THE SYSTEM OF RECORD FOR AI DECISIONS

    You can't scale AI agents you can't explain or control.

    AISquare connects agent reasoning, enterprise context, policy, and human judgment into one governed system. Every decision, approval, and correction becomes shared enterprise memory that your people and agents can reuse.

    This is collective intelligence, your people and AI agents continuously learning from each other.

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    Explain whyControl what happensPreserve what’s learned

    Agent Reasoning

    Explained

    Enterprise Context

    Reusable

    Policy

    Policy Approved

    Human Judgment

    Human Corrected
    AI + BI

    Collective Intelligence

    GOVERNED SYSTEM OF RECORD

    Every decision, approval, and correction becomes shared memory that people and agents reuse.

    People

    AI Agents

    Business Outcomes

    0%

    of enterprise GenAI pilots delivered no measurable return.

    MIT Project Nanda, 2025

    0%+

    of agentic AI projects are projected to be canceled by the end of 2027.

    Gartner, 2025

    The trust gap

    AI capability is not the constraint. Enterprise trust is.

    Collective Intelligence

    Every human and every agent should make the others more intelligent.

    Collective intelligence: your agents' reasoning plus your enterprise's knowledge, governed as one system of record. Every interaction adds insight the entire organization can reuse.

    Read the paper: The Future of the Organization with Collective Intelligence
    Agent asks

    Can I approve this $180 refund?

    Draws on your sources
    Business rulesHuman
    Decision rationaleHuman
    ClaudeAgent
    Agent reasoning tracesAgent
    Meeting decisionsHuman

    Drawn from your connected human and agent sources.

    Collective intelligence
    Governed enterprise memory

    Every question draws on your human and agent intelligence, resolved into one governed, auditable answer with the reasoning path shown.

    Governance

    Collective intelligence powers governance.

    Every AI decision runs the same governed loop: understood, controlled, corrected, and remembered. Human judgment and agent reasoning compound into control that gets stronger with every decision.

    The foundation
    Auditability
    Traceability
    Reasoning
    Policy Enforcement
    Observability

    Five pillars every decision stands on, the shared ground your people and agents govern from.

    The closed loop

    One decision. One governed loop.

    01Understand

    Reasoning Graph

    See the claims, evidence, and tools behind every decision. Explain why it happened, debug what broke, and leave a record you can stand behind.

    Plain-language explanationEvidence trailAudit-ready
    Reasoning graph showing claims, evidence, assumptions, and decision details
    02Prevent

    Policy Enforcement

    Enforce policy at decision time, before an action runs, not in a post-run audit. Compliance sets the rules to allow, route, or block without an engineering ticket, and every outcome is logged.

    Decision-level enforcementNo-code policy UIRuntime blocking
    Policy Manager interface showing active policies with allow, route, and block rules
    03Fix

    Correction Loop

    When a decision goes wrong, correct it where it happened. The fix applies in context and carries into future runs, with no redeploy and no bouncing between traces and code.

    Fix in loopNo redeployPersistent corrections
    Human-agent loop showing agent run timeline with apply correction in context panel
    04Remember

    Enterprise Memory

    Every decision, correction, and approval becomes reusable context, grounded in your knowledge and policies and portable across models, frameworks, and workflows.

    Model-independentFramework-agnosticCompounding memory
    Enterprise memory diagram feeding a persistent context layer into the decision loop

    Every correction becomes the next decision's context.

    Observability is table stakes

    Most tools report what happened. AISquare governs what happens next, and gets smarter every time.

    Observability stops at tokens and latency. Evals score outputs. Guardrails block words. Only AISquare explains the decision, enforces the policy at decision time, and remembers the fix, so every human and agent it touches gets sharper.

    01 · Observability

    Shows the trace of what happened.

    Limit

    Reports after the fact. Doesn't tell you why, or stop anything.

    02 · Eval platforms

    Scores the output.

    Limit

    Grades quality, not the cause behind a decision.

    03 · Guardrails

    Block bad tokens.

    Limit

    Filter words in and out. No control over tool use, authority, or downstream action.

    AISquareAISquare

    Explains the decision. Enforces the policy. Remembers the fix.

    Collective intelligence at decision time. Every run smarter.

    ReasoningPolicyEvidenceMemory
    AI Studios

    The collective intelligence platform that drives AI adoption through Studios.

    This is where it comes together. Your team and your agents work side by side in Studios, and everything they create, connect, and govern flows into one reasoning graph.

    AISquare · StudiosPersonal Workspace
    Good morning
    Where your team's knowledge and your agents' reasoning become one intelligence.
    Ask me anything, or pick a starter below…Send
    CreateHuman knowledge in
    ExpertA chat assistant grounded in your sources
    NoteA cited summary you can save and share
    PodcastYour sources as a narrated podcast
    VideoYour sources as a short explainer video
    ConnectAgents and tools in
    Agent sources
    GovernThe governed loop
    PolicyControl what your agents can do
    AuditA signed record of every decision
    Human in the loopReview, correct, approveEvery decision tracked & auditableTailored to your workflowGets smarter every runPortable across models & frameworks

    The only AI you can trust is AI with people at the center.

    Studios are how AISquare powers human-centered AI. Whatever the agent learns, a human reviews. Whatever a human corrects, the agent remembers. So trust compounds with every run.

    Learn more about AI Studios
    CONNECTORS

    AISquare connects to every layer of your stack.

    Models, agent frameworks, data systems, and the tools your teams already use. AISquare plugs into each one and captures the signal it produces, so every reasoning trace, policy check, and human decision becomes shared memory.

    Models

    The reasoning behind every answer, captured as it runs.

    ClaudeClaude
    OpenAIOpenAI
    Azure OpenAIAzure OpenAI
    GeminiGemini
    MistralMistral
    LlamaLlama

    Agent frameworks

    Every agent trace, step by step.

    AgnoAgno
    LangChainLangChain
    Google ADK
    Vercel AI SDK
    CodexCodex
    CrewAICrewAI
    LlamaIndexLlamaIndex

    Data & knowledge

    The context your decisions depend on.

    Google Drive
    Google Docs
    Google Sheets
    PDFs
    Outlook
    OneDriveOneDrive
    Notion

    Tools & systems

    The decisions and human actions that close the loop.

    Slack
    Salesforce
    Jira
    GitHub
    n8n
    Zoom
    SNServiceNow
    Zendesk

    Extend with any tool

    Connect any agent through our SDK, an MCP server, or GitHub. Bring your own tools, on top of a growing library of native connectors.

    COMPOUNDING RETURNS

    Collective intelligence compounds. Trust goes up, cost goes down.

    Every correction a human makes and every fact your team verifies is remembered and reused. The same collective intelligence that makes your agents more trusted also makes every run cost less.

    THE AGENT-HUMAN CO-LEARNING LOOP
    Learning loop
    Agent run
    Policy-enforced decisions
    Business context
    Ontologies, policies
    Human feedback
    Corrections, expertise
    Reasoning graph
    DecisionsReasoningPoliciesHuman feedbackBusiness contextDomain expertise

    One loop, two compounding outcomes.

    TRUST COMPOUNDS

    The more it runs, the more you trust it.

    AISquareWithout CI
    Day 1Month 12
    COST DROPS

    Your token bill drops as the graph grows.

    you save~40%by month 12
    TraditionalOn AISquare
    cost per agent run
    Day 1Month 12
    CACHED CONTEXT

    Verified once. Never re-fed.

    Human-validated context lives in the graph. Your agent does not re-fetch it or re-validate it, so it never burns tokens on context it already had.

    SMART ROUTING

    Expensive models, only when needed.

    Simple lookups hit fast, cheap models. Top-tier models get called only when the graph cannot answer on its own, so average cost per query drops.

    REPLAY VS RERUN

    Decisions reused, not regenerated.

    Replay a past decision in under a second for near-zero tokens, instead of rerunning the whole chain through an LLM every time.

    SHARED CONTEXT

    One ingestion, many agents.

    Pay once to ingest a document. Every Studio, agent, and teammate reads from the same graph. No redundant retrieval, no duplicate spend.

    AISQUARE IN ACTION

    See it govern the decisions that matter.

    The same reasoning, policy, and memory across every high-stakes workflow your agents run, from code to contracts to customer refunds.

    CUSTOMER SUPPORT

    Know which policy the agent used.

    A support agent approves a refund that should have been reviewed.

    OUTCOME

    Attach the policy, the evidence, the customer context, and the escalation path.

    AISquare customer support decision trace showing the refund issue and policy invoked
    FOR DEVELOPERS

    Connect AISquare to your agent stack in minutes.

    Works with the agents you already built. Add reasoning graphs, policy checks, and decision records without retraining or redeploying.

    Drop-in Python SDK
    pip install, wrap your agent, and you are capturing.
    MCP or proxy servers
    Bring your own tools over MCP, or connect through a proxy for Claude Code, OpenAI, Gemini, and more.
    No retrain, no redeploy
    Instrument the agents you already run in production.
    Learn more for developers
    agent.py
    5-minute setup
    # pip install "aisquare[explainability]"
    import aisquare.explainability as sdk
    from aisquare import GovernedAgent
     
    # Reads EXPLAINABILITY_API_KEY and
    # EXPLAINABILITY_GATEWAY_URL from the env.
    sdk.init_from_env()
     
    # Agno and LangChain tools are intercepted
    # automatically; custom agents work too.
    agent = GovernedAgent.from_agno(
    my_agent,
    rule_book="your-rule-book",
    enforce=True,
    )
    agent.print_response("...")
     
    # Short-lived scripts: flush before exit.
    sdk.flush()
    Every run now includes a reasoning graph, policy check, and decision record.
    Your agents + AISquare = SuperAgents
    TRUSTED · GOVERNED · AUDITABLE · DEFENSIBLE · EXPLAINABLE · SCALABLE
    For Enterprise Teams

    From AI pilot to governed production workflow.

    AISquare works with your team to turn high-value agent workflows into a repeatable system for reasoning, policy, review, and audit.

    Build with Partners

    Bring AISquare into your existing consulting or implementation motion.

    Build with AISquare

    Work with our forward-deployed team on your first governed AI workflow.

    Build Internally

    Use AISquare docs, SDKs, and APIs with your own engineering team.

    01/

    Discover

    • Map AI decisions
    • Identify risk
    • Define human review
    02/

    Build

    • Reasoning graphs
    • Policy checks
    • Approvals
    • Decision records
    03/

    Deploy

    • Launch with workflow team
    • Monitor real decisions
    • Close the loop
    04/

    Scale

    • Repeatable playbook
    • Across agents
    • Across models
    • Across teams
    Talk to the Forward Deployed team

    ReadytoshipAIyoucantrustinproduction?

    AI moves fast. Human judgment makes it trustworthy. AISquare connects agents, context, policy, and people so every decision can be understood, guided, and defended.

    Request a pilot