Enterprise RAG
Enterprise RAG that answers from your documents - with citations, not hallucinations.
You have thousands of documents - wikis, policies, contracts, technical manuals - and your team wastes hours hunting through them. An enterprise RAG system turns that pile into an AI assistant that answers in plain language and cites the exact source, so people actually trust the answer. I design and build these end to end: production-grade, connected to your systems, with your data staying in your cloud.
What it is
RAG - retrieval-augmented generation - is how you get an AI chatbot to answer from your documents instead of making things up. It retrieves the right passages from your knowledge base first, then generates an answer grounded in them, with citations back to the source.
I build enterprise RAG systems that hold up in production: hybrid search so retrieval is actually accurate, reranking tuned to your content, permission-aware access so people only see what they should, and an evaluation harness so you can measure accuracy over time. Enterprise RAG is my flagship specialty.
It is the engine behind an internal knowledge assistant, a customer-facing support bot grounded in your docs, or a compliance search tool that has to cite chapter and verse - anywhere a wrong answer is unacceptable.
What you get
Built for production, not a demo
Hybrid retrieval, not pure vector
BM25 + dense vectors + reranking tuned to your corpus, so answers are actually relevant - not just semantically close.
Citations on every answer
Each answer links back to the exact document and section. The system says "I don’t know from your documents" rather than inventing one.
Connects to your systems
Standard connectors for SharePoint, Confluence, Google Drive, Notion, S3, and most SQL/NoSQL databases.
Your data stays in your cloud
Self-hosted models in your VPC, enterprise APIs with zero data retention, or hybrid - chosen to fit your compliance posture.
Access control & audit logs
Permission-aware retrieval, role-based access, and audit trails - so the right people see the right documents.
Evaluation harness & monitoring
Ongoing accuracy scoring, monitoring, logging, and runbooks your team can operate - measurable confidence, not blind trust.
How it works
From first call to production
Readiness audit
Two weeks to map your documents, pick the highest-ROI use cases, and recommend an architecture. You get a clear plan before committing to a build.
Pilot build
One use case, one document set, shipped to production in 6-8 weeks with hybrid retrieval, citations, and monitoring.
Scale to platform
Multi-source ingestion, role-based access, agents for multi-step retrieval, and custom integrations - the full internal knowledge platform.
Handover
Your team gets training, runbooks, and everything needed to operate and extend the system without me.
Proof
Real systems, shipped
Live products I designed and built end to end - the clearest signal I can build yours.
NirixAI
A production RAG system: turns 20K+ videos and documents into cited, streaming answers - the full ingestion, retrieval, and generation pipeline at interactive speed.
PiperQL
Natural-language access to structured data - ask in plain English, get the answer and the right chart. The same self-serve pattern enterprises want over their knowledge.
FAQ
Questions clients ask
How do I stop the AI from hallucinating on our content?
Every answer is grounded in retrieved passages and includes a citation to the exact document and section. The system is designed to say "I don’t know based on your documents" rather than fabricate. An evaluation harness ships with it so your team can score accuracy on an ongoing basis - measurable confidence, not blind trust.
Can it connect to SharePoint, Confluence, or Google Drive?
Yes. Standard connectors exist for SharePoint, Confluence, Google Drive, Notion, S3, and most SQL/NoSQL databases. For a pilot we pick one source to keep scope tight; the platform tier adds multi-source ingestion and 3-5 custom integrations.
Does our data go to OpenAI, Anthropic, or any third party?
Only if you want it to. Three deployment models: fully self-hosted open-source models in your VPC, enterprise API tiers with zero-data-retention agreements, or a hybrid. Your raw documents never leave for a model provider without your explicit approval.
How much does an enterprise RAG system cost?
It depends on document volume, compliance needs, and integrations. Rather than a made-up number, I scope it on a strategy call or send a private proposal PDF - most engagements start with a 2-week readiness audit that gives you a phased plan and pricing before any build.
What if we already have a RAG system that isn’t working?
Start with the audit. In 2 weeks I’ll tell you exactly what’s failing - usually chunking, retrieval quality, or missing reranking - and whether it’s worth fixing or rebuilding. About 40% of audits result in "fix it," the rest in "rebuild on a cleaner foundation."
How long until it’s in production?
Audits start within about a week. A pilot build ships to production in 6-8 weeks. A full multi-source platform runs 3-4 months depending on integrations and internal alignment.
Related
Next Step
Ready to scope this?
Book a 60-minute strategy call. No sales pitch - just honest, actionable direction on your project.
Start with a RAG Audit