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], Avaamo, Cognigy, eGain, Indenta Technologies, Interactions, IPsoft,, 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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米国ニューヨーク州 – 2019年5月29日 – コグニティブ検索・アナリティクスのリーダーと評価されているSinequa社は、“The Forrester Wave™: Cognitive Search, Q2 20191.”でリーダーに選ばれました。

当レポートで、独立系調査会社であるForrester Researchは次のように述べています。「Sinequaはインテリジェンスを増大させます。 Sinequaが目指しているのは、企業における全従業員が、適切な情報に素早くアクセスできる能力を提供することで、組織を”インフォメーション・ドリブン”へと変革をもたらすことです。具体的には、コグニティブ検索テクノロジーを駆使し、知見や洞察を明らかにし、特定分野におけるエキスパートサーチを実現させます。またSinequaは、最新のオープンソースのMLテクノロジーに、独自のNLUテクノロジーを、絶妙なバランスで組み合わせたエンジンを搭載しています。ライフサイエンスや金融サービス、製造業界などの専門分野にも柔軟に対応できるソリューションです。」

Forrester Waveは、顧客が導入選定する際に役立つ、テクノロジー市場のベンダー・製品を調査した報告書です。 このレポートでは、「Current Offering(現行の製品)」や「Strategy(戦略)」、「Market Presence(市場でのプレゼンス)」の3カテゴリで、合計12社のベンダーが評価されていました。 Sinequaは、Current Offeringで情報に対する高度なハンドリング力が、Strategyでは実行能力・ソリューションロードマップ・カスタマーサービス、またMarket Presenceでは市場の認知度と、各カテゴリそれぞれの評価基準において、最高のスコアを獲得しました。

今回の評価を受け、Sinequa社の代表取締役社長兼最高経営責任者(CEO)のAlexandre Bilger氏は、次のように述べています。「我々は、Forresterのレポートでリーダーとして認められたことを光栄に思います。膨大な量のエンタープライズデータを取り込んで分析することで、顧客は状況に即した実用的な情報をタイムリーに引出し、洞察の獲得や意思決定、生産性の向上に繋がります。我々は、優れたプラットフォームと、金融サービスや製造、製薬業界の組織をサポートする能力に自信を持っています。」

さらにレポートでは、「Sinequaの強みはデータコネクターやingestion intelligence(データソースに対するインデックス作成)、intent intelligence(検索に対する的確な回答)、チューニングツールにあります。この高度なコグニティブ検索アプリケーションを導入したお客様は、Sinequaの幅広くて深みのあるingestion intelligenceの性能を理解できることでしょう。」とも述べています。そして「Sinequaの強力なロードマップには、最新のオープンソースAIテクノロジー活用も構想に入っています。」と締めくくっています。

Sinequaプラットフォームは、最近Angular 7を基盤にした、レスポンシブ・ユーザーインターフェース設計フレームワークを通じて、エンドユーザーへ洞察を提示する能力が強化されました。同プラットフォームには、SparkまたはTensorFlowに基づく機械学習モデルが搭載されたため、現在はインデックス作成パイプラインが直接適用できるようになりました。またクエリーや言語に対して、意図・意味の解釈をさらに自動化するための、大幅な新機能が追加されています。


レポート「Forrester Wave:Cognitive Search, Q2 2019」は、以下からダウンロードできます。

1 Forrester Research, Inc., “The Forrester Wave™: Cognitive Search, Q2 2019” by Mike Gualtieri, with Srividya Sridharan and Elizabeth Hoberman.




詳細は、 をご覧ください。

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The Heat in the Trend Point: May 13 to May 17

Enterprise Data Warehousing (EDW) has been an important technology for many years. The basis of the need for data storage and management shows little sign of slowing based on several recent articles that we have seen pop up in The Trend Point over the past week.

One article discusses how Hadoop will impact EDW technologies. “The Future of EDW’s with Hadoop on the Horizon” shares the following:

HBase/Hive are all still very IT focused. They need people with lot of expertise in writing MapReduce programs in Java, Pig, and other specialized languages. Business users who actually need the data are not in a position to run ad-hoc queries and analytics easily without involving IT…Most EDW come with pre-built adaptors for various ERP systems and databases. Companies have built complex ETL functions, data marts, analytics and reports on top of these warehouses. It will be extremely expensive, time-consuming and risky to recode that into a new Hadoop environment…

Another article we saw points to this major emphasis on an efficient and intuitive user experience for professionals in any given organization that are not data experts. “Kerns Interview Shines Light on Future of EDW” sources information found on IBM Big Data Hub:

…what should be happening more, is a trend around users, especially the people who don’t know much about data and don’t exactly need to know much about the data but need answers…Any user who doesn’t know anything about the data side should be able to go into their enterprise interface and type ‘regional sales’ and then have a chart pull up. We should not have to ask people to construct queries for everything.

During the Enterprise Data World event that happened about a month ago, Unified Information Access was a topic that Cambridge Semantics presented on. In “Making Sense of Unstructured Data” we saw the following summary:

Cambridge Semantics will present the case for Unified Information Access at this year’s Enterprise Data World event. Addressing hot topics such as data integration, the challenges of effective MDM and SOA, and enterprise information management, Cambridge Semantics founding team members will highlight how Unified Information Access, powered by semantics, can serve as a critical differentiator for enterprise data management initiatives.

What we are seeing is a push for next generation EDW solutions – or rather the next generation of solutions encompassing EDW,Search and semantic tehcnologies. Many companies are interested in a solution that works out-of-the-box and is not IT-dependent, but that still offers plenty of data accessibility and search capabilities. Unified Information Access (UIA) is leading the way in this sector. With the functionality of both traditional EDW mixed with the ability to perform semantic analysis and extraction on unstructured data, this type of technology goes above and beyond traditional enterprise search and data warehousing.

Search and semantic technologies can contribute in two different ways (amongst others): Augment (meta) data that can then be treated by traditional DW analystics. Extend analysis to unstructured data, for example from public information Websites, in order to deliver the reasons behind changes in figures and performance indicators in BI dashboards.

Visualization and analytics become just a short step for analysts to take when they access data through a UIA platform.

Jane Smith, May 22, 2012

Sponsored by, developer of Beyond Search

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