AI Agents & Enterprise RAG
Autonomous multi-agent workflows, vector search knowledge bases, and local/hybrid private AI models.
Knowledge is scattered across PDFs, drives and chat — answers depend on who you ask.
A private knowledge layer and controlled agents that answer with citations — from your data, under your permissions.
WHAT YOU GET
Cited answers
Every answer traces back to source documents — no blind trust.
Permission-aware
Retrieval respects who may see what.
Private by design
Cloud, local or hybrid models chosen on privacy needs.
Tool-using agents
Agents that query systems and act — inside approval gates.
Evaluated
Test sets and retrieval scoring before production claims.
Governed
What AI may do, approval rules and full audit trails.
DELIVERY PROCESS
Discover
Knowledge sources, access rules and the decisions AI should support.
Ingest
Parsing, chunking, embeddings and hybrid search indexes.
Ground
Reranking, citations and permission filters tuned on real queries.
Agentize
Controlled agent loops with tools, approvals and guardrails.
Operate
Quality monitoring, feedback loops and model maintenance.
DELIVERABLES
FREQUENT QUESTIONS
Is our data used to train public models?
No. Retrieval keeps your data in your boundary; models only see the retrieved context.
Cloud or local models?
Depends on privacy, latency and cost — we design cloud, local or hybrid accordingly.
How do you stop hallucinations?
Grounding, citations, confidence thresholds and human approval on risky actions.
Can agents act, not just answer?
Yes — inside defined tools and approval gates, with every action logged.
START WITH A CONVERSATION
Tell us your problem in one email — we reply with honest next steps, not a sales deck.
ASK AI ABOUT OP FUCOM
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