Search Optimization & Semantic Search

We transform your search into an intelligent discovery experience — using semantic search, vector databases, rerankers, and RAG to deliver the right results faster.

Semantic & Vector Search
Neural Reranking & Relevance Tuning
RAG-Ready Architecture

Turn Search into a Competitive Advantage

Users expect Google-level search everywhere — your help center, product, and internal tools. Traditional keyword search often fails with vague, long, or multi-language queries. At Cognivox Labs, we design search systems that understand intent, leverage embeddings and rerankers, and stay fully under your control.

Relevance That Makes Sense

We tune search so your users see truly relevant results for their language, context, and business domain.

Semantic Understanding

Move beyond exact matches. Semantic embeddings capture meaning, synonyms, and intent — even in noisy queries.

Reranking & Control

Neural rerankers and business rules ensure the top results are not just similar, but truly useful and safe.

Where Better Search Pays Off

We focus on high-impact search scenarios where relevance, trust, and speed directly influence revenue or productivity.

Knowledge base & help center search that actually finds the right answer
Internal document search across PDFs, Confluence, SharePoint, and drives
E-commerce product search optimized for intent and conversion
SaaS in-app search for users, records, tickets, or configurations
Legal, technical, or compliance search with filters & citations
Context-aware search powering chatbots & AI copilots

Built for Products & Teams

Whether you are shipping a SaaS product, an internal portal, or a public knowledge base, we design search flows that match your users’ mental models.

Smart filters and ranking tuned to your domain.
Easy integration via clean, documented APIs.

What Our Search Solutions Include

Semantic understanding of queries instead of pure keyword matching
Hybrid retrieval: combine BM25 + embeddings for robust relevance
Neural reranking for higher precision on the top results
Faceted filters, permissions-aware search, and result boosting
Support for multiple languages (ideal for DE/EN environments)
RAG-ready architecture for AI assistants built on your search layer
Analytics to track zero-result queries, clicks, and search quality
Optimized for latency, scalability, and cost-efficiency

Search & RAG Tech Stack

We use a modular architecture so you can combine classic search engines, vector databases, and LLMs without vendor lock-in.

Semantic Search
Vector Search (pgvector, Pinecone, Qdrant, Supabase)
Hybrid Search (BM25 + Embeddings)
Neural Rerankers
RAG Pipelines
OpenAI / Azure OpenAI
Hugging Face Models
LangChain / Custom Pipelines
Next.js / Node.js / Python (FastAPI)
PostgreSQL / Elasticsearch / Meilisearch
Docker & CI/CD

Structured & unstructured content indexed with metadata for precise filtering and ranking.

Neural rerankers and embeddings tuned to your domain, not just generic text.

Permissions-aware search and GDPR-conscious deployments in EU-friendly environments.

Our Search Optimization Process

01. Audit & Strategy

We review your current search, data sources, and user behavior to identify gaps and opportunities.

02. Data & Index Design

We model your content, metadata, and permissions and choose the right mix of keyword, vector, and hybrid search.

03. Retrieval & Reranking Engine

We implement semantic retrieval, scoring logic, and neural rerankers tuned to your domain.

04. Integration & UX

We integrate the API into your app, portal, or chatbot with clean UI and filters that guide users, not confuse them.

05. Evaluation & Tuning

We measure relevance with real queries, adjust weights, and iterate using clear KPIs.

06. Monitoring & Evolution

We keep improving using search analytics, new models, and content changes over time.

Why Optimize Search with Cognivox Labs

Hands-on expertise in RAG and reranking from real-world client projects and academic research.

We design search as a stable backend capability — not just a quick embedding demo.

Balanced architectures: fast, explainable, and aligned with your infrastructure & budget.

Built-in support for integrating search with LLM-based chatbots and assistants.

Engineered in Germany with attention to security, access control, and data privacy.

Accurate. Fast. Explainable.

Our goal is not just “AI search”, but predictable and measurable improvements in how users find information.

Perfect if you’re building a knowledge-heavy product, platform, or internal tool and want search to feel like a superpower, not a bottleneck.

Ready to Upgrade Your Search?

Share your current search setup and we’ll outline a concrete plan — from quick relevance fixes to full semantic search & RAG rollout.