QORIS

Treat memory like code.Pull. Propose. Review. Merge.

Qoris Memory gives AI workers a GitHub-like layer for long-term context — versioned, governed, reviewable, and safe to roll back. Workers can learn from work without rewriting truth.

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Fast recall finds context. Canonical memory stores what the business accepts as true.

AI workers cannot run long-term work on short-term context.

AI workers need to remember customers, workflows, decisions, preferences, exceptions, policies, and prior outcomes. But most memory systems blur the line between recall and truth — they retrieve old conversations, similar tickets, or raw notes and let the agent treat all of it as reliable memory.

That works for a demo. It breaks in production. A worker that learned the wrong customer preference from one bad ticket will keep acting on it forever. There’s no review, no merge, no rollback — just a vector database that quietly drifts from reality.

Not every past interaction should become trusted memory.

Knowledge is what the company knows. Memory is what the system remembers from doing the work.

Before diving into how Qoris Memory works, it’s worth separating two things that often get confused. Knowledge is reference material — SOPs, policies, product docs, contracts — static, approved, sourced. Memory is operational continuity — what workers learn by doing work, evolving across customer interactions, workflow runs, and decisions over time.

Knowledge

Approved source material workers retrieve from.

  • SOPs
  • Policies
  • Product documentation
  • Contracts
  • Help center articles
  • Sales playbooks
  • Pricing rules

Memory

Evolving context generated from work performed.

  • Customer preferences
  • Prior decisions
  • Workflow history
  • Account summaries
  • Support resolutions
  • Renewal risks
  • Agent learnings

Knowledge is reference. Memory is continuity. Qoris workers use both. This page is about memory.

Fast recall for speed. Canonical memory for trust.

Qoris Memory combines two layers. Fast Recall helps workers find relevant context quickly — similar tickets, prior conversations, related workflow runs. Canonical Memory stores durable facts, customer preferences, workflow rules, SOPs, decisions, and approved business context. The two are linked but distinct: fast recall finds, canonical confirms.

Fast recall is read-mostly. Canonical memory is the part that requires governance.

Fast Recall Memory

Semantic and vector retrieval for recent context, similar cases, conversations, tickets, runs, and workflow traces.

Best for: Finding relevant context quickly.

  • Similar support tickets
  • Recent customer conversations
  • Prior sales follow-ups
  • Related workflow runs
  • Previous tool outputs

Canonical Memory

Verified long-term memory for trusted facts, customer preferences, workflow rules, SOPs, decisions, and durable business context.

Best for: Storing what the business accepts as true.

  • Approved customer preferences
  • Account summaries
  • Policy interpretations
  • Workflow rules
  • Prior decisions
  • Verified SOP updates

Give each workflow the right memory scope.

Qoris Memory is organized into scoped repositories. Each repository defines what it stores, who can access it, what requires approval, and how changes are reviewed. A Customer Memory repository doesn’t leak into Vendor Memory. Compliance Decisions stay scoped to compliance reviewers. Sales Context is available to sales workers but not customer-facing ones.

  • Customer Memory

    Customer preferences, account history, relationship context, and approved customer facts.

  • Sales Context

    Lead notes, buying signals, meeting history, objections, and follow-up preferences.

  • Support History

    Past issues, resolutions, escalation patterns, and support preferences.

  • Compliance Decisions

    Prior reviews, exceptions, blocked actions, and approved interpretations.

  • Vendor Memory

    Vendor history, document reviews, risk notes, exceptions, and procurement context.

  • Workflow Patterns

    Reusable operational learnings, SOP patterns, automation candidates, and task routing logic.

Workers propose memory. Teams approve what becomes truth.

Qoris Memory is designed around a GitHub-like model for long-term context. When a worker learns something durable, it doesn’t overwrite canonical memory. It proposes an update — including the summary, source, scope, risk level, confidence, and reason. Knox checks the proposal. A human or policy reviews it. Approved updates merge into canonical memory. Bad updates can be rolled back with full history.

Workers learn. Teams stay in control. Every change is traceable.

AI workers should be able to learn. They should not be able to rewrite truth without governance.

  1. Pull

    KNOX · scope check

    Worker retrieves memory scoped to the task, customer, room, or workflow.

  2. Use

    Worker applies the context in its workflow execution.

  3. Propose

    Worker suggests a durable memory update — not a direct write.

  4. Knox Check

    KNOX · policy applied

    Knox evaluates sensitivity, scope, permissions, and approval requirement.

  5. Review

    Human, policy, or approval workflow checks the proposed change.

  6. Merge

    Approved memory becomes canonical long-term memory.

  7. Audit

    AUDIT · evt_8f3a...

    Every change recorded — pull, propose, check, review, merge, or rollback.

Rollback (if needed)

Incorrect memory can be reverted with full traceable history. Original state restored. Rollback itself is audited.

Memory Proposal · Customer Memory · awaiting review

- Customer prefers refund on damaged items

+ Customer prefers replacement shipment when items arrive damaged

Source: Support Resolution Worker · Ticket #4821

Confidence: 0.94 · Risk: low · Scope: customer_id_8821

Knox: scope check passed · approval required

Reviewer: account owner

Memory follows the work from first contact to long-term relationship.

Qoris workers use memory across the full customer lifecycle. A Customer Intake Worker captures the first request. A Sales Follow-Up Worker remembers the buying journey. A Customer Success Worker tracks goals and risks. A Support Resolution Worker learns preferences. A Renewal Retention Worker recalls renewal history. Each worker retrieves context and proposes updates without losing governance.

Customers using canonical memory across renewal workflows have seen meaningful pipeline impact from prior-context-aware outreach.

  1. Customer Intake Worker

    Captures the first request.

  2. Sales Follow-Up Worker

    Remembers the buying journey.

  3. Customer Success Worker

    Tracks goals and risks over time.

  4. Support Resolution Worker

    Learns support preferences.

  5. Renewal Retention Worker

    Reviews account history.

Canonical Memory

Durable relationship context shared across all workers

Bring governed memory to the agents you already use.

Qoris Memory isn’t locked to Qoris Workers. Teams running agents on LangChain, CrewAI, AutoGen, Claude, or custom runtimes can connect Qoris Memory over MCP — without rebuilding the stack.

Same two-layer model. Same proposal/review/merge flow. Same Knox checks. Same audit. The agent stays where it is. Memory travels to it.

Your stack → MCP → Governed memory.

  • LangChain
  • CrewAI
  • AutoGen
MCP
Qoris Memory
  • Scoped access tokens
  • Repository-level permissions
  • Protected memory enforcement
  • Memory proposals from external agents
  • Knox checks on every read and write
  • Full audit trail

Long-running AI workers need governed memory, not just bigger context windows.

Basic vector memoryLong context windowsChat historyQoris Memory
Separates recall from verified truth
Supports propose-review-merge
Can be rolled back with audit
Governed by policy at read and write

The future of AI work isn’t remembering more. It’s remembering correctly.

Give AI workers memory you can trust.

Qoris Memory gives workers fast recall, verified long-term context, governed updates, and full auditability — whether they run inside Qoris or on the stack you already use.

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MCP-agnostic · Knox-governed · Patent pending U.S. 63/907,730