LLM Integration & Private RAG Pipelines
Ground Claude & GPT into your private data with verified accuracy.
Engineering Approach & Outcomes
Standard RAG implementations frequently fail in production due to poor document parsing, noisy chunking, and naive similarity searches that retrieve irrelevant context. This leads to hallucinations, excessive token costs, and user distrust.
Solid Sphere builds enterprise-grade RAG systems featuring multi-stage retrieval pipelines: hybrid dense vector search plus sparse BM25 keyword matching, cross-encoder reranking, and contextual query rewriting.
We safeguard your proprietary data by integrating local vector databases (pgvector), ensuring no client training on public AI models and adhering to enterprise compliance standards.
Key Architecture Deliverables
Hybrid Search & Reranking Engine
Combination of dense embeddings and BM25 full-text search with Cohere / BGE rerankers for pinpoint retrieval.
Document Parsing & Chunking Workers
High-accuracy extraction from complex multi-column PDFs, tables, spreadsheets, and internal wiki systems.
Semantic Cache & Rate Optimization
Redis semantic similarity cache cutting recurrent query latency by ~60% and reducing API token expenditure.
Citation & Verification Layer
Direct deep-linking from AI answers to source paragraphs and page numbers, enabling instant human verification.
Production Tech Stack
Telemetry Intelligence
Automated anomaly detection across high-volume synthetic scan logs.
Fixed-Scope Sprints
We lock in exact acceptance criteria, milestone demos every 10 days, and full IP handover. Senior-led with zero junior hand-offs.
Frequently Asked Questions
Straightforward answers about our LLM Integration & RAG engagements.
Will our internal documents be used to train public AI models?▼
No. We utilize zero-data-retention enterprise API endpoints or self-hosted VPC model instances. Your data is never shared or trained upon.
How do you handle tables and complex financial PDFs?▼
We use vision-augmented parsers and structural markdown convertors that preserve table rows, headers, and cell relationships prior to chunking.
What latency should we expect for RAG queries?▼
With optimized vector indices (HNSW) and streaming token output, users receive initial tokens in under 600ms.