Advanced AIClient result
Technology leader: RAG and MCP at enterprise scale
A global technology company grounded its AI in its own knowledge. Answer accuracy rose from 65% to above 95%, across five orchestrated models.
The client
A global technology company with 25,000+ employees across software development, cloud services, and enterprise solutions. Its knowledge estate spans technical documentation, code repositories, and support history for multiple product lines.
90%Accuracy gain
5Models integrated
60%Faster responses
The challenge
- ▸Generic answers: off-the-shelf LLMs had no company context and it showed
- ▸Data silos: knowledge sat fragmented across 15+ systems
- ▸No model governance: no central control over deployment or monitoring
- ▸Security constraints: sensitive data could not leave the enterprise boundary
- ▸Integration cost: every AI use case meant custom plumbing
- ▸No live context: AI could not read current ServiceNow data
Why it mattered
The company needed AI that knew its products, processes, and customers, without loosening security or governance.
How we ran it
Phase 1: architecture and strategy, 4 weeks
- ▸AI initiative and data landscape assessment
- ▸Enterprise RAG architecture, security first
- ▸Vector database and embedding model selection
- ▸Governance framework and first use cases
Phase 2: RAG foundation, 8 weeks
- ▸Vector database infrastructure deployed
- ▸Ingestion pipelines from 15+ source systems
- ▸Semantic chunking and embedding strategy
- ▸Hybrid retrieval: vector plus keyword
Phase 3: MCP integration, 10 weeks
- ▸Model Context Protocol connected to ServiceNow
- ▸Five models integrated with intelligent routing by query type
- ▸Live context injection from CMDB and knowledge base
- ▸Prompt templates and guardrails per use case
Phase 4: AI Control Tower, 6 weeks
- ▸Central governance and monitoring platform
- ▸Accuracy, latency, and usage tracking
- ▸Automated QA with human-in-the-loop validation
- ▸Audit trails and compliance reporting
What we delivered
Enterprise RAG
- ▸Unified knowledge: docs, code, wikis, and tickets behind one retrieval layer
- ▸Hybrid search: semantic understanding with keyword precision
- ▸Grounded answers: responses cite actual company knowledge
- ▸Live sync: continuous updates keep the AI current
- ▸Boundary security: data never leaves the enterprise, access is role-based
MCP layer
- ▸Multi-model orchestration with routing to the best model per query
- ▸Direct access to CMDB, incidents, changes, and knowledge
- ▸Dynamic context injection into prompts
- ▸One standard interface for every AI interaction
“”
This is AI that understands our business. RAG plus MCP took it from novelty to strategic asset.
Results
AI quality
- ▸Answer accuracy up from 65% to above 95%
- ▸60% faster responses through optimized retrieval
- ▸85% fewer hallucinations
- ▸99.9% uptime for AI services
Developers
- ▸40% less time hunting for technical information
- ▸50% faster onboarding for new engineers
- ▸70% better code quality via AI-assisted review
- ▸30% of support inquiries shifted left to self-service
Customer support
- ▸80% of tier-1 queries handled by AI
- ▸55% faster average resolution
- ▸4.8/5 satisfaction with AI-powered support
- ▸24/7 multilingual coverage without added staffing
Strategic
- ▸$3.2M annual savings from efficiency gains
- ▸A platform now carrying 50+ AI use cases
- ▸Less vendor lock-in through the standard MCP interface
Under the hood
Distributed vector store holding 10M+ embeddings at sub-100ms retrieval. Five models routed automatically by query classification, with fallback for availability.
Stack
RAGMCPVector DatabaseAI Control TowerLLM IntegrationServiceNow AISemantic SearchModel Orchestration
Ground your AI in your own knowledge
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