Semantic search
Finding information by meaning, not just keywords.
RAG and knowledge bases
RAG and semantic search
Intelligent knowledge bases with context
We build RAG systems that connect a language model to approved company knowledge while preserving permissions and source traceability. The delivery covers ingestion, retrieval, ranking, citations, evaluation and monitoring—not just a vector database.
We build RAG (Retrieval-Augmented Generation) systems that combine semantic search with generative AI. Your documentation, knowledge base, and data become accessible through natural language.
Cooperation process
How does our cooperation look like?
A transparent process that leads from idea to finished product
01
Data Analysis
We learn about your data sources, documentation, and information structure. We plan embeddings and indexing.
02
Embeddings & Vector DB
We configure embeddings, select vector database (Pinecone, Weaviate, pgvector), and index data.
03
Retrieval & Ranking
We implement semantic search, ranking, and re-ranking. We optimize recall and precision.
04
Generation & Context
We integrate RAG with LLM, optimize context and prompts. We ensure source citation.
05
Evaluation & Optimization
We test retrieval and generation quality. We optimize chunking, embeddings, and prompts.
06
Deployment & Updates
We deploy the system with automatic index updates. We provide monitoring and support.
Key benefits
Why choose us?
Business outcomes first—technology is the means, not the end in itself.
Vector databases
Scalable vector databases for large document collections.
Source citation
Every answer with reference to data source.
Controlled quality
Retrieval and ranking evaluated on project data.
Automatic updates
Indexes update automatically when data changes.
Natural language
Users ask in English, system finds answers.
FAQ
Questions about RAG and enterprise knowledge bases
When should we use RAG instead of fine-tuning?
RAG is usually a better fit when answers depend on frequently changing documents and need source citations. Fine-tuning can change model behavior or style, but it is not a convenient way to keep company knowledge current.
Which data sources can a RAG system use?
Common sources include documents, intranet pages, file systems, databases, CRM and help desks. Each source needs update, versioning and permission rules so users only receive information they are authorized to access.
Can answers include citations and links to source documents?
Yes. We retain the provenance of retrieved passages and can display the document name, section and link. Citations improve verifiability, but retrieval and answer quality still need evaluation.
How do you test whether a RAG system answers correctly?
We create a representative question set with expected sources and answer criteria. We separately measure retrieval quality, faithfulness to the source, correct refusal when evidence is missing, latency and cost per question.
Technologies
What technologies do we use?
Modern tools and proven solutions for the best results
OpenAI Embeddings
Embeddings
Pinecone
Vector DB
Weaviate
Vector DB
pgvector
Vector DB
LangChain
Framework
Next.js
Frontend
Node.js
Backend
PostgreSQL
Database
Guides and proof
Articles, technology pages and case studies that support your buying decision.
LLM integrations for products
Add knowledge-grounded search and answers to an existing application.
Read moreAI agents and automation
Use company knowledge inside controlled workflows and decisions.
Read moreAI Opportunity Sprint
Assess data quality and choose the use case before implementing RAG.
Read moreCase study: Cognic AI
An example of AI working with sensitive context and documentation.
Read moreService scope
What does the service include?
Below is an example scope of work that we adjust to the stage and goals of the project.
Data analysis and preparation
Embeddings and vector databases
Semantic search and ranking
RAG integration with LLM
Source citation system
Quality evaluation and optimization
Automatic index updates
Monitoring and support
Next step
Ready to start?
Schedule a free consultation and learn how we can help with your project. After our DDT process (Discovery, Design & Technology), we offer a price guarantee and a fixed-price agreement.
Contact
Let's talk
about the project.
Have an app idea or need technological support? Write to us — we'll prepare a preliminary analysis and estimate within 48h. Projects that go through our DDT process (Discovery, Design & Technology) come with a price guarantee and a fixed-price agreement — a key differentiator for us.
Write to us[email protected]
Visit us
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80-890 Gdansk, PolandNearshore product delivery across EU, UK and US time zones.
80-890 Gdansk, PolandNearshore product delivery across EU, UK and US time zones.
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