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Enterprise Search Advisory.

Design the enterprise search architecture that actually works — with a relevance audit, content and metadata strategy, and semantic indexing design that addresses the root causes of poor search, not just its symptoms.

Information Management Services

The Situation — When Clients Come to Us

Organizations come to us for enterprise search advisory when:

  • Internal search returns irrelevant, incomplete, or inconsistent results — and employees have stopped using it, defaulting to asking colleagues or rebuilding information from scratch
  • An organization has deployed an enterprise search platform (Microsoft Search, Coveo, Elastic, Solr, or equivalent) but relevance quality is poor — and the problem isn't the platform, it's the content structure, metadata, and taxonomy design that the platform depends on
  • GenAI, RAG, or AI knowledge assistant programs are underperforming because the search and retrieval layer those systems depend on has never been designed for precision, recall, or semantic relevance
  • Organizations planning a new enterprise search implementation or migration and wanting the architecture, indexing strategy, and taxonomy integration designed before the platform is selected or configured
  • Content management teams who have invested in taxonomy and metadata design and want to ensure that investment is correctly operationalized in the search indexing strategy — rather than lost in a default platform configuration

What We Do — Our Approach

  1. // Phase 1

    Search Relevance Audit & Current-State Analysis

    We conduct a search relevance audit combining qualitative user research (what users search for, what they expect, where they fail) with quantitative analysis (zero-result rates, query refinement patterns, click-through distribution, result rank quality) — establishing a documented baseline and identifying the highest-value improvement opportunities. We distinguish between content problems, metadata problems, taxonomy problems, and platform configuration problems.

  2. // Phase 2

    Content & Metadata Analysis

    We analyze the content and metadata in source repositories — identifying enrichment opportunities, classification gaps, and taxonomy improvements that will improve search ranking signals. We produce specific, prioritized recommendations for content and metadata remediation that will improve relevance without platform changes.

  3. // Phase 3

    Search Architecture Design

    We design the search architecture — indexing strategy, field weighting model, faceted search design, result type definitions, and the taxonomy and ontology integration approach that enables semantic search capabilities. We document the search system architecture and produce a platform-agnostic specification that can be implemented by the client team or technology partner.

  4. // Phase 4

    Semantic Search & Taxonomy Integration Design

    We design the semantic search capabilities — knowledge graph integration for entity-aware search, taxonomy-driven facets and filters, synonym and related-term expansion from the controlled vocabulary, and the auto-tagging approach that enriches content metadata at index time. We specify the integration between the search platform and the taxonomy management system.

  5. // Phase 5

    Enterprise Search Optimization (ESEO) Program Design

    We design the ongoing ESEO program — governance processes for search quality, content quality standards, monitoring dashboards, query analytics review cycles, and the iterative improvement process that keeps search performing as content, vocabulary, and user needs evolve. Implementation is performed by the client or technology partner.

What You Get — Deliverables

  • Search relevance audit report — baseline metrics, root-cause analysis, and prioritized improvement recommendations
  • Content and metadata enrichment specification — specific remediation priorities with expected relevance impact
  • Search architecture design — indexing strategy, field weighting, facet design, result types, and semantic search specifications
  • Taxonomy and ontology integration design — connecting controlled vocabulary to search indexing and faceted navigation
  • Semantic search capability specifications — entity-aware search, synonym expansion, and auto-tagging at index time
  • Platform-agnostic implementation specification for the client team or technology partner
  • ESEO governance program design — quality monitoring, content standards, analytics review cycle, and iterative improvement process
  • Before/after relevance metric framework for measuring improvement

Most enterprise search problems aren't platform problems — they're content, metadata, and taxonomy problems.

Iknow's search advisory practice addresses the root causes that platform vendors don't tell you about. Start with a relevance audit.

Contact Iknow

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