Step 01
Data Analysis
We learn about your data sources, documentation, and information structure. We plan embeddings and indexing.
Cooperation process
A transparent process that leads from idea to finished product
Step 01
We learn about your data sources, documentation, and information structure. We plan embeddings and indexing.
Step 02
We configure embeddings, select vector database (Pinecone, Weaviate, pgvector), and index data.
Step 03
We implement semantic search, ranking, and re-ranking. We optimize recall and precision.
Step 04
We integrate RAG with LLM, optimize context and prompts. We ensure source citation.
Step 05
We test retrieval and generation quality. We optimize chunking, embeddings, and prompts.
Step 06
We deploy the system with automatic index updates. We provide monitoring and support.
Key benefits
Business outcomes first—technology is the means, not the end in itself.
Finding information by meaning, not just keywords.
Scalable vector databases for millions of documents.
Every answer with reference to data source.
95%+ accuracy thanks to retrieval and ranking optimization.
Indexes update automatically when data changes.
Users ask in English, system finds answers.
Modern tools and proven solutions for the best results
Below is an example scope of work that we adjust to the stage and goals of the project.
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.
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.