Pulp & Paper

Put reliability effort where the next production loss or shutdown overrun matters most.

Tacit AI connects approved CMMS history, mill documents, and engineer judgment to source-traceable risk and maintenance proposals. Mill owners approve every priority and change.

<3 weeks
first FMEA delivered
70-95%
engineer-ready accuracy
Mill-wide
failure visibility

Where mill evidence creates leverage

Use mill evidence to prioritize production loss, shutdown scope, and engineering time.

Set the production-loss priority

Compare repeat events, maintenance burden, quality impact, and customer-defined lost-production consequence across paper-machine, pulp-line, and utility systems.

Prepare a reviewed risk baseline

Tacit AI drafts FMEA/FMECA content from authorized work orders, manuals, drawings, and procedures. Every material claim links to a source or is marked as an assumption for engineer review.

Defend shutdown scope

Connect selected failure modes and asset consequence to proposed inspection, repair, and replacement work. Planners and technical owners decide what enters the outage package.

Review maintenance changes

Compare proposed PM tasks and intervals with failure evidence, operating context, and current controls. Approved owners accept, revise, or reject each proposal before CMMS change control.

Expose evidence gaps

Show missing failure detail, conflicting document versions, and unsupported assumptions before they shape a risk or shutdown decision. The mill retains its existing systems of record.

Recover mill knowledge

Make approved troubleshooting, outage, and equipment knowledge searchable with citations and revision context. Shift teams can reuse known evidence without treating local experience as universal.

Pilot evidence

Prove value on one critical mill system.

Select one critical system and one decision: production-loss priority, shutdown scope, or maintenance strategy. Agree the current baseline, approved source set, named engineering reviewers, and acceptance record before Tacit AI prepares any output.

Define the pilot evidence

For the team that owns mill economics

One workflow for operations, maintenance, reliability, and shutdown leaders.

Built for mill managers and functional leaders who must balance tonnes, quality, risk, maintenance spend, and scarce engineering capacity without bypassing technical authority.

Brownfield

Existing operations

Use a controlled sample of current work orders, event records, manuals, and existing analyses to test one active mill decision.

Greenfield

New installations

Prepare an initial source-linked risk and maintenance baseline from approved design and OEM evidence, with assumptions visible until operating history develops.

Standards supported

Tacit AI supports source-linked evidence preparation for customer-selected reliability, asset-management, environmental, and process-safety workflows. Qualified mill personnel determine applicability, approve decisions, and retain responsibility for compliance.

ISO 55001
IEC 60812
TAPPI standards
ISO 14001
OSHA PSM
NFPA 85
Controlled validation

Test one critical system against a mill-owned baseline.

The mill defines the economic question, source boundary, reviewers, and evidence required for an expansion decision.

Phase 1

Scope and baseline

Customer-defined

Choose one critical system and agree the production or maintenance baseline, source set, deliverable, and named reviewers.

Gate
Proceed when the mill approves the evidence boundary and acceptance method.

Phase 2

Build and review

Bounded pilot

Deliver cited analysis and maintenance proposals, an assumption log, and a structured engineering review record.

Gate
Compare review effort and decision usefulness with the agreed baseline.

Phase 3

Approve and decide

After mill review

Authorized engineers approve or reject outputs. The economic buyer decides whether the measured evidence supports another system or process area.

Gate
Expand only against the mill’s agreed production, maintenance, and governance criteria.

Scope one mill decision

Bring the critical-system boundary, recent event evidence, and the production or shutdown measure your team wants to improve.

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