FAST Search

FAST Search
Name	FAST Search
Type	Private
Founded	2000
Headquarters	Oslo, Norway
Industry	Information retrieval
Products	Enterprise search, indexing, clustering
Fate	Acquired

FAST Search

FAST Search was an enterprise search and information retrieval company known for large-scale indexing, multilingual search, and semantic analysis. Founded in Oslo, with research ties to academic and industry institutions, the company served customers across telecommunications, finance, publishing, and government sectors. Its technology influenced subsequent platforms in web search, vertical search, and enterprise content management.

Overview

FAST Search developed scalable search appliances and software for indexing structured and unstructured content across distributed environments. The company targeted clients such as Telenor, Deutsche Bank, BBC, Thomson Reuters, and Siemens. FAST Search integrated with enterprise systems like Microsoft SharePoint, SAP NetWeaver, Oracle Database, Salesforce.com, and IBM Lotus Domino to provide relevance ranking, clustering, and content enrichment. Research collaborations included ties to Norwegian University of Science and Technology, NTNU Department of Computer Science, and industry research centers.

History and Development

FAST Search was established by researchers and engineers drawing on work in information retrieval, natural language processing, and distributed systems. Early milestones included participation in European research initiatives alongside organizations such as CERN and European Space Agency, and technology transfer activities with SINTEF. The firm expanded through rounds of investment from venture entities and partnerships with companies like Accel Partners and Sequoia Capital (investors in related technology firms). Key management and engineering recruits had backgrounds at institutions such as Bell Labs, Silicon Graphics, and TeliaSonera.

Throughout its history, FAST Search grew its product line, acquired specialist teams with expertise in clustering, taxonomy management, and multimedia indexing, and engaged in standards-related work with bodies including W3C and ISO. Competitive interactions involved large vendors such as Google, Microsoft, IBM, and Autonomy Corporation. The company’s trajectory culminated in acquisition activities and strategic transitions involving multinational technology firms.

Technology and Features

FAST Search’s architecture combined distributed indexing, incremental crawling, and real-time relevance computation. Core components supported tokenization, stemming, language detection, and named-entity processing leveraging linguistic resources comparable to work from groups at Stanford University, University of Cambridge, and Massachusetts Institute of Technology. The platform implemented clustering algorithms influenced by research from Carnegie Mellon University, and relevance ranking incorporating link analysis ideas from University of California, Berkeley research.

Feature sets included faceted navigation popularized by deployments at organizations like eBay and Amazon (company), advanced query operators similar to those in academic systems from University of Michigan and Cornell University, and multimedia indexing approaches akin to projects at MIT Media Lab. FAST Search supported multilingual pipelines for languages studied at University of Helsinki and Saarland University, and employed compression and distribution techniques informed by publications from ETH Zurich and Princeton University.

Deployment and Use Cases

Typical deployments spanned intranet portals, public-facing search for media, and vertical search for sectors such as finance and healthcare. Notable customer types included telecom operators (e.g., Telenor), broadcasters (e.g., BBC), news agencies (e.g., Agence France-Presse), and publishers (e.g., Wolters Kluwer). Integrations extended to content management systems from Adobe Systems, OpenText, and Interwoven as well as business applications from SAP, Oracle Corporation, and Microsoft Corporation.

Use cases comprised knowledge management in firms like Deloitte, regulatory discovery workflows used by law firms such as Baker McKenzie, and e-commerce search experiences akin to those built by Rakuten and Alibaba Group. FAST Search also supported public sector information access initiatives modeled on projects by European Commission agencies and national libraries including National Library of Norway.

Performance and Evaluation

Performance assessments emphasized indexing throughput, query latency, and relevance effectiveness measured using test collections and evaluation protocols originating from the Text REtrieval Conference and academic benchmarks maintained by groups at NIST and TREC. Scalability was demonstrated in clusters running across commodity servers from vendors such as Dell Technologies and HP Inc., with parallelism strategies comparable to implementations in Apache Hadoop-era research. Precision, recall, and mean average precision scores in case studies were compared against offerings from Google Search Appliance and enterprise systems from Autonomy.

Operational metrics included fault tolerance, replication behaviors inspired by distributed systems research at University of California, San Diego, and maintenance workflows similar to those used in large-scale content platforms at Facebook and Twitter. Evaluations also considered multilingual retrieval challenges tackled in collaborative projects with institutions like University of Edinburgh.

Adoption, Licensing, and Business Model

FAST Search’s commercial model combined software licensing, professional services, and appliance sales through partnerships with system integrators such as Accenture, Capgemini, and KPMG. Licensing arrangements ranged from perpetual licenses used by enterprises like Deutsche Bank to subscription-style support agreements resembling models used by Salesforce.com partners. The company pursued channel strategies with hardware partners and OEM agreements akin to those between Oracle Corporation and ISV ecosystems.

Post-acquisition transitions affected support, product roadmaps, and licensing terms for customers, similar to historical precedents in acquisitions involving Sun Microsystems and BEA Systems. Legacy technology and expertise influenced later search and analytics offerings from major vendors and open-source communities, contributing to projects and initiatives at institutions such as European Commission research programs and university labs across Europe and North America.

Category:Information retrieval companies