Zanus AI for Business Operations
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ZANUS AI FOR BUSINESS OPERATIONS — AI WORKFORCE

An analyst that never misses a Monday.

Assign work to the AI the way you assign it to a coordinator: type, sources, deadline, recurrence. The daily exception review, the weekly ops report, the monthly SOP audit — executed on schedule, cited, waiting for sign-off.

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Private tenant or your own server · unlimited users · no per-run automation metering

Assigning recurring operational AI work

Four steps: work type, sources, tags, schedule — the operational drumbeat, automated.

What it does

Work types molded to the operation

Exception review, shift summary, vendor scorecard, SOP audit — work types carry their default libraries and examples, so every run starts from your standards.

6 AM: yesterday is already analyzed

The daily review reads the day’s exceptions, groups them by cause, flags repeat offenders and drafts the actions — filed before the ops huddle convenes.

First of the month: the SOP audit runs

Every procedure checked for staleness, contradictions and gaps against the month’s actual exceptions — the binder confronted with reality, monthly, automatically.

Visible, redirectable, accountable

Every AI work is a full session: reasoning streamed as it runs, sources cited, milestones tracked. Open the Tuesday vendor scorecard mid-run, redirect its emphasis, or stop it — it works like staff, on the record.

  • Reasoning and citations visible per session
  • Urgent / Overdue / Today views over the AI’s queue
  • Output review: nothing final without a human yes
Roster of scheduled operational AI works
Operations tooling is metered by workflow, by automation run and by editor seat — so the process map lives in a slide deck, the real SOPs live in a shared drive nobody opens, and the ops manager is still the human router between both. Zanus AI: SOPs, automations, exception handling and reporting in one system — unlimited users, unlimited runs, flat price, in your private tenant or on your own server.

Questions, answered

What if two works need the same data at once?

They run in parallel — the AI does not queue like a person. Your review capacity is the constraint, not its throughput.

Can works trigger other works?

Chain them through the automation canvas: the exception review’s output can trigger the escalation draft, visibly, with run states per block.

See it working on YOUR operation — the demo IS the product.

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