Meaning
Embeddings represent text, products or other supported media as vectors and enable similarity search across wording differences.
Semantic and vector search
Build semantic search that finds meaning without losing permissions or precision
We build search systems for products, documents, knowledge and images. Embeddings produce semantic candidates; keywords, filters, access rules and measured relevance turn them into dependable enterprise search.
Semantic search complements exact search rather than replacing it
Keyword search is strong for product numbers, names, specialist terms and exact phrases. Vector search finds content that matches the meaning even when different words are used. In business systems, hybrid retrieval is often the strongest baseline: both candidate lists are combined with metadata, filters and business rules.
Precision
Keywords, attributes, dates, source status and boosting rules preserve exact signals in ranking.
Authorization
Tenant, role, owner, document class and approval status restrict candidates before output, not merely in the interface.
Suitable use cases
Product discovery
Users describe a need in natural language and receive suitable products combined with category, price, availability and seller filters.
Documents and knowledge
Manuals, contracts, policies, tickets or internal articles become discoverable without bypassing access control.
Image search
Supported multimodal embeddings can retrieve images from subjects, situations or descriptions, while usage rights remain separate metadata.
Support and RAG
An AI agent receives only relevant, current and permitted sources. Retrieval quality and answer quality are evaluated separately.
Marketplace discovery
Products, services and sellers are combined through meaning, filters, popularity and business rules.
Similar content
Related documents, products or cases are suggested without a rigid manual keyword list.
Architecture of dependable enterprise search
- Sources: APIs, databases, files or object storage provide content, stable IDs, timestamps and permissions.
- Preparation: text is segmented meaningfully; products and images receive consistent metadata. Empty, duplicate or stale content is detected.
- Embedding and index: a versioned model creates vectors. TEX8 can use Weaviate and a controlled in-house pipeline.
- Retrieval: keyword and vector search produce candidates. Filters, authorization, reranking and business rules determine output.
- Evaluation: real queries, expected results, no-result cases, latency and feedback are measured by version.
- Operations: changes, deletions and permission updates trigger traceable re-indexing; queues and index state are monitored.
Access control must be part of retrieval
A relevant result list is unacceptable when it exposes protected content. Tenant, user, role and approval filters are applied during retrieval. Snippets, facets, counts and previews must not reveal information derived from excluded documents. When source permissions change, the index must reach the same state through repeatable events and a visible failure queue.
Relevance is proven with an evaluation set
A successful demo query only proves that the technology can return something. Before launch, we collect representative queries: exact terms, natural language, synonyms, misspellings, ambiguous questions, authorized and unauthorized content, and requests with no useful answer. Each version compares ranking quality, coverage, false positives, latency and freshness.
| Measure | Example | Decision |
|---|---|---|
| Relevance | Is an expected result near the top? | Adjust weighting, chunking or model |
| Security | Does role A receive no content from role B? | Fix filters and index contract |
| Freshness | How quickly do updates and deletions appear? | Improve events, queue and re-indexing |
| Operations | Which queries have no useful result? | Improve content, synonyms or product data |
Staged implementation
- 1. Search goal: define users, sources, permissions and measurable success cases.
- 2. Data sample: inspect real content and build a representative evaluation set.
- 3. Baseline: compare classic search with semantic and hybrid variants.
- 4. Integration: connect retrieval to website, app, marketplace, desktop or AI agent.
- 5. Operations: monitor feedback, no-result queries, freshness, cost and latency.
No quality or privacy promise without evidence
Embeddings can produce misleading similarity and model changes can alter results. Quality depends on data, language, domain and measurement. Cloud or local models are selected from privacy, sensitivity, hardware, cost and evaluated quality. TEX8 does not promise perfect retrieval; it builds a measurable and versioned improvement process.
Semantic search FAQ
How does semantic search differ from classic search?
Classic search mainly evaluates words and exact signals. Semantic search uses vectors to find related meaning. Hybrid search combines both with filters and ranking rules.
Can the system search images?
Yes, with a suitable multimodal embedding model and consistent image metadata. Visibility and usage rights must still be enforced independently of the vector.
Is Weaviate suitable for the implementation?
Weaviate can provide vector, hybrid and filtered retrieval. Fit depends on data volume, operating model, tenant separation, latency and existing infrastructure.
How are protected documents kept out of results?
Authorization attributes are indexed and filtered during candidate retrieval. Tenant and role boundaries are also covered by negative tests.
How does the index remain current?
Updates and deletions are processed through repeatable events or scheduled re-indexing. The queue, failures and last successful index state need visible monitoring.
Related guides and solutions
Test with real queries before search goes live
We compare keyword, vector and hybrid retrieval on your data before building the production integration.