Tealfabric
AI

Vendor intake assistant

Controlled · Playbook: vendor-intake-triage

Ask about vendor intake, approvals, or missing evidence…Send

Simulated conversation — connections run only after human approval

Controlled AI on your data

When judgment matters, AI proposes based on data it is allowed to see—then pauses for human approval before connections run.

See ops exception workflow on a live demo.

Platform

AI and agents — inside your platform, not beside it

When exceptions need AI on live operational data—with human approval before anything runs—Trace AI works inside your workflows, not next to them. Team playbooks, controlled connection runs, and Datapools on one platform.

Adoption context

You need this when
Exceptions need triage on live platform data—with human approval before connections run, not a chat tool beside email.
Often bundled with
Governance for live workflows on one platform.
Not required if
Your first workflow is straightforward intake or sync with no judgment steps—add controlled AI when exceptions need propose-and-approve.

Trace AI is not a disconnected chat tool. It is controlled operational intelligence inside the platform your business operations team already runs.

Most teams feel pressure to add AI on top of email, spreadsheets, and scattered connections—but chat tools that cannot see workflows, Datapools, or controlled connections create more risk than value.

Tealfabric AI loads team playbooks, works on allowed operational data, proposes actions, and pauses for human approval before anything runs under your rules.

  • Started from chat, WebApp, workflow, schedule, or event—the same AI everywhere
  • Automatic model routing so ops teams are not tied to a single AI release cycle
  • Query and update allowed Datapool and connection data without side copies
  • Skills playbooks your operations team publishes and controls
  • Human approval before risky connection runs
  • Configurable tool rules, safe test profiles, and history on every action

Entry points

One AI runtime. Five ways to start the same controlled workflow.

One approach across channels—not a separate chat tool for each surface. The playbook, safety rules, and history stay the same whether the session began in chat or from a workflow event.

  1. You: What is blocking vendor intake?
    AI: 3 records in pending_approval…

    01. Human chat

    Ops leads ask questions, triage exceptions, and approve proposals in conversation.

  2. POST /webapp/vendor-intake
    → playbook: vendor-intake-triage

    02. WebApp submit

    Public intake surfaces start the same playbook-backed AI path as internal chat.

  3. step: ai_assist
    → propose → approve → continue

    03. Workflow step

    Workflows invoke AI mid-flow—with wait steps and shared history.

  4. cron: 0 8 * * 1-5
    → weekly intake review

    04. Scheduler

    Recurring checks—SLA reviews, stale record sweeps, renewal prep—run on a cadence.

  5. event: sync_failed
    → retry under rules

    05. Connection event

    Alerts and connection events wake AI to summarize, retry, or escalate.

Live conversation

From escalated case to governed fix—with full delivery context.

A support lead picks up a customer escalation. Trace AI pulls ticket history, delivery commitments, and operational records from one place, proposes a controlled correction, waits for human approval, and runs only what your team authorizes—with the full trail on the case. See operational support in the solutions catalog.

AI

Operational exception assistant

Controlled · Playbook: ops-exception-triage

Ask about delivery context, proposed fixes, or approval steps…Send

Simulated conversation — connections run only after human approval

Capabilities

Operational AI with safety rules your security team can actually configure.

Every capability runs on one platform—Datapools, workflows, connections, and reviewable history together.

  1. chat · webapp · process
    scheduler · event

    01. One AI, many entry points

    Started by a person, WebApp, workflow, schedule, or event.

    The same controlled AI can start from a chat message, a WebApp form submit, a workflow step, a scheduled run, or a connection event—one approach across channels instead of a separate chat tool for each surface.

  2. task → best model
    no playbook rewrites

    02. Right model, automatically

    Each task uses the most suitable language model.

    Tealfabric routes AI work to the appropriate model behind the scenes. Operations teams do not chase the release cycle—capability upgrades apply on your platform without rewriting playbooks.

  3. SELECT datapool.*
    policy: granted

    03. Query and update allowed data

    Read and write operational records your rules allow.

    AI works against Datapools, workflow context, and connection results in place—no copying supplier lists into a side database or replicating CRM rows into a chat window.

  4. SKILLS/vendor-intake
    ops-owned playbook

    04. Playbooks your ops team owns

    Skills playbooks on the platform for repeatable AI workflows.

    Business operations and platform admins publish operational playbooks—exception handling, intake triage, renewal checks—that AI loads by policy. Your team builds the workflow logic; the platform enforces safety rules.

  5. human approval
    awaiting decision

    05. Human approval built in

    Approvals, clarifications, and error routing when judgment matters.

    The platform asks the right person when your rules require it—approve a connection run, confirm a bulk update, or handle an ambiguous exception. AI assists; people decide on risky paths.

  6. tool rules: on
    execute: controlled

    06. Safeguards you configure

    Clear safety rules and overrides on tools and what may run.

    Tool access rules, connection run permissions, and safe test profiles combine into one policy boundary. Security and ops leads define what AI may propose versus run—secure by design, not bolted on after launch.

  7. wait → execute
    same platform loop

    07. Same execution model as workflows

    Wait steps, connection runs, and workflow steps on one platform.

    AI shares the execution model with Process Automation—wait for events, call connections under your rules, and hand results back to the workflow. No second automation runtime to govern.

  8. propose · approve · run
    full trace logged

    08. Full action history

    Every propose, approve, and run step is logged.

    History captures what the AI read, which playbook it loaded, what it proposed, who approved, and what ran. Post-incident review and compliance questions get answers from one trail—not chat exports.

  9. alert: sync_failed
    AI triage

    09. Alert-aware responses

    React to connection failures and operational signals.

    AI can be invoked from alerts or workflow exceptions—summarize the failure, suggest retry or escalation, and route to a person for approval before any connection runs.

Who it is for

Platform admins, business operations leads, procurement teams, and security stakeholders who need AI on operational data and system connections—not generic copy generation in a separate chat window.

Related workflow: Ops exception handling with human approval. Platform depth: Governance, Datapools, Integrations, Process Automation.

Ready to see it on your stack?

Put agents inside the workflows you already run—not beside them.

We walk through team playbooks, human approval, controlled connection runs, and the history your security and ops teams need.

Book a demoStart a pilotGovernance depth

Pilots start with one live workflow—system connections, steps, and full history.