Enterprise knowledge retrieval, grounded in proof.
We build production-grade Retrieval-Augmented Generation (RAG) and search systems over your private documentation. Answers are synthesized with exact passage citations and character-level source lineage.
System Architecture
Hybrid Retrieval Pipeline
Combines dense vector semantic embeddings with BM25 sparse keyword matching and reciprocal rank fusion for maximum retrieval precision.
Hierarchical Ingestion & Contextual Chunking
Documents, tables, and code snippets are parsed with semantic awareness, preserving structural context, headers, and metadata tags.
Deterministic Source Citation & Verification
Every synthesized response links to verifiable passages. If documentation is insufficient, the system gracefully declines to answer.
Role-Based Access Control (RBAC)
Permissions are enforced at the retrieval layer, guaranteeing that users only retrieve information their credentials permit.
The Antidote to Hallucination
“No speculation, no open-web contamination. Every statement grounded in authenticated internal sources.”
Off-the-shelf vector database tutorials fail in production because they rely on naive text splitting and single-index cosine similarity. When documents contain complex tables, technical jargon, or conflicting versioning, basic RAG systems hallucinate.
We engineer robust knowledge architectures: re-ranking models, query decomposition, context compression, and automated evaluation metrics (faithfulness, answer relevancy, and context recall) to guarantee factual consistency.
Knowledge Retrieval Systems
Select a knowledge system architecture to explore your technical implementation.
Scoping Requirements
Source Corpora & Ingestion Cadence
File formats (PDF, Markdown, HTML, Notion, Confluence, SQL) and update frequencies (real-time vs scheduled batch).
Access Control & Security Policies
User authentication schemes (OAuth, SSO, SAML) and permission boundary rules across document repositories.
Benchmark Golden Dataset
Representative set of high-priority questions with known correct answers used to evaluate retrieval accuracy.
Questions
How do you handle complex document layouts like tables and PDFs?
We employ multimodal layout analysis and structured document parsers that convert tables and charts into semantic representations (e.g. structured Markdown or JSON) rather than flattening them into unreadable raw text chunks.
Can our knowledge system run entirely on our own cloud infrastructure?
Yes. We frequently deploy knowledge systems entirely inside client AWS, GCP, or Azure VPCs using self-hosted vector stores (such as pgvector, Qdrant, or Milvus) and private model endpoints, ensuring zero external data leakage.
How do you evaluate retrieval quality over time?
We establish continuous evaluation pipelines using frameworks like Ragas or TruLens, tracking context precision, context recall, faithfulness, and answer relevancy against curated golden datasets and production query samples.
Tell us the corpus and the questions it must answer.