Enterprise RAG Development Company
Transform complex enterprise documents, databases, and unstructured knowledge into high-precision, citation-backed answers with zero hallucinations and strict access control filtering.
Overcoming the Critical Flaws of Naive Document Search
Standard vector chunking loses context and hallucinates. We engineer enterprise-grade retrieval pipelines.
Naive RAG systems retrieve irrelevant document chunks
Basic vector search matches synonyms poorly and misses crucial context buried in complex PDF tables or diagrams. We engineer multi-stage hybrid search and cross-encoder re-ranking.
Hallucinations and lack of verifiable source attribution
When employees or clients receive answers without citations, trust evaporates. We build strict citation systems that link every claim directly to original source text and page numbers.
Document permissions and access control leaks
A lower-level employee should never retrieve confidential executive compensation files. We enforce document-level and organization-level Access Control Lists (ACLs) directly in the vector search layer.
Multi-format document ingestion headaches
Enterprise data is trapped in scanned PDFs, Excel spreadsheets, PPTs, and Notion workspaces. We implement OCR and semantic chunking engines that preserve tables, hierarchy, and metadata.
Stale vector indexes when company data changes
Re-indexing millions of vectors when a single document updates is slow and expensive. We build real-time change-data-capture (CDC) pipelines that synchronize vector databases automatically.
High query latency degrading user experience
Users abandon assistants when queries take 10+ seconds. We optimize embedding generation, utilize approximate nearest neighbors (HNSW), and stream response tokens with sub-2-second time-to-first-token.
Production-Grade Retrieval-Augmented Generation
Hybrid vector search, table extraction, metadata filtering, and continuous evaluation benchmarks.
Hybrid Search & Semantic Re-Ranking
Combining dense vector embeddings with sparse BM25 keyword matching and Cohere/BGE cross-encoder re-ranking to achieve 95%+ retrieval accuracy.
Complex Document Parsing (OCR & Tables)
Ingesting complex financial statements, contracts, and technical manuals using LlamaParse, unstructured.io, and vision models with table preservation.
Enterprise Vector Database Engineering
Deploying and optimizing pgvector (PostgreSQL), Pinecone, Qdrant, Milvus, and Weaviate for multi-million vector datasets with fast filtering.
Security & Role-Based Access Filtering (RBAC)
Embedding permission metadata directly into vector payloads, ensuring users only retrieve information authorized for their security clearance.
Real-Time Sync & Change-Data-Capture (CDC)
Automated ingestion pipelines connecting Google Drive, SharePoint, Salesforce, and Postgres databases to keep knowledge bases constantly fresh.
Continuous Evaluation & Groundedness Auditing
Automated regression testing using the RAGAS and TruLens frameworks to monitor faithfulness, answer relevance, and context precision.
Our 5-Stage RAG Implementation Process
From document parsing calibration to real-time synchronization pipelines and telemetry monitoring.
Data Ingestion & Corpus Audit
We analyze your document repositories, file formats, metadata structures, and access policies to determine optimal chunking and embedding strategies.
Chunking & Vector Architecture
We implement semantic chunking, hierarchical parent-child retrieval, and select high-performance embedding models (e.g. OpenAI text-embedding-3, BAAI).
Hybrid Retrieval Pipeline Setup
We configure vector databases with hybrid BM25 search, metadata filtering, cross-encoder re-ranking, and strict citation formatting.
Evaluation Benchmarking (RAGAS)
We build automated test datasets to measure context recall, answer relevance, and groundedness before moving into production.
Production Deployment & Real-Time Sync
We deploy streaming API endpoints, connect continuous synchronization webhooks, and configure OpenTelemetry / LangSmith tracing.
Frequently Asked Questions: RAG Development & Vector Search
Ready to Connect Your LLM to Your Enterprise Data?
Consult with our RAG and vector database architects. We will evaluate your document corpus and design a high-accuracy retrieval blueprint.
Schedule Technical RAG Call →