Sinequa Featured in IDC Technology Spotlight Dedicated to Manufacturing Organizations


Constrained by manual processes and lost productivity, AI-powered search platforms are critical for manufacturers to streamline production and decision support.

We are proud to announce that we’ve been featured in a new IDC Technology Spotlight report: Digital Transformation of Manufacturing Through Intelligent Search. The report, written by Jeffrey Hojlo, program director, Product Innovation Strategies and Hayley Sutherland, senior research analyst, AI Software Platforms highlights the importance of how intelligent search — the combination of artificial intelligence, machine learning, big data analytics, and enterprise search — can optimize a manufacturer’s Industry 4.0 strategy.

According to the report and a recent IDC study, knowledge workers in the manufacturing industry remain constrained by manual processes. On average, knowledge workers waste up to 18 hours per week searching for, combining, and reformatting information. This time usage creates the general impression of inefficiency and brands the overall experience of information access as discouraging. Further, the manufacturing company’s yield might be injured, and important business conclusions could be drawn using inadequate information.

“Manufacturers should look for tools like Sinequa’s that are contextually aware and semantically savvy, able to analyze and categorize info from across the organization to deliver the most relevant insights”

With the demand for AI technologies that enable intelligent analytics increasing every year, IDC estimates that enterprise data from things like IoT devices, smart devices, email, metadata and other sources created annually is expected to grow exponentially.

“Sinequa’s platform allows manufacturing companies to streamline procedures and create operational efficiencies that alleviate the major challenges highlighted in this report,” said Scott Parker, director of product marketing at Sinequa. “By offering a broad AI-powered platform including search, content analytics, semantic understanding and auto categorization technologies, Sinequa provides relevant details to users when they need it, while supporting a range of machine learning algorithms and capabilities that improve findability and relevance.”

Harnessing the power of intelligent search, “manufacturing organizations see faster time to decision, better business decisions, and increased employee productivity as the most common benefits,” said Sutherland. “To achieve this, manufacturers should look for tools like Sinequa’s that are contextually aware and semantically savvy, able to analyze and categorize info from across the organization to deliver the most relevant insights.”

Download a complimentary copy of the IDC Technology Spotlight report: Digital Transformation of Manufacturing Through Intelligent Search.

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Becoming Information-Driven Begins with Pragmatic AI

Written by guest blogger, David Schubmehl, IDC Research Director, Cognitive/Artificial

Intelligence Systems.  Sponsored by Sinequa.

Over the last several years, I’ve spoken to many organizations that have all asked the same question: How can we most effectively make use of all of the research, documents, email, customer records and other information that our employees have collected over the years, especially those that are now retiring? In the past, organizations had corporate libraries and corporate librarians whose job it was to help collect, organize, and disseminate information to employees and staff when and where they needed it. That department and positions are long gone from most organizations today. Why have they gone? The rate of data and documents (including research papers, contracts, and even emails) has exploded, making this task impossible. But let’s be honest: even before today’s information explosion, no classification system could ever keep up with the fast pace of change in the economy. No one could have foreseen today’s most important questions, in content categories that did not exist until today. And with the baby boomers retiring at an ever-increasing rate, an urgent question must be asked: How do organizations get the most value from the vast amounts of information and knowledge that they’ve accumulated over decades?

IDC has identified the characteristics of organizations that are able to extract more value out of the information and the data available to them. Leader organizations make use of information access and analysis technologies to facilitate information access, retrieval, location, discovery, and sharing among their employees and other stakeholders. These insight leaders are characterized by:

  • Strategic use of information extracted from both content and data assets
  • Efficient access to unified and efficient access to information
  • Effective query capabilities (including dashboards)
  • Effective sharing and reuse of information among employees and other stakeholders
  • Access to subject matter experts and to the accumulated expertise of the organization
  • Effective leverage of relationships between information from different content and data sources

So how can artificial intelligence (AI) and machine learning affect information access and retrieval? The types of questions that are best answered by AI-enabled information access and retrieval tools are those that require input from many different data sources and often aren’t simple yes/no answers. In many cases, these types of questions rely on semantic reasoning where AI makes connections across an aggregated corpus of data and uses reasoning strategies to surface insights about entities and relationships. This is often done by building a broad-based searchable information index covering structured, unstructured, and semi-structured data across a range of topics (commonly called a knowledge base) and then using a knowledge graph that supports the AI based reasoning.

AI-enabled search systems facilitate the discovery, use, and informed collaboration during analysis and decision making. These technologies use information curation, machine learning, information retrieval, knowledge graphs, relevancy training, anomaly detection, and numerous other components to help workers answer questions, predict future events, surface unseen relationships and trends, provide recommendations, and take actions to fix issues.

Content analytics, natural language processing, and entity and relationship extraction are key components in dealing with enterprise information. According to IDC’s Global DataSphere model developed in 2018, of the 29 ZB of data creation, 88% is unstructured content that needs the aforementioned technologies to understand and extract the value from it. In addition, most of this content is stored in dozens, if not hundreds of individual silos, so repository connectors and content aggregation capabilities are also highly desired.

AI and machine learning provide actionable insights and can enable intelligent automation and decision making. Key technology and process considerations include:

  • Gleaning insights from unstructured data and helping to “connect the dots” between previously unrelated data points
  • Presenting actionable information in context to surface insights, inform decisions, and elevate productivity with an easy-to-use application
  • Utilizing information handling technologies that can be used in large scale deployments in complex, heterogeneous, and data-sensitive environments
  • Enriching content automatically and at scale
  • Improving relevancy continuously over time, based on user actions driven by machine learning
  • Improving understanding by intelligently analyzing unstructured content

IDC believes that the future for AI-based information access and retrieval systems is very bright, because the use of AI and machine learning coupled with next-generation content analysis technologies enable search systems to empower knowledge workers with the right information at the right time.

The bottom line is this: enabled by machine learning–based automation, there will be a massive change in the way data and content is managed and analyzed to provide advisory services and support or automate decision making across the enterprise. Using information-driven technologies and processes, the scope of knowledge work, advisory services, and decisions that will benefit from automation will expand exponentially based on intelligent AI-driven systems like those that Sinequa is offering.

For more information on using AI to be an information leader, I invite you to read the IDC Infographic, Become Information Driven, sponsored by Sinequa at

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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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Surfing the Cognitive Search Wave (as a Leader) Again

The Forrester™ Wave: Cognitive Search, Q2 2019
Sinequa has again been named a leader in “The Forrester™ Wave: Cognitive Search, Q2 2019.” The report is available for download here.

For global enterprises with complex use cases, Sinequa stands out as the right platform, according to the latest Forrester™ Wave1.

Let’s take a closer look and unpack some of the underlying details….

“Sinequa augments intelligence en masse […] to help organizations become ‘information driven’ by augmenting the intelligence of every employee.”

This statement astutely captures Sinequa’s view that it’s not about the data, the software, or analytics, it’s about the people. This perspective lies at the heart of our tagline, Become Information-Driven, which implies that organizations should leverage modern technology to power solutions that help individual knowledge workers accomplish their goals in order for the entire organization to perform at peak levels and outcompete in their market.

“[Sinequa] accomplishes this by surfacing knowledge, uncovering insights, and connecting experts via its cognitive search technology.”

These are indeed critical capabilities that should transcend the current (or future) state of the customer’s IT environment. As Sinequa-powered solutions can (and should!) be deployed wherever the relevant content and data resides – whether it be on-premise, in private or public clouds, or as a hybrid model – Sinequa customers can choose the deployment model that best aligns with their strategy and security policies, particularly when it comes to hosting sensitive data in the cloud.

“Sinequa expertly balances using its own NLU technology in combination with the latest open source ML technology.”

At its core, the Sinequa platform employs technology that automates the interpretation of meaning and applies structure to unstructured content. It intelligently combines deep natural language processing, rich semantic analysis, advanced entity extraction, and machine learning technology. This combination distills meaning from unstructured content and removes the noise, producing a clean and enriched index that consistently provides relevant information in context. In recent years, Machine Learning technology has been expertly woven into the platform to enhance understanding of complex data through experience instead of (or along with) codified business rules. Cohesively integrating these technologies into the Sinequa platform has effectively produced a production-grade machine learning platform for any enterprise.

“[Sinequa] has significant industry expertise in and offers solutions for life sciences, financial services, and manufacturing.”

This is no accident. With over a decade of experience, Sinequa understands the attributes of our most successful customers and realizes that they thrive with Sinequa because:

  • They are geographically-distributed and knowledge-centric
  • They have ambitious business goals and sophisticated IT environments
  • They routinely collaborate internally across geographies and across lines of business to drive decisions
  • They depend on highly diverse, high value content and data in different languages to drive their business forward
  • They either work with Systems Integrators for project execution or have their own in-house resources with a dedicated focus on systems integration

At Sinequa, we realize that a large share of our customers match these criteria and occupy the verticals pointed out by Forrester – i.e. life sciences, financial services, and manufacturing. After all, this is where Sinequa’s core competencies and unique capabilities can make the biggest impact.

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

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