Your agents act on what your company knows.
The Collective Intelligence Graph brings your documents, policies, past decisions and expert judgment into one graph, and gives each agent the right part of it at run time. Every answer can point to where it came from.
Can I approve this $1,250 refund?
Drawn from your connected human and agent sources.
Every question draws on your human and agent intelligence, resolved into one governed, auditable answer with the reasoning path shown.
The problem
Most enterprise AI forgets everything the moment it ships.
Your rules don't stick.
Policies sit in PDFs and wikis the agent never reads, so every run starts from general knowledge.
Your experts' judgment never makes it in.
The exceptions your best people approve every week are gone by the next run.
Every team starts from zero.
Context gets rebuilt for every agent and every project, and drifts apart.
How it works
One question. Three kinds of intelligence.
Sent to the graph · just now
Knowledge
Policy 4.2: refunds over $500 need manager approval.
Expert judgment
Approved exception: refunds above the cap are allowed when an outage breached the SLA.
Agent experience
Order record, the outage on this account, and the outcome of similar past runs.
Approve $1,250 under exception 4.2a. Outage confirmed on this account, SLA breached.
Search finds the policy. The graph also knows the exception, who approved it, and why.
How it compounds
Three streams feed the graph.
Connectors
Your systems.
Docs, tickets, CRM records and chat sync in, with the permissions you already set.
Studios
Your experts.
Experts add playbooks, policies and past decisions to a Studio, and review what agents learn. Studios are how judgment gets into the graph.
Refunds above the cap allowed when an outage breached the SLA
Agent runs
Your agents.
Reasoning, tool results and approved corrections are written back after every run, so the next run starts where this one ended.
reasoning · tool results · approved corrections
Collective Intelligence Graph
As the graph learns
Repeat questions are answered from precedent, so agents make fewer frontier model calls and answer faster.
Why a graph
Why search alone isn't enough.
FAQ
Questions about grounding AI agents in your company’s knowledge.
What is a knowledge graph for AI agents?
A knowledge graph for AI agents links the facts, rules and people behind your business, so an agent retrieves not just documents but how they relate: which policy applies, which exception overrides it, and who approved it. The Collective Intelligence Graph adds expert judgment and past agent runs to that picture, and serves the relevant part to each agent at run time.
How is the Collective Intelligence Graph different from RAG?
RAG retrieves passages that look similar to the question. The graph returns connected context: the policy, its approved exceptions, the expert who owns it, and what happened in similar past runs, each with its source. Agents get the rule and the judgment around it, not just matching text.
Which sources can it connect to?
Connectors cover common systems such as Google Drive, Confluence, Notion, Jira, GitHub, Zendesk, Salesforce, Slack and Outlook. Access follows the permissions you already set in those systems. See the Connectors page for the full list.
How do experts add knowledge without engineers?
In AI Studios. Experts upload playbooks, policies and past decisions, and review the learnings agents propose. Approved items go into the graph and reach every agent that needs them, with no code and no redeploy.
How does grounding reduce hallucinations?
Agents answer from your sourced context instead of general knowledge, and each claim in their reasoning can be checked against the source it cites. When nothing in the graph supports a claim, it is flagged for review instead of passed through.
Does our data leave our environment?
No. AISquare runs in your cloud, the graph stays in your environment, and it works with any model you choose. Your graph belongs to you, not to a model vendor.
Does the graph reduce model cost and latency?
Yes. Agents receive only the context relevant to the task, so prompts stay small. When a question matches a case the graph has already resolved, the agent can answer from that precedent instead of making a new frontier model call. Fewer and smaller calls mean lower cost and faster answers.
How does the graph get smarter over time?
Every run writes back what it learned: the reasoning, the tool results and any correction an expert approves. Approved corrections become rules and precedents in the graph, so the next agent that meets the same situation starts with that knowledge instead of asking again. One fix reaches every agent that needs it, which is why accuracy improves and repeat questions get cheaper and faster the more you run.
See how Learnings worksStop re-explaining your business to every agent.
Start your journey with AISquare
Connect your sources, add your experts, and give every agent the same ground truth.