SLOV AI

Agents that work on your plant's records.

Answers with their source. One agent per area, passing cases to each other. Automations that flag deadlines before they hit. They propose; someone on your team approves.

See the agents at my plant

Safety agent

Oil mist at press 2

Step: Investigate

What the agent delivers, exactly as it produces it

On record: A report of denser oil mist at press 2 and a saturated filter.
Not established: Whether the filter is the only cause, whether the area is safe right now, and whether the filter has already been replaced.
Still missing: Someone's assessment of the area today, the machine history and running hours, an inspection, a CAPA owner and a pass criterion.
Next step: The flow waits for a person to assess the area as it is right now.

Sample data. A person from Safety approves, returns or rejects.

From question to decision

What changes once your plant has a history

SLOV AI works on the records your plant already keeps. Each agent is built around your plant's processes, together with your team. It learns from your plant, not from the internet. It proposes; a person decides.

Today

People dig through folders, investigate the same problem twice, and write every report from a blank page.

Dig through folders, ask whoever remembers

The same problem investigated twice

Everyone starts from a blank page

Nobody knows who approved what

With SLOV AI

Every answer comes with its source. Every precedent comes with what was done. Every proposal comes with owners and evidence. Every decision is signed.

An answer with its source: document, clause, version

The similar case, what was done, and whether it worked

Actions, owners and evidence, proposed

A person approves, and it is on record

01Question

TodayDig through folders, ask whoever remembers

With SLOV AIAn answer with its source: document, clause, version

02Precedent

TodayThe same problem investigated twice

With SLOV AIThe similar case, what was done, and whether it worked

03Proposal

TodayEveryone starts from a blank page

With SLOV AIActions, owners and evidence, proposed

04Decision

TodayNobody knows who approved what

With SLOV AIA person approves, and it is on record

People dig through folders, investigate the same problem twice, and write every report from a blank page.

Every answer comes with its source. Every precedent comes with what was done. Every proposal comes with owners and evidence. Every decision is signed.

What changes with SLOV AI

AI needs an operation with a history

The records your plant produces every day add up to an operating base: what happened, where, when, who responded, what evidence they left and how it turned out. That is the context SLOV AI uses to find precedents, answer with a source and propose actions.

Knowledge

What your plant knows, with its source: procedures, matrices, lessons learned and the history of every case. Every answer says where it came from, so anyone can check.

Agents

One per area, with a clear boundary. It reads its area's records, spots the similar case and proposes the action that already worked. When a problem crosses into another area, the full case moves to the next agent, and the handoff is on record: who passed it, and why.

Automations

The calendar work: a warning before a permit runs out, an escalation when something has not moved in days, a request for the effectiveness check when it is time. Each one records what it evaluated and whom it notified.

Judgment

Nothing is approved, closed or sent on its own. Every agent proposes. A person reviews and decides, and that decision is signed.

What SLOV AI does not do

The limits are part of the design, not a gap. They are what makes it safe to run inside a plant.

Signing stays with people. The agent proposes; a person approves.
No invented sources. When the procedure does not cover it, the answer says so.
An agent never approves its own proposal or closes its own case.
It does not have to be everywhere. It belongs where there are records and a decision to make.
Clear rules stay rules. A deadline is tracked by a date, not guessed by a model.

One case, step by stepOil mist at press 2Walk through the case

Sample data throughout. The Saltillo plant reports denser oil mist at press 2 and a saturated filter, and a CAPA opens with no cause, no actions and no owner. Below are four of the eight steps. Deciding is one of them, and it belongs to a person.

01
Receive Automation
The run opens, and a snapshot of the event file is taken so nothing that happens later can change what the agent saw.
02
Investigate Agent
The agent pulls running hours, the date of the last filter change, photos and similar past events. Then it lays out hypotheses, each with the evidence for it and against it.
What the agent delivers, exactly as it produces it
On record
A report of denser oil mist at press 2 and a saturated filter.
Not established
Whether the filter is the only cause, whether the area is safe right now, and whether the filter has already been replaced.
Still missing
Someone's assessment of the area today, the machine history and running hours, an inspection, a CAPA owner and a pass criterion.
Next step
The flow waits for a person to assess the area as it is right now.
03
Decide Person
The root cause hypothesis goes to a person, who approves it, sends it back or rejects it. Anything sent back has to come back corrected, with the change on record.
04
Plan Agent
Once the cause is approved, the agent drafts the plan: what to do, who should own it, and what evidence each action has to produce.
What the agent delivers, for a person to approve
Proposed actions
Swap the filter and measure how quickly the new one saturates. Measure the oil mist at press 2 with calibrated equipment.
Suggested owners
Maintenance takes the filter, Safety takes the measurement, unless the person decides otherwise.
Evidence each action must produce
For the filter, a closed work order with a photo of the new part. For the measurement, a signed report.
Effectiveness criterion
Two consecutive readings within the limit, taken with the new filter in service.

An agent earns its place in a plant by keeping three things apart: what is on record, what is not established, and what is still missing. A model that sounds sure about things it does not know is worse than no model at all.

How many times has the same problem been investigated twice?

It is set up with your operation's documents, areas and rules, and tuned with your team until it works the way your plant needs. Every agent, every flow and every automation is built to fit. Nothing about it comes off the shelf.

See the agents at my plant

Not yet? Keep reading here: See SLOV Operations