help customers and employees find what they need.

We have spoken with a number of companies ranging from those considering the technology, to building prototypes, to deploying chatbots in customer-facing applications.

Several of the concerns about the value produced by chatbot deployments

  • Slow Conversation speeds
  • Conversation path-sets grow larger and longer
  • Low accuracy because the chatbot was unable to answer and was unable to maintain the chat
  • High development effort with too many expert hours spent conceiving, designing, deploying, and maintaining those conversational paths.

Some Reasons Why?

Chatbots work best when guiding a well-defined type of user through a set of preconceived conversational paths.

The typical chatbot’s tooling provides a graphical interface, and some testing capabilities; conceiving, designing, deploying, and maintaining those conversational paths will be up to you.

  • When you consider how many paths a user might take, multiplied by the number of user types, it can grow to an astonishing amount of work.
  • When chatbots have a lot of this work to do, they tend to slow down compromising, the chat experience
  • Most requests for information are ‘ad-hoc’ and therefore not well-suited for a pre-planned and pre-built conversation flow.

When Do Chatbots Make Sense?

An example is a chatbot at your local bank

  • They have a limited set of offerings for users to choose from
    • E.g. checking, savings, mortgages, lines of credit
  • Those offerings have a limited number of actions
    • Checking deposit, transfer, bill pay, balance inquiry
  • The site is often for reference, not as much for execution
    • To actually open up an account type, you typically have to apply in-person

If you can’t narrow the scope to specific user-types and paths like these, then the outcome of multi-step “chats” is by definition, less predictable, leading to a higher failure rate.

This also makes it difficult for some chatbots to get a PTO (Permit to Operate), because companies have not let applications go into production that couldn’t guarantee outcomes.  This is to avoid “Rogue AI” situations, among other things.

Addressing the Challenge

Enterprise Search, like Sinequa’s, leverages natural language processing (NLP) to get users the most relevant content, without the chatbot’s requirement that the conversational path be designed, built and maintained.

Where chatbot interactions are sometimes helpful, that chatbot can connect to enterprise search; when the chatbot gets a user’s request for information, the chatbot can refine and forward the request to the underlying Sinequa search, then channel the results back to the user’s conversation.

In Short

By using chatbots and a powerful enterprise search platform together for the jobs they were designed for, you can deliver profitable and productive solutions that enhance both customer and employee experiences.

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Data Doesn’t Drive Finance

Your people need information, not data. On average, they waste a day a week searching across silos, systems, and clouds for information. It’s pre-digital-age work. Learn how AI-Powered Search gives your employees the information and intelligence they need.

View the white paper

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Sinequa 2018 Roundup… 2019 Here We Come!

2018Sometimes it helps to look at an entire year to gauge just how far you’ve come in a relatively short period of time. Sinequa experienced some very positive developments in 2018 that are worth highlighting. Our software platform evolved on several fronts to help us accelerate our mission to power the information-driven economy. In parallel, our customers demonstrated what the platform can do, even when stretched in creative and unexpected ways.

On the Technology Front

The Sinequa platform evolved with some very useful and powerful new capabilities in 2018.

Content-related Capabilities

Many of the new capabilities improved on the platform’s ability to integrate with even more enterprise applications and content formats, including:

  • New connectors to support the goal of ubiquitous connectivity across the enterprise. Among these were connectors to Atlassian products to incorporate information from software development projects, including source code files. Also addressed were new versions of popular repositories like SiteCore (a leading web content management platform according to Gartner), along with the likes of Azure storage, AODocs, Beezy, and Teamcenter.
  • New converters to index more formats like OCR on PDF and Images, AutoCAD and Windchill files, Visio, Improvements on PowerPoint, and a dedicated converter for source code files
  • Tighter integration with SalesForce.com
  • In a year full of major data privacy breaches being reported, the Sinequa platform continued to strengthen support of additional levels of encryption like in-flight encryption between all components in a distributed deployment and encryption at indexing time to secure the document cache, which contains elements like HTML preview and thumbnails to better serve customers operating in highly secure environments

Further Automation for the Interpretation of Meaning

The platform’s ability to interpret the meaning of content also evolved in 2018.

  • Query Intent: It is now possible to configure rules to be applied on queries to change the behavior of the underlying search process. This new query intent capability analyzes the query to detect certain words and entities and triggers actions based on the specified rules and classifications. New default entities were also introduced in the platform in 2018 that can be leveraged by the query intent capability and for enrichment during indexing.
  • Enhanced Linguistics: There were some language-specific improvements added to the platform to help automate the interpretation of meaning. These included things like enhanced linguistic processing for compound words in French, improved lexical disambiguation in English, enhanced detection of ordinal numbers for Danish & Swedish.

Improvements in Machine Learning

The year 2018 brought several significant improvements in the Sinequa platform’s ability to leverage machine learning, including:

  • The platform evolved to embed Online Machine Learning, applying machine learning models based on Spark or TensorFlow directly in the indexing pipeline. This represents the first of many new components that can serve machine learning models in real time. Deep learning is also used during indexing to detect new entities or concepts. These are immediately fed into machine learning algorithms, for example in the classification of incoming documents.
  • Packaged with the platform is a new unsupervised Deep Learning application for text analysis that detects the key words, key phrases, and key sentences of a document.
  • The platform now supports the Spark 2.3 implementation.
  • Packaged integration with 3rd party spark distribution providers – e.g. AWS EMR, HortonWorks.
  • Battle testing of supervised classification algorithms – i.e. Sinequa reached a threshold training set size over 10M documents
  • First machine learning customers are now in production
  • Packaging of hierarchical classification
  • Ongoing transition to Software 2.0 paradigm where software is effectively “trained” rather than manually programmed with the packaging of the lifecycle of the model and the model feedback from the search based applications into the Sinequa platform.

Presentation Enhancements for End Users and Admins

Sinequa invested significantly during 2018 to evolve the way the platform presents insights to end users as well as status information and optional settings to administrators. Here are a couple of the most significant developments:

  • A very exciting development from 2018 involved a complete overhaul of the user interface framework to a responsive design based on Angular 7. This will not only ensure optimal flexibility and performance for end users on all kinds of devices, but will open up Sinequa application development to a much wider audience.
  • On the Admin front, components have been reshaped to offer administrators of the platform more functionality and a better user experience for their work behind the scenes.

On the Customer Front

There were a few compelling themes driven by our customer base in 2018, each of which was rewarding in its own way.

Customer satisfaction and retention is a predominant theme for Sinequa. We are extremely pleased by the sheer number of existing satisfied customers that came back to us in 2018 with additional use cases to accelerate their information-driven journey. For instance, business drivers related to governance, risk and compliance with the advent of GDPR and related regulatory demands spurred a lot of activity this past year.

We also had a significant number of customers who experienced that “light bulb moment”, which often occurs when they realize their existing return on Sinequa investment could be significantly amplified by extending the use of the platform with information-driven applications in other parts of the business – e.g. areas like customer service, R&D, supply chain, and other knowledge-intensive arenas.

We even had a few long-time customers take a pause to re-evaluate their vendor choice and, without exception, decided to double-down on their commitment to Sinequa for years to come.

Of course, the disappearance of the Google Search Appliance brought some new customers into the fold, most of them fiercely determined to go beyond their previous use of a dying application and truly become information-driven.

Possibly the single most exciting development for Sinequa in 2018 was the surge in machine learning projects, which contributed significant business value back to the respective organizations, especially in the Financial Services industry. As the underlying technology matures, we see a steady trend for machine learning projects going from research to production stages. Some of the projects from 2018 focused on applying machine learning models to automate the curation of enterprise content and improve relevance. For example, one customer demonstrated how trained machine learning models could be used to make the enrichment of their enterprise corpus more efficient. It turns out that by proactively identifying what content qualifies as “scientific”, both time and money can be saved by preventing non-scientific content from even being considered for scientific enrichment during ingestion. Another customer took a completely different tack, using machine learning to automatically reproduce confidentiality policies to classify large volumes of banking documents with measurably higher quality at a fraction of the cost they would have spent to do it manually or even with a more traditional rules-based approach.

Now it’s on to 2019!

As we turn the corner into 2019, we are grateful for both the accomplishments of our R&D team and for all of our partners and customers, especially those who provide the challenges, creativity and critical feedback necessary for Sinequa to continue providing the leading platform for information-driven applications and solutions.

We wish you all the best and look forward to serving all of you in 2019 and beyond.

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Accelerating GDPR Compliance with an Information-Driven Approach

With GDPR in full effect, Ovum has published a new white paper featuring Sinequa and outlining how cognitive search can be a compliance accelerator.  The full white paper can be found at http://go.sinequa.com/white-paper-gdpr-ovum-2018.html.

ovum-gdprThe white paper, written by Ovum senior analyst Paige Bartley, outlines the challenges of aligning existing business objectives with GDPR compliance and how companies can accelerate GDPR compliance with an information-driven approach.

There is a new precedent with GDPR for enterprise control of data of all kinds, both structured and unstructured. Gaining better control of data is a deceptively simple concept, but requires numerous capabilities, such as the ability to search for data across silos and granularly manage who has access to what. Sophisticated search and analytics capabilities, spanning organizational silos, are key to both facilitating compliance and driving informational value.

According to the white paper: “GDPR compliance requirements should not be thought of as an antithetical force against enterprise initiatives to leverage and analyze data. The more the enterprise can align its compliance obligations with existing business objectives – such as the demand to provide excellent customer service via a 360° understanding of customer desires – the more it can benefit from the perceived ‘burden’ of GDPR compliance. Enterprise-wide search is central to these capabilities and will help the enterprise gain a centralized view of subjects and topics in the increasingly distributed IT ecosystem.”

As an example of this concept, the white paper highlights Sinequa’s platform deployment at Stibbe, a global, full-service law firm with an internationally oriented commercial practice. Stibbe implemented Sinequa’s Cognitive Search & Analytics Platform to provide secure, unified access to millions of documents and legal matters. The result was a holistic view of the firm’s informational landscape, with the ability to quickly retrieve all material related to individual topics, cases or clients.

This Ovum white paper gets at the heart of Sinequa’s approach to GDPR and data analytics. By taking control of data and unleashing it as a strategic tool, organizations can become information-driven in a way that meets compliance requirements while accelerating innovation and creating a key competitive advantage.

The full white paper can be found at http://go.sinequa.com/white-paper-gdpr-ovum-2018.html

 

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How organizations can evolve from data-driven to information-driven

This article was originally published on Information Management.

Over the last several years, data analytics has become a driving force for organizations wanting to make informed decisions about their businesses and their customers.
With further advancements in open source analytic tools, faster storage and database performance and the advent of sensors and IoT, IDC predicts the big data analytics market is on track to become a $200 billion industry by the end of this decade.
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