The collective intelligence platform where humans and agents reason together.
Your team's knowledge plus your agents' reasoning, on one graph that remembers. The longer you run, the more it knows, and the less it costs to run.
Four things break in every AI rollout. You've seen them all.
Building agents is the easy part. Keeping them defensible, keeping their context alive, and keeping the bill from spiraling, that's the hard part.
Knowledge cliffs every quarter
Sarah leaves. Her playbook leaves with her. Six months later, your team rebuilds the same workflow from scratch, because nobody could see what she'd already built.
Agents you can't defend
Your agent flagged a tier-1 supplier. Your CFO asks why. You start a reconstruction. The audit trail you needed isn't there.
Token spend that compounds wrong
Your AI bill triples this quarter. Half of every prompt is context you already fed in last week. The agent doesn't remember. You're paying twice for the same answer.
Model upgrades reset everything
You switch from Sonnet to the next-best model. The agent forgets your team's standards. Trust resets to zero.
Collective intelligence is when…
Humans contribute knowledge. Agents contribute reasoning. Both write to one engine. Here's what changes.
Your second project starts where your first one ended.
Studios share context across the engine. Knowledge built for finance feeds your compliance studio without you re-uploading anything. Two projects, one substrate.
The correction your teammate made last week is structure your agent respects today.
Feedback becomes a graph edge. Mistakes get fixed once and stay fixed. No fixing the same bug three times.
Sarah leaves. Her playbook stays.
Institutional knowledge lives in the engine, not someone's head.
Auditor asks why. The trail is already there.
Decisions, evidence, policy versions, refusals — all queryable months later.
Models change. Your context only deepens.
Three model generations come and go. The engine is the moat.
AI Studio's Persistent Reasoning Graph
AI Studio organizes around actions your team and your agents perform inside the workspace. Each one is a node, an edge, or a query against your graph.
01. Connect
Wire your sources, docs, and agents into the graph on day one — no manual modeling, no warehouse rebuild, no waiting on ETL.
Sources as Nodes
Turn docs, PDFs, Notion pages, Confluence wikis, Salesforce and SaaS systems into structured, queryable context with ownership intact.
Conversations as Context
Slack threads, support tickets, meeting transcripts, and voice memos are parsed into queryable context the moment they're added. No manual setup or prior documentation needed.
Agents on the Graph
Bring your own agents over MCP or the SDK. They read and write to the same nodes your team does, with full permissions and audit.
Your token bill drops as the graph grows.
Most AI bills compound the wrong way: every new project costs more than the last. AISquare flips that. The longer your team uses the graph, the cheaper each agent run becomes.
Verified once. Never re-fed.
Human-validated context lives in the graph. Your agent doesn't re-fetch it. Doesn't re-validate it. Doesn't burn tokens on context it already had.
Expensive models, only when needed.
Simple lookups hit fast, cheap models. Sonnet and Opus get called only when the graph can't answer alone. Average cost per query drops sharply.
Decisions reused, not regenerated.
Replay a past decision in under a second, for nearly zero tokens. Compare to rerunning the same chain through an LLM every time. Same answer, fraction of the cost.
One ingestion, many agents.
Pay once to ingest a document. Every Studio, every agent, every teammate that needs it reads from the same graph. No redundant retrieval, no duplicate spend.
One source of knowledge.
Multiple ways to experience it.
Every Studio you build becomes five surfaces your audience can use. Same engine underneath, five experiences on top, all published from one place.
A conversational expert shaped by your thinking.
Drop a chat into your portal, your team's Slack, or a standalone link. Every answer cites the nodes it used. No black-box replies.
When does a supplier dispute trigger an audit?
A quiz that teaches your framework.
Guided multi-step questions that walk your audience through your thinking.
Q3 Supplier Risk Brief
A structured brief people can revisit.
Clean, citable explanations. Long enough to teach, short enough to scan.
Audio episodes from your ideas.
Auto-generated, in your voice. Listenable on the commute, the gym, the walk.
A visual walkthrough of how it works.
Chaptered explainers your audience can scrub, share, and embed.
AI can summarize your content.
It can't capture the way you think.
Works with the tools you already use.
Reach AI Studio through the UI, the SDK, or the API. Bring your own context through Drive, Notion, or any MCP-compatible source. Notify your team where they already work.
SDK + API
Full SDK and REST API access for builders and automations.
Custom tools (MCP)
Connect any Model Context Protocol server. Bring your own tools.
Web search
Up-to-date data in chat, with sources captured in the graph.
Slack
Activity notifications and shared channels for team workspaces.
Storage
RoadmapKeep knowledge bases in sync with your source-of-truth docs across Drive, Dropbox, and OneDrive.
Productivity
RoadmapNotify your team, sync pages, and pipe agent-flagged exceptions into your existing workflow tools.
Start building the graph that pays you back and builds trust
The graph you build now compounds in capability and in cost savings.