Your agents are smart but operate blind. Give them a live knowledge graph of your organization — queryable via API in real time.
Your AI agents can access documents, databases, and the internet — but they can't access the most important information: how your organization actually works. Who's responsible for what. How decisions are really made. Which processes connect to which teams. This operational context lives in people's heads, not in any system your agents can query. RAG over company documents produces outdated or conflicting answers because the documents don't reflect current reality. Your agents hallucinate on org-specific questions — and employees stop trusting them.
How it works
Capture the organizational reality
Holo conducts 30-minute AI-guided voice interviews with employees across your organization, capturing the real processes, teams, tools, dependencies, and decision chains.
Build a structured knowledge graph
The AI extracts entities and relationships from every conversation, consolidates and deduplicates them, and produces a structured, queryable graph of your organization.
Connect your agents via API
Any agent — HR assistant, IT helpdesk, onboarding guide, ops copilot — can query the graph via a single API endpoint. Zero integration code on the agent side.
Get grounded answers
Every answer your agents give is grounded in actual organizational knowledge, not document fragments. Accuracy on org-specific questions jumps from typical RAG levels to 85%+ on average.
What you get
85%+
Average agent accuracy on organizational queries after connecting to Holo — vs. 40% with RAG alone
1 day
Average time to connect an agent to Holo — vs. 3–6 months of custom context engineering
0
Lines of integration code per agent — just an API call
“We'd spent 6 months building custom context pipelines for our agents. RAG, embeddings, fine-tuning — the works. Agent accuracy on org-specific questions was stuck at 40%. We connected Holo in one afternoon — literally one API endpoint — and accuracy jumped to 85% overnight. Six months of engineering replaced by one afternoon of voice interviews.”
Romain V.
Head of AI, Enterprise SaaS, 1,200 employees
Case study
An enterprise SaaS company had deployed 4 internal AI agents (IT helpdesk, HR assistant, onboarding guide, and project allocator) but all were struggling with organizational questions. RAG over company documents produced outdated or conflicting answers because the documents didn't reflect current reality.
Holo conducted voice interviews with 45 employees across all departments in 1 week. The resulting knowledge graph was connected to all 4 agents via API. Accuracy on organizational queries jumped from 40% to 85% within the first week. User adoption of AI tools doubled within a month. The IT helpdesk agent alone saved an estimated 120 hours/month in ticket handling by correctly routing requests based on real team responsibilities.