A general-purpose AI assistant helps a person draft and answer; Threada governs the work itself — tracked WorkItems, cited evidence, approvals, and audited actions across your systems.
ChatGPT Enterprise gives employees a secure, general-purpose AI assistant for chatting, drafting, and analysis, with admin controls and a no-training-on-your-data commitment. Threada is a governed work platform: it turns requests arriving by email, chat, document, form, or API into structured WorkItems, answers them with retrieval-grounded cited evidence, and executes approval-gated actions across your business systems — every step lifecycle-managed and auditable. The two are often complementary: many teams give people a general assistant and run their accountable operational work on Threada.
| Capability | Threada | Alternative approach |
|---|---|---|
| From answer to tracked work | Every request becomes a typed WorkItem with status, owner, SLA timers, and an outcome — work you can route, govern, and measure. | Optimized for individual chats and drafts; there is no shared WorkItem queue, routing, or outcome taxonomy for operational work. |
| Grounding and citations | Retrieval-grounded answers over your approved sources, with cited URLs and snippets and an explicit no-answer fallback below the relevance threshold. | Answers draw on model knowledge plus connected files or browsing; cited, source-linked grounding per answer varies by setup and is not guaranteed. |
| Taking action in your systems | Governed actions create, update, comment, notify, or schedule in connected systems behind approval gates, with idempotency and audited execution records. | Focused on generating text and analysis for a person to act on; executing governed changes across your business systems is not the core model. |
| Approvals, governance, and audit | Per-request decision steps, approval gates, action allowlists, and a time-stamped audit trail for every answer, decision, and action. | Provides admin and workspace controls, but not per-request approval gates or an action-level audit trail for operational outcomes. |
| Multi-tenant control and policy | Tenant isolation, role and capability scoping, and versioned policy overlays from tenant down to channel. | Workspace and member roles govern access to the assistant; per-tenant operational policy overlays are outside its scope. |
| Model choice and portability | Works across model providers (including OpenAI and others) and can fail over between them, so you are not locked to one vendor's models. | Runs on the vendor's own models; the assistant is tied to a single model family by design. |
Use these samples to inspect the operational records behind the comparison instead of evaluating the approaches from claims alone.
A minimal cited-evidence bundle with source URLs, snippets, retrieval trace, and fallback reason.
{
"citations": [
{
"url": "https://docs.example.com/security/exampleco-scope",
"title": "ExampleCo security scope note (illustrative)"
}
],
"retrieval_trace_id": "rt_8c2147f0",
"fallback_reason": ""
}A simple fixture-driven rubric for testing extraction, grounding, approval routing, action safety, and receipt completeness before release.
gate_id,gate_name,expected_behavior,pass_threshold
extract-001,Extract required fields,"required fields are present",0.95
ground-001,Citation grounding,"answer cites current sources",0.98
audit-001,Receipt completeness,"event links actor, WorkItem, evidence, and policy",1.00These are fair, general characteristics of the approach, not claims about any specific product. Choose the path that matches your governance, integration, and accountability needs.