The Stateless LLM Bottleneck
Standard LLM applications treat every meeting as an isolated island. You upload a 45-minute audio transcript, send a prompt like "Extract all action items", and receive an ephemeral JSON array.
The fundamental flaw? Software engineering is an ongoing, asynchronous graph, not a series of disconnected episodes.
When an engineer says in Monday's standup:
*"I'm waiting on Mark's pull request before I can deploy the telemetry pipeline."*
And on Thursday says:
*"It's merged and live in US-East."*
A single-session LLM sees two unrelated fragments. It cannot know that the Thursday statement fulfills the dependency stated on Monday.
Single-Session AI:
[Call 1] ──► Extract Items ──► Dump Text (Context forgotten)
[Call 2] ──► Extract Items ──► Dump Text (No link to Call 1)
Rapto Cross-Meeting Memory:
[Call 1] ──► Commitment Node #104 [PENDING] ──┐
├──► State Transition: [FULFILLED]
[Call 2] ──► "It's merged" ──► Graph Resolver ─┘Architecture: The Temporal Entity Graph (TEG)
Rapto solves this with a Temporal Entity Graph (TEG). Every meeting contributes nodes and edges to an evolving project state:
graph TD
User((Engineer: Alice)) -->|Pledged in Mtg #1| C1[Commitment #104: Optimize DB queries]
C1 -->|Target Date| D[2026-08-20]
C1 -->|Linked Tool| L[Linear Issue: ENG-492]
User -->|Spoke in Mtg #2| C2[Utterance: 'Database queries latency down 40%']
C2 -.->|Resolved via Graph Matching| C1Pronoun & Ambiguity Resolution in Developer Dialogue
Developers rarely speak in clean formal specifications. They say "I pushed it", "We scrapped that approach", or "Sarah took over the migration".
To resolve these ambiguities accurately, Rapto executes a three-stage filter:
AUTH-102, endpoint names like /v1/checkout).State Machine Implementation & Graph Nodes
Here is the deterministic TypeScript schema powering the commitment node resolver:
interface TemporalCommitmentNode {
id: string;
tenantId: string;
creatorUserId: string;
assigneeUserId: string;
verbalStatement: string;
normalizedAction: string;
confidence: number;
originMeetingId: string;
originTimestamp: string;
targetDeadline: string | null;
status: "PENDING" | "IN_PROGRESS" | "BLOCKED" | "FULFILLED" | "SUPERSEDED";
externalIntegrations: {
linearIssueId?: string;
jiraTicketKey?: string;
githubPrNumber?: number;
};
resolutionProof?: {
resolvedMeetingId?: string;
resolvedUtterance?: string;
gitEventSha?: string;
timestamp: string;
};
}By decoupling deterministic graph state from statistical LLM reasoning, the system prevents hallucinations while maintaining deep context across months of collaboration.
Zero-Data Retention Tenant Sharding
Enterprise security demands that cross-meeting memory does not compromise confidentiality:
