Build retrieval-augmented generation systems that ground LLM responses in your actual documents, databases, and knowledge — accurate, fast, and hallucination-free.
The pipeline that connects your data to intelligent AI responses
From simple document chatbots to enterprise-grade knowledge platforms
Chat with your PDFs, docs, and knowledge bases. Upload documents and get accurate, cited answers instantly. Perfect for internal knowledge management.
Semantic search across your entire document corpus. Hybrid search combining keyword and vector matching for maximum recall and precision.
Combine vector retrieval with knowledge graphs for complex reasoning over structured and unstructured data. Superior to pure vector search for multi-hop questions.
AI agents that dynamically choose retrieval strategies, query reformulation, and multi-source synthesis. Self-reflective retrieval with quality checks.
Cross-language retrieval and response generation. Index documents in any language and query in another. Built with multilingual embedding models.
REST/GraphQL APIs for RAG pipelines. Integration with Slack, Teams, Notion, Confluence, and custom applications. Webhook-based document sync.
Deep experience with the leading vector storage solutions
Managed vector DB with metadata filtering, namespaces, and hybrid search.
Managed CloudOpen-source with built-in vectorization, GraphQL API, and multi-tenancy.
Open SourceHigh-performance Rust-based vector DB with advanced filtering and quantization.
High PerformanceDeveloper-friendly, great for prototyping and small-to-medium scale RAG systems.
Developer FriendlyPostgreSQL extension — keep vectors alongside relational data. Zero new infrastructure.
PostgreSQLDistributed vector DB for billion-scale datasets. GPU-accelerated search.
Enterprise ScaleClear pricing for every RAG project scope
Simple RAG chatbot
1–2 week delivery
Multi-source RAG
3–5 week delivery
Full knowledge platform
6–10 week delivery
RAG (Retrieval-Augmented Generation) grounds LLM responses in your actual data by retrieving relevant documents before generating an answer. The LLM can only cite and synthesize information it retrieves, dramatically reducing hallucinations compared to relying on the model's training data alone.
Most formats: PDFs, Word docs, HTML pages, Markdown, CSVs, JSON, emails, Slack messages, Confluence pages, Notion databases, SQL tables, and more. I build custom parsers for specialized formats and handle images/tables with multimodal approaches.
For most projects, Pinecone (managed, zero ops) or Qdrant (self-hosted, high performance) are excellent choices. If you already use PostgreSQL, pgvector avoids new infrastructure. For billion-scale datasets, Milvus handles distributed workloads. I'll recommend based on your scale and budget.
All data stays within your infrastructure or private cloud. I implement document-level access control, encryption at rest and in transit, and audit logging. For regulated industries, I deploy on-premise with air-gapped LLMs. No data ever leaves your security boundary.
RAG is best for factual Q&A over dynamic, up-to-date knowledge. Fine-tuning is better for teaching models a specific style, format, or domain reasoning. Often the best results come from combining both — fine-tune for style and domain understanding, then use RAG for grounded factual answers.
Let's build a retrieval system that makes your knowledge instantly accessible through natural conversation.