RAG as a Service
Fully managed Retrieval Augmented Generation solutions. Build intelligent Q&A systems on your documents with 90%+ accuracy. 4-8 week deployment, no ML expertise required.
Answer Accuracy
Achieve 90%+ accuracy on domain-specific questions with source citations.
Fast Deployment
From kickoff to production in 4-8 weeks with our managed service.
Cost Savings
Reduce support and research time by 70-90% with instant answers.
What's Included
Complete RAG Pipeline
- ✓ Document ingestion (PDF, Word, Excel, web pages, databases)
- ✓ Intelligent chunking and preprocessing
- ✓ Vector embeddings with state-of-the-art models
- ✓ Vector database setup and optimization
- ✓ Hybrid search (semantic + keyword)
- ✓ LLM integration (GPT-4, Claude, or custom)
Enterprise Features
- ✓ Multi-tenancy and access control
- ✓ Source citation and provenance tracking
- ✓ Confidence scoring for answers
- ✓ Conversation memory and context
- ✓ Analytics dashboard (queries, accuracy, usage)
- ✓ Continuous learning and improvement
Deployment Options
- ✓ Web interface for end users
- ✓ REST API for custom integrations
- ✓ Slack, Teams, Discord bots
- ✓ Chrome extension for contextual help
- ✓ Embed widget for websites
Managed Services
- ✓ Infrastructure management (AWS/Azure/GCP)
- ✓ Model monitoring and retraining
- ✓ Performance optimization
- ✓ Security and compliance (SOC 2, HIPAA)
- ✓ 99.9% uptime SLA
- ✓ 24/7 support
Use Cases
Internal Knowledge Assistant
- • Answer employee questions from policies, SOPs
- • 70-90% reduction in internal support tickets
- • 24/7 availability
Customer Support Automation
- • Answer product questions from documentation
- • 60-80% automation rate
- • Instant, accurate responses with sources
Legal/Compliance Research
- • Query contracts, regulations, case law
- • 80-95% time savings on research
- • Citation tracking and audit trails
Technical Documentation
- • Developer Q&A from code documentation
- • Onboarding acceleration (50% faster)
- • Reduce expert interruptions by 60%
Pricing
Starter
- ✓ Up to 10K documents
- ✓ 5K queries/month
- ✓ Web interface + API
- ✓ Basic analytics
- ✓ 4-6 weeks setup
Professional
- ✓ Up to 100K documents
- ✓ 50K queries/month
- ✓ Multi-channel deployment
- ✓ Advanced analytics
- ✓ Custom integrations
- ✓ 6-8 weeks setup
Enterprise
- ✓ Unlimited documents
- ✓ Unlimited queries
- ✓ Multi-tenancy
- ✓ SOC 2/HIPAA
- ✓ Dedicated support
- ✓ Custom SLA
Case Study: SaaS Company
Challenge: 15K support tickets/month, 5K pages of documentation
Solution: RAG-powered chatbot with documentation + previous tickets
Results:
- • Answer accuracy: 93%
- • Automation rate: 68%
- • Response time: 8 hours → 30 seconds
- • CSAT: 4.2 → 4.7/5
Business Impact:
- • Investment: $51K
- • Annual savings: $217K
- • ROI: 429% first year
- • Payback: 2.8 months
Ready to Build Your RAG System?
Get a free RAG assessment and see how we can help you build intelligent Q&A on your documents.
Schedule Free Consultation →Frequently Asked Questions
What is RAG and when should I use it instead of fine-tuning?
RAG (retrieval-augmented generation) injects relevant context into the prompt at query time. Use RAG when your data changes often, when you need source citations, or when the knowledge is too large to fit in a fine-tuned model. Use fine-tuning when you need to change behavior, tone, or output format.
Which vector database do you recommend for production RAG?
Pinecone for fully managed, Qdrant or Weaviate for self-hosted, pgvector if you want to keep everything in Postgres. We pick based on your latency budget, dataset size, and existing infrastructure.
How do you measure RAG quality?
We track retrieval recall@k, answer faithfulness (whether the answer is grounded in retrieved context), answer relevance, and end-to-end task success. Each of these gets a regression suite that runs on every change.