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QpiAI Agent Hive

Battery manufacturing example

One shared source of context for a battery plant.

Connect live plant events with engineering knowledge, let agents do the legwork and keep engineers in charge of the decision.

See the architecture
Automated battery cell manufacturing equipment inside a clean production environment
This story is anonymized. It explains a proposed scope and architecture, not a completed deployment or measured results.

Bring live events and engineering documents into the same picture.

Both are mapped to a shared manufacturing model, so an alarm, a process step and the right procedure can be connected.

Sources move through discovery into a plant context graph and then into agents, feedback and reviewed answers.

Sources

  • Live operational events

    Machine states, values, alarms and counts

  • Engineering documents

    SOPs, recipes, datasheets and specifications

  • Genealogy

    Material, lot, cell, module and pack history

Build the connections

  • Map the event stream

    Connect events to equipment and process steps

  • Read the documents

    Find equipment, limits, rules and applicability

  • Send uncertain items for review

    Engineers check low-confidence findings

What teams use

  • Specialist agents

    Different roles investigate the same question

  • Feedback requests

    Ask for missing data or clarification

  • Reviewed answers

    Sources, reasoning and engineer approval

Let agents do the groundwork. Keep engineers in charge.

Agents suggest new entities, relationships and rules as they read the sources. Engineers review every change before it becomes part of the graph.

Look back at any answer. Each answer stays tied to the graph version and sources that produced it.

  1. SuggestAgents read the sources and suggest entities, relationships and rules.
  2. ReviewProcess engineers accept, edit or reject each proposed change.
  3. Add to the graphApproved changes are applied to the live stream and document set.
  4. Find what is missingUncertain connections and unanswered questions go back for review.
Agents suggest changes, engineers review them, approved changes are added and missing information returns for review.

Test it with the questions engineers actually ask.

A question set agreed with plant engineers checks whether answers are correct, complete and backed by useful evidence.

Four plant question types flow through engineer review, a test set, a new version check and an approved graph update.

  • Find a fact

    What is the current specification?

    Return the right setpoint, limit or procedure for this product and process.

  • Show the evidence

    Where did this answer come from?

    Link the relevant events, documents, lots and timestamps.

  • Investigate a cause

    What is the most likely cause?

    Compare likely causes across process areas and show the supporting evidence.

  • Check a proposed change

    What else changes if we do this?

    Find the affected specifications, procedures, limits and downstream process steps.

Keep the same graph, whichever model setup you choose.

Models can run inside the plant or through a controlled gateway. Plant context and access controls stay consistent.

Fully air-gapped deployment architecture

Run everything inside the plant network.

The graph, agents and models run locally. Updates arrive as signed offline packages, with no data leaving the site.

  • Local model serving
  • Read-only plant connectors
  • Plant SSO, RBAC and audit
Hybrid model access deployment architecture

Keep plant data local and use approved models.

A policy gateway controls what is sent, which model receives it and what comes back.

  • Per-agent model routing
  • One controlled egress path
  • Graph and source data remain local

Want the technical detail?

Share the full implementation brief with your team.

It covers the source model, graph-building process, evaluation approach and both deployment options.

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