Harness the power of RAG and Vector Search for next-gen apps.

We implement vector databases and RAG (Retrieval-Augmented Generation) architectures to give your AI applications memory, context, and semantic understanding.

How we provide this service

Implementation of Oracle AI Vector Search and other vector stores (Pinecone, Qdrant).
Building RAG pipelines to ground LLMs in your private enterprise data.
Semantic search implementation for document and knowledge management.
Integration of vector capabilities into existing APEX and web applications.

Main usage scenarios

Intelligent enterprise search allowing "talk to your data" experiences.
Context-aware customer support chatbots and assistants.
Recommendation engines based on semantic similarity.
Automated document classification and extraction workflows.

Proof in practice

Clients and real-world results

Each engagement blends advisory, co-delivery, and enablement so you inherit the playbooks along with the product.

Legal

Legal Tech Company

Result

Built a semantic search for case law, improving research efficiency by 300%.

Retail

E-commerce Platform

Result

Implemented vector-based product recommendations increasing cart value by 15%.

Delivery system

How we keep teams confident

Beyond code, Werbonix introduces the rituals and tooling that make the practice stick inside your organization.

Accelerators & toolkits

RAG architecture blueprints for secure enterprise deployment.
Data ingestion and chunking pipelines for optimal vectorization.
Evaluation frameworks for measuring RAG retrieval accuracy.

Operating model & rituals

Experiment-driven development to find the right chunking/embedding strategies.
Close collaboration with data science and engineering stakeholders.
Continuous monitoring of vector index performance and relevance.

Ready to explore this practice?

Book time with the practice lead to tailor a roadmap around your goals.

Talk to us