Turn enterprise knowledge into operational intelligence.

Generic LLMs answer from their pretraining data — they have no access to your SOPs, contracts, CRM notes, or internal systems, and they hallucinate when asked specifics. Enterprise RAG grounds every answer in your real, current knowledge sources, with citations, permission controls, and full audit logs. The LLM becomes a reasoning layer over your trusted data.

IDS implements permission-aware retrieval at the document and chunk level. The RAG pipeline checks the requesting user's identity and group memberships against ACLs from source systems (SharePoint, Confluence, CRM, etc.) before retrieving any chunk. A user only sees answers grounded in documents they were already allowed to access — nothing leaks across permission boundaries.

No. We design RAG architectures based on your data residency and security requirements. Options include private LLM deployments (self-hosted or in your VPC), enterprise contracts with zero-retention guarantees from major model providers, or hybrid setups where retrieval and embeddings stay in your infrastructure and only sanitized prompts go to the model. Most clients land on AWS Bedrock, Azure OpenAI, or fully on-prem depending on their constraints.

Multi-layer defense: every answer cites the source chunks it came from, the system refuses to answer if retrieval confidence is below threshold, evaluation pipelines continuously score answer accuracy against gold-standard Q&A sets, and a monitoring dashboard surfaces hallucination risk, retrieval gaps, and low-confidence answers for review by your knowledge team.

Most enterprises begin with a 4-week AI Audit Sprint that maps your knowledge ecosystem (SharePoint, Confluence, CRM, helpdesk, file shares, etc.), identifies the highest-impact retrieval workflows, and produces an architecture and pilot plan. The 6–10 week First AI Pilot then deploys a production-ready RAG system on the chosen workflow with KPI measurement built in. Enterprise rollouts run over multiple quarters with phased source integration and evaluation gates.

We build a continuous evaluation loop into every production RAG system: a gold-standard Q&A test set scored on every model or index change, sample answer reviews routed to subject-matter experts, alerts on confidence-score drift, and dashboards showing source coverage and freshness. Drift detection happens before users complain, not after.

Yes. Our retrieval pipeline supports multilingual embeddings and hybrid retrieval across Vietnamese, English, and other major business languages. We tune chunk size, normalization, and ranking for the dominant language(s) of your corpus, and route generation through models that perform well in those languages.