Knowledge management
Semantic search across internal documents, wikis and manuals: employees find answers by asking natural-language questions instead of guessing exact search terms.
Vector databases
Vector Database Integration
A vector database stores content as embeddings and finds matches by meaning rather than by exact words. Context Studios, an AI-native development studio in Berlin, integrates Pinecone, Weaviate, Qdrant or pgvector into your applications, with embedding pipelines, hybrid search and monitoring for semantic search, RAG systems and recommendations.
AI-native development studio from BerlinPinecone · Weaviate · Qdrant · pgvectorOpenAI & Cohere embeddingsGDPR-compliant
- Workshop
- Setup
- Sprint
- Build & Support
Fixed price after scoping · proposal within 48 h
Last updated:
(01)
What is vector database integration?
AI technology
Vector database integration connects a vector database to existing AI architectures for semantic search, RAG systems and recommendations. Content is stored as embeddings that capture meaning: the question “How do I cancel my contract?” also finds texts about notice periods. Context Studios selects the right database, configures index and embeddings and takes both into production.
- Specialisation
- Vector indexing, hybrid search, embedding pipelines
- Technologies
- Pinecone, Weaviate, Qdrant, ChromaDB, pgvector
- Target audience
- Companies with RAG systems, search or recommendation needs
- Typical project duration
- Typically 2–8 weeks, depending on data volume
- Compliance
- GDPR, on-premise options, data encryption
RAG developmentAI data pipeline developmentLLM developmentAI recommendation system
(02)
What does the integration include?
Context Studios supports you from selection to production, end to end from Berlin
(01)
Evaluation & technology selection
We compare Pinecone, Weaviate, Qdrant, ChromaDB and pgvector against your requirements for data volume, latency, hosting and cost and recommend the right solution.
(02)
Schema design & indexing
We design collections, metadata fields and index parameters so that filters and similarity search stay fast even as your data grows.
(03)
Embedding pipelines (OpenAI, Cohere)
We turn your documents, products and knowledge bases into embeddings, with a suitable chunking strategy and automatic updates as soon as new content is added.
(04)
Hybrid search implementation
A combination of vector similarity and classic full-text search, so that both matches by meaning and exact matches are found.
(05)
RAG integration (retrieval-augmented generation)
Your knowledge base is connected directly to a language model: answers are based on retrieved sources and cite them, which significantly reduces hallucinations and makes statements verifiable.
(06)
Monitoring & evaluation (RAGAS)
We measure retrieval quality and answer quality with RAGAS and our own test sets and monitor latency and cost in ongoing operation.
(03)
How does the integration work?
- (01)
Initial consultation
Free 30-minute initial video call. We clarify your data sources, search scenarios and hosting requirements and give you a first assessment of feasibility and approach.
Day 1 - (02)
Proposal & planning
Breakdown of requirements, technical architecture plan with a database recommendation, milestones and a fixed-price proposal.
Days 2–3 - (03)
Integration & testing
Building the embedding pipeline, indexing and connecting it to your application with weekly demos. We measure search quality with test questions from your daily work. Goal for a first production version: about 4 weeks, depending on data volume.
From week 1 - (04)
Launch & support
Production deployment with monitoring and documentation. After that: 30 days of free bug fixing from final delivery, with operation and further development by agreement.
After integration
Frequently asked questions about vector databases
(01)Which database best suits our project?
That depends on data volume, hosting requirements and budget. Pinecone suits managed cloud setups, Qdrant and Weaviate offer more control and on-premise operation, and pgvector suits teams that already use PostgreSQL and do not want to run another database. We recommend the right solution after a short evaluation with your own data.
(02)How does Context Studios choose the right embedding model?
We test several embedding models, for example from OpenAI, Cohere or the open-source ecosystem, with your own data and typical search queries. We select the model with the best balance of match quality, language coverage, cost and data protection requirements. For German-language content we pay particular attention to multilingual models that handle technical terms and compound words reliably.
(03)Can Context Studios migrate existing search systems to vector databases?
Yes. We gradually move existing keyword searches to semantic or hybrid search. The previous system can keep running in parallel until the new solution has been tested and approved. Using test questions from your daily work, we compare the match quality of both systems, so you decide on the switch based on measurable results.
(04)What does a vector database integration cost?
Costs depend on data volume, the number of data sources and the depth of integration. A standard setup with one data source is considerably leaner than a solution with a RAG system, permission model and enterprise features. On top come running costs for hosting and embeddings, which we estimate transparently during scoping. Fixed price after scoping, proposal within 48 hours.
(05)What is the difference between vector search and classic full-text search?
Full-text search finds documents that contain the same words as the query. Vector search compares meanings instead: it also finds texts that describe a topic in different terms. Both have strengths, for example full-text search for product numbers or proper names. That is why we often combine them in a hybrid search that merges both result lists.
(06)Can vector databases be operated in a GDPR-compliant way?
Yes. Qdrant, Weaviate and pgvector can be self-hosted, in your own infrastructure or with a cloud provider using an EU data centre. For managed services, too, we check server location and the data processing agreement. Personal data can be pseudonymised before embedding, and permissions ensure that users only find documents they are allowed to see.
(07)How is the data in the vector database kept up to date?
Through an automated embedding pipeline: new or changed documents are detected, split into chunks, re-embedded and updated in the index, and deleted content is removed. Depending on the source, this happens via webhook, on a schedule or with every change. Your search therefore always works with the current state of knowledge, without manual maintenance.
(08)How do you measure search quality?
Together with your business unit, we compile a test set of real questions and expected matches. Against it we measure hit rate and ranking, and for RAG systems also answer quality with RAGAS. Every change to chunking, embedding model or index is checked against this test set, so that improvements are backed by evidence.
(04)
Which technologies do we work with?
(01)
AI & ML
Anthropic ClaudeOpenAI GPTGoogle GeminiOpen-Source LLMs (Llama, Qwen, DeepSeek, Mistral)ConvexRAG & Vector DBs (Pinecone, Weaviate)MCP (Model Context Protocol)Hugging Face TransformersComputer Vision (YOLO, SAM)ElevenLabs (Voice AI)Google Veo (Video AI)
(02)
Web & Mobile
Next.js & ReactTypeScriptReact Native & ExpoTailwind CSSShadcn/uiVercel Edge Runtime
(03)
Backend & Data
Node.js & Hono (Edge)PythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI
(04)
DevOps & Infrastructure
Vercel & AWSDocker & KubernetesCI/CD-Pipelines (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)
(05)
Where do industries use vector databases?
E-commerce
Semantic product search and AI recommendations: customers find suitable products even when they use different terms from the catalogue.
Legal
Intelligent document search in contracts and rulings: relevant passages and precedents are found by meaning rather than by keywords.
Pharma & research
Semantic search in specialist literature, studies and patent databases as a basis for research and decision-making.
Media & content platforms
Automatically find and recommend similar articles, videos or posts, based on their content rather than on tags alone.
IT support & DevOps
Automatic answer suggestions from tickets, runbooks and documentation speed up the handling of support requests.
(06)
Project examples
Examples we can build
Customer service
AI-powered support agent
An example we can build: a support agent that uses semantic search in your knowledge base to find suitable answers and can answer first-line requests automatically.
Automated first response · Multilingual · Available 24/7
Knowledge management
RAG-based document system
An example we can build: a RAG-based document system with semantic search across large document collections and answers with source references, which can make research much easier.
Answers with sources · Fast search · Scalable
Process automation
Workflow automation with AI agents
An example we can build: AI agents that access company knowledge semantically within your workflows, which can reduce manual research work.
End-to-end automated · Fewer manual errors · Time savings
(07)
Vector database integration: consulting in Berlin
- Founder AI-native since
- 2024
- Phone
- +49 30 96610664
- info [at] contextstudios [dot] ai
Related pages
Semantic search for your business
Make your data semantically searchable. Start with a free 30-minute initial call or write to [email protected].
- First call: 30 minutes by video
- Directly with the founder, Michael Kerkhoff
- Free of charge, no obligation
Context Studios · Kaiser-Friedrich-Str. 6 · 10585 Berlin