Capabilities
What we build
Vector Database Architecture
Design and deploy vector storage on Pinecone, Weaviate, Qdrant, or pgvector, with namespace strategies, metadata filtering, index tuning, and infrastructure sized to your document corpus and query load.
Intelligent Chunking Strategies
Semantic chunking, recursive document parsing, table extraction, and parent-child chunk relationships that preserve context and improve retrieval precision for complex documents.
Hybrid Search & Reranking
Dense vector similarity combined with BM25 keyword search via reciprocal rank fusion, then cross-encoder rerankers like Cohere Rerank or ColBERT to surface the most relevant passages.
Multi-Source Ingestion
Pipelines that process PDFs, Confluence, Notion, Slack, code repositories, and structured databases into a unified retrieval layer with source tracking and incremental updates.
RAG Evaluation & Testing
Systematic evaluation using RAGAS, DeepEval, or custom frameworks to measure retrieval recall, answer faithfulness, hallucination rate, and context relevance before every deployment.
Query Routing & Decomposition
Handle complex user questions with intent classification, query rewriting, multi-hop retrieval, and sub-question decomposition so your RAG system handles nuanced queries — not just simple keyword lookups.
How we build it
Data & Requirements Audit
We catalog your document sources, analyze content types and formats, define the question patterns your system needs to handle, and establish accuracy baselines against a curated set of ground-truth Q&A pairs.
Retrieval Pipeline Development
Our engineers build the ingestion, chunking, embedding, and search pipeline — testing multiple embedding models, chunk sizes, and retrieval strategies against your evaluation set to maximize recall and precision.
Generation & Guardrails
We wire the retrieval layer to your chosen LLM with citation generation, answer grounding checks, confidence scoring, and fallback responses for cases where the knowledge base lacks sufficient information.
Production Hardening
Deploy with document sync automation, cache layers for frequent queries, latency optimization, and monitoring dashboards that track retrieval quality metrics — plus a feedback mechanism for users to flag incorrect answers.
Ground Your AI in
Your Actual Data
Let's discuss your knowledge base and build a RAG system that delivers accurate, cited answers your team can trust.
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Reviews
Founder & CEO, Sokrateque.ai
Tjaco Walvis
“Xpiderz has been instrumental in bringing Sokrateque.ai to life. Their team built advanced multi-agent systems, integrated Power BI with LLMs, and delivered a seamless data exploration pipeline that exceeded our expectations. Their deep understanding of AI, automation, and scalable architectures helped us unlock real value from our product. We're incredibly satisfied with their work and highly recommend them.”