UPLO Manufacturing

Connects your AI assistant to the structured knowledge layer built from your plant floor documentation — work orders, inspection reports, preventive maintenance schedules, CAPA records, production batch logs, and equipment manuals. When a machine goes down at 2am or a customer reports a defect, you need answers from your own data, not a web search.

Session Start

Pull your manufacturing context first. This loads your role (maintenance engineer, quality manager, production supervisor), active production priorities, and any open quality holds or equipment issues.

CODEBLOCK0

Check directives if you need to understand current throughput targets or quality improvement initiatives:

CODEBLOCK1

When to Use

• - Investigating a non-conformance: what were the process parameters for batch #4471 on Line 3?
• Finding the torque specification and calibration schedule for the CNC mill in Cell B
• Pulling the FMEA (Failure Mode and Effects Analysis) for the new product introduction
• Checking if a specific raw material lot passed incoming inspection before it hit the floor
• Reviewing OEE trends for a production line to justify a capital expenditure request
• Locating the lockout/tagout procedure for the hydraulic press before a maintenance window
• Determining which shifts had the highest scrap rate last month and what corrective actions were taken

Example Workflows

Root Cause Analysis for Customer Complaint

A customer received parts with dimensional non-conformances. You need to trace back through your process.

CODEBLOCK2

The structured extraction links inspection data back to specific work orders, machine settings, and operator certifications — giving you a complete traceability chain for your 8D report.

Preventive Maintenance Planning

You're building next quarter's PM schedule and need to consolidate equipment data.

CODEBLOCK3

Cross-reference PM intervals against actual failure data to shift from calendar-based to condition-based maintenance where the data supports it.

Key Tools for Manufacturing

search_knowledge — Query across work orders, inspection records, PM logs, and SOPs simultaneously. The structured extraction means you get typed fields (part numbers, batch IDs, measurement values) not just raw text. Example: INLINECODE0

searchwithcontext — Follows the relationships between documents. A work order connects to the BOM, which connects to incoming material certs, which connect to supplier audits. Example: INLINECODE1

reportknowledgegap — Found a machine with no documented setup procedure? A process with no control plan? Flag it. This feeds back into your quality system and ensures gaps get closed. Example: report that the new laser welder has no documented process validation (IQ/OQ/PQ).

propose_update — When an SOP is wrong or a spec has changed, propose the correction directly. It enters the review queue for the document owner. Example: update the anodizing bath concentration range after a process optimization study.

flag_outdated — Critical for manufacturing where revision control is everything. Mark superseded drawings, expired calibration certs, or obsolete work instructions before someone on the floor uses the wrong version.

Tips

• - Search by part number, work order number, or equipment asset ID for the most precise results — the extraction engine indexes these as structured fields, not just text tokens.
• Manufacturing data is deeply interconnected. If a simple search_knowledge doesn't give you the full picture, switch to search_with_context to traverse the relationships (part -> BOM -> supplier -> cert -> inspection).
• Always check document revision levels in results. If you spot an outdated revision, flag it immediately — in manufacturing, the wrong revision can mean scrapped parts or a safety incident.
• When logging conversations about quality issues, include the NCR or CAPA number — it makes the audit trail searchable when regulators or customers ask about corrective actions taken.