AI Implementation
GenAI Implementation: RAG pipelines, knowledge layers, and LLM fine-tuning — built for your data.
Generic AI gives generic answers. We build RAG pipelines with Jina Embeddings v3, knowledge layers on Neo4j, and fine-tuned LLMs that understand YOUR domain.
Capabilities
What makes our approach different
We combine semantic chunking with knowledge graphs and multi-model orchestration. Your data stays protected while accuracy climbs.
Semantic chunking with Jina v3
Jina Embeddings v3 breaks your documents into meaningful chunks that preserve context.
Knowledge graphs on Neo4j
Relationships between concepts get mapped and queried, so your LLM understands connections humans see.
Multi-LLM orchestration and compliance
Route queries to the right model while maintaining PDPA compliance and human validation at every step.
PDPA-Compliant Data Handling
Your data never leaves your control. We build RAG systems with PDPA-compliant data pipelines — on-premise or private cloud — with encryption, access controls, and audit trails that satisfy your DPO and your regulators.
Human-in-the-Loop Validation
Critical decisions get human oversight. We build validation workflows where subject-matter experts review, correct, and approve AI outputs — improving accuracy while building a feedback loop that makes the system smarter over time.
Production RAG Deployment
We don't leave you with a prototype. Our RAG systems deploy to production with load balancing, caching, monitoring, and SLA-backed uptime — ready for enterprise-scale traffic from day one.
"Our internal knowledge base was giving wrong answers 30% of the time. EIS rebuilt it with RAG and Neo4j knowledge graphs. Accuracy hit 97% within the first month, and our support team's resolution time dropped by half."
PR
Priya Ramanathan
VP of Digital Operations, Regional Insurance Group