Semantic Search Implementation for Products, Suppliers and Enterprise Knowledge
Replace brittle keyword-only search with meaning-aware retrieval grounded in structured metadata, embeddings and business semantics.
Users often know what they need but not the exact words stored in a catalog or knowledge base. RFQmatch implements semantic search that combines vector retrieval, metadata filters and domain semantics so relevant products, suppliers or documents are found even when terminology differs.
What is Semantic Search Implementation?
Implements semantic enterprise search based on intent, embeddings and ontology-aware retrieval.
The problem this solves
Keyword search fails to understand intent, context and business meaning.
Symptoms you may recognise
- Search misses: Do you notice employees typing the same question in different ways and getting completely different results for the same policy, contract, or procedure?
- Time waste: Do you see teams spending 15 to 30 minutes hunting through SharePoint, drives, ticket notes, and inboxes before they find the right file or answer?
- Wrong answers: Do you experience staff forwarding outdated documents because the search tool surfaces old versions higher than the current one?
- Duplicate work: Do you notice people recreating reports, templates, or analyses because they cannot find the existing one quickly enough?
- Knowledge silos: Do you see critical information trapped in department-specific systems so one team cannot easily find what another team already documented?
KPIs that deteriorate
- Longer resolution: Do you see customer service or internal support resolution times increase because agents cannot quickly find the right answer?
- Lower productivity: Do you notice billable staff, analysts, or operations teams spending more time searching than completing casework or deliverables?
- Higher rework: Do you see a rise in duplicated analyses, repeated data pulls, and re-created documents because prior work is not easy to retrieve?
- Slower onboarding: Do you notice new hires take longer to become effective because they cannot locate procedures, templates, and past examples on their own?
- More escalations: Do you see more questions routed to senior experts because frontline staff cannot reliably find approved knowledge?
Business risks
- Compliance exposure: Do you risk people using outdated policies, expired contract language, or obsolete SOPs because the current version is hard to locate?
- Decision delays: Do you risk leadership making slower or weaker decisions because supporting facts are scattered across systems and hard to discover?
- Customer leakage: Do you risk losing customers when service teams cannot find the right information fast enough during live interactions?
- Expert dependency: Do you risk key-person bottlenecks where a few subject matter experts become the only source of institutional knowledge?
- Asset waste: Do you risk paying for expensive content platforms, repositories, and knowledge bases that employees still cannot practically use?
Typical trigger events
- Failed audit: Did a compliance review or audit uncover that teams could not quickly prove which policy, contract, or procedure was current?
- Search overhaul: Did a major intranet, DMS, or knowledge base migration make it obvious that people still cannot find content after the upgrade?
- AI pilot: Did a chatbot or copilot pilot fail because it could not reliably retrieve the right internal information?
- M&A integration: Did a merger or acquisition expose that two organizations use different terms, taxonomies, and document stores?
- Support spike: Did a sudden increase in customer or employee inquiries overwhelm teams because they could not retrieve answers fast enough?
Who this service is for
Organisation size
100-500 · 500-2000 · 2000-10000 employees — 50M-250M USD, 250M-1B USD, 1B+ USD
Company maturity
Scale-up, Enterprise, Multinational
Industry verticals
Financial Services, Healthcare and Life Sciences, Legal and Professional Services, Technology and Software, Manufacturing and Supply Chain
Typical buyers
- Chief Information Officer (CIO) — Decision Maker
- Chief Technology Officer (CTO) — Decision Maker
- Chief Data Officer (CDO) — Decision Maker
What RFQmatch delivers
Deliverables
- Enterprise Search Ontology Map mapping domain-specific vocabulary, acronyms, and operational concepts.
- Vector Embeddings Strategy and Pipeline Design specifying model selection, chunking parameters, and indexing schedules.
- Production-deployed Semantic Search Engine integrated into primary employee portals and intranet systems.
- Role-Based Access Control (RBAC) Validation Matrix ensuring search results respect existing data governance boundaries.
- Search Performance & Analytics Dashboard tracking query latency, click-through rates, and user satisfaction metrics.
Business outcomes
- Significant recovery of lost operational hours, expanding weekly productive employee time.
- Accelerated customer support and sales responses due to immediate internal file and contract locating capabilities.
- Enhanced organizational agility from breaking down structural information silos across business functions.
- Minimized duplication of work caused by teams failing to discover existing internal research or collateral.
- Future-proofed enterprise knowledge foundations, ready to support advanced generative AI use cases instantly.
Expected ROI
- 50% reduction in employee hours spent manually hunting for internal documents
- 35% acceleration in complex RFQ analysis and supplier evaluation turnaround
- Significant reduction in Generative AI implementation errors via high-fidelity retrieval matrices
- Measurable mitigation of compliance operational risks through robust cross-system discovery
- Rapid optimization of employee onboarding times through direct access to contextual truth
How the engagement works
- 1
Phase 1: Discovery & Ontology Alignment
Assess the existing search landscape, audit target data sources (ERP, RFQs), and define the initial domain ontology and intent structure.
- 2
Phase 2: Architecture & Embeddings Pipeline
Select embedding models, design data ingestion and chunking strategies, and set up the vector database infrastructure.
- 3
Phase 3: Integration & Core Implementation
Connect the semantic search engine to target repositories, implement ontology-aware retrieval logic, and apply security controls.
- 4
Phase 4: Testing & Relevance Tuning
Conduct precision/recall benchmarking, execute user acceptance testing across departments, and fine-tune embedding weights.
- 5
Phase 5: Change Management & Launch
Deliver user training, deploy the solution to production, launch adoption campaigns, and establish ongoing optimization processes.
Small project
8 - 10 weeks
Medium project
14 - 18 weeks
Large project
22 - 26 weeks
Quick Scan
A 4-week diagnostic evaluating current search system gaps, data ingestion readiness, and outlining a tailored semantic architecture roadmap.
Best for: Organizations looking to understand technical complexity and build a business case before committing capital.
Pilot
A 12-week focused implementation targeting one high-value data repository (e.g., procurement contracts) to validate semantic lift.
Best for: Companies needing immediate, visible validation of search improvements to secure wider corporate budget.
Full Implementation
A comprehensive end-to-end rollout connecting all primary enterprise repositories, embedding a global ontology, and enabling full scale.
Best for: Enterprises suffering severe productivity losses from fragmented information silos ready for full transformation.
Data and systems required
- Knowledge base
- ERP
- contracts
- RFQs
- vector index
Scope and pricing
Semantic Search Pilot
From €20,000 (indicative)
What's included
- Search-use-case design
- corpus/data audit
- chunking/index design
- embeddings
- metadata filters
- hybrid retrieval
- ranking tuning
- evaluation set
- pilot API/UI integration
- relevance report.
Not included
- Full website rebuild
- unlimited corpus cleanup
- search-engine licences
- product enrichment unless added
- production hosting unless scoped.
Why RFQmatch
RFQmatch Hybrid Semantic Retrieval
RFQmatch combines text meaning with procurement/product/supplier metadata and ontology context; evaluates retrieval against real buyer queries; avoids treating embeddings as the only source of relevance.
- Hybrid rather than vector-only
- domain metadata and taxonomy aware
- measurable relevance evaluation
- B2B product/supplier search expertise
- direct fit with knowledge graphs and copilots.
Related services
- Knowledge Graph Engineering; Product Ontology Engineering; Supplier Ontology Development; Metadata Engineering; Procurement Knowledge Base.
Frequently asked questions
What is semantic search?
Semantic search retrieves items based on meaning rather than exact keyword overlap, usually using embeddings plus other ranking signals.
Is vector search the same as semantic search?
Vector retrieval is one technique
How do you measure search quality?
production semantic search often performs better when combined with keywords, metadata filters and business rules.
Does semantic search require an ontology?
Use representative queries and relevance judgments to track metrics such as precision, recall, success rate and zero-result rate.
Can permissions be preserved?
No, but taxonomy or ontology context can improve filtering, disambiguation and relationship-aware ranking.
Ready to get started?
Tell us about your situation and we'll help you scope the right engagement.
Request a Semantic Search Pilot