Turn KnowledgeInto Answers.
Build sophisticated RAG systems that transform your documents, databases, and knowledge bases into intelligent, searchable systems with accurate, contextual responses at enterprise scale.
// Hybrid RAG Pipeline
const pipeline = createRAG({
vectorStore: "pinecone",
embeddings: "text-embedding-3",
reranker: cohere.rerank,
chunks: semanticSplitter,
});
Applications
RAG system use cases.
Purpose-built retrieval systems tailored to your specific knowledge domains and use cases.
Enterprise Knowledge Base
Transform internal documentation, policies, and procedures into an intelligent Q&A system that employees can query naturally.
- Document ingestion & processing
- Natural language queries
- Source attribution
Customer Support Intelligence
Enable support teams with instant access to product information, troubleshooting guides, and customer history.
- Multi-source knowledge integration
- Context-aware responses
- Escalation workflows
Research & Analysis Platform
Intelligent research assistants that analyze large document collections and provide insights across multiple sources.
- Cross-document analysis
- Trend identification
- Citation tracking
Architecture
Advanced RAG techniques.
Cutting-edge techniques for maximum accuracy and performance.
Hybrid Search Implementation
Combine semantic vector search with traditional keyword search for optimal retrieval accuracy across different query types.
Advanced Chunking Strategies
Sophisticated document chunking and preprocessing that preserve context and improve retrieval quality.
Production Vector Databases
High-performance vector databases handling millions of documents with sub-second query response times.
Real-time Knowledge Updates
Systems that automatically detect, process, and index new content to keep your knowledge base current.
Process
From data to production.
A methodical approach to building RAG systems that deliver accurate, reliable answers.
Knowledge Audit & Strategy
Catalog your data sources, document types, and knowledge gaps. Map out retrieval requirements, accuracy targets, and integration points with your existing systems.
Ingestion & Embedding Pipeline
Build robust document processing pipelines with semantic chunking, metadata enrichment, and multi-format support. Select and tune embedding models for your domain.
Retrieval & Generation Tuning
Implement hybrid search with reranking, tune retrieval parameters, and optimize generation prompts. Extensive testing against real queries and edge cases.
Deploy & Evolve
Production deployment with monitoring for retrieval quality, hallucination detection, and user feedback loops. Continuous improvement as your knowledge base grows.
Investment
Start with the audit, scope from there.
Implementation work is scoped after the 2-week audit, not during it. The audit inventories your data sources, sets accuracy targets, and lands on an architecture plan your engineers can defend. One fixed price, one readout, then we scope the build together.
RAG Feasibility Audit
$7.5K fixed
2 weeks
Know whether your data is ready for RAG before you fund the pipeline.
- Data source inventory + quality and coverage review
- Retrieval requirements + accuracy benchmarks
- Architecture plan (vector DB, embeddings, reranker)
- Build-vs-buy recommendation (Glean / Credal / custom)
- 60-min readout with you and the executive sponsor
Audit fee credited toward a build retainer if you move forward within 30 days.
RAG Build Retainer
From $15K/mo
Ongoing · typical 2–3 mo
The audit sized it. This tier ships the pipeline.
- Ingestion pipeline + semantic chunking strategy
- Hybrid search + reranker tuned to your accuracy targets
- Eval harness (accuracy, hallucination, latency)
- Production deploy + ops handoff to your team
Enterprise RAG Platform
$100K+ custom
6–12 months
Multi-source RAG with governance integration and multi-tenant architecture.
- Multi-source ingestion + unified retrieval layer
- Tenant isolation, RBAC, and prompt/decision logging
- Governance integration (data lineage, audit, eval)
- Strategic advisory to the CTO/CAIO/CISO triad
Next Step
Ready to build your RAG system?
Start with the 2-week audit. Before committing to a build, you'll know whether your data is ready for RAG, what accuracy to expect, and what architecture fits.