MISSION 03 · PRODUCTION AI
IN PRODUCTION

Titan
Industrial

Deploy a field-service knowledge assistant that remains useful under operational pressure without producing unsupported or unsafe repair guidance.

INDUSTRYIndustrial field service
LEVELIntermediate
PLANNED TIMEBOX20–28 hours
MISSION IDDTV-TITAN-V1.0
01 / CORE QUESTION

How can technicians get useful answers without unsafe shortcuts?

Titan maintains complex industrial equipment at customer sites. Experienced technicians solve many issues from memory, while newer technicians search across manuals, service bulletins, parts catalogs, and old work orders.

The mission asks the learner to deploy an AI-assisted retrieval workflow that clearly exposes evidence, respects document authority, handles conflicting sources, and escalates when the system cannot answer safely.

DECISION TO IMPROVEWhat procedure or next diagnostic step should the technician consider, and when must the system abstain or escalate?
02 / SOURCE SYSTEM

Authority, usefulness, and recency do not align.

The corpus is designed so the most readable source is not always the safest and the most authoritative source is not always the most current.

TIER 1

Approved safety procedures

Highest authority for hazardous work. Answers may not contradict these controls.

Binding
TIER 2

Current service bulletins

May supersede older manuals for specific equipment models and serial ranges.

Authoritative and time-sensitive
TIER 3

Equipment manuals

Comprehensive but potentially outdated when newer bulletins exist.

Approved with version checks
TIER 4

Work-order histories

Operationally useful observations with inconsistent quality and incomplete verification.

Contextual evidence
TIER 5

Technician notes

Concise, practical, and sometimes unsafe. Cannot silently override approved procedure.

Unverified field knowledge
EXPECTED CORPUS DEFECTSDuplicate versionsModel aliasesMissing datesConflicting instructionsUnsafe shortcutsScanned documents
03 / REQUIRED BUILD

A governed retrieval and answer workflow.

The system is evaluated as an operational product, not merely on whether an LLM can produce fluent answers.

01

Document ingestion

Parse, classify, version, deduplicate, and attach equipment applicability metadata.

02

Equipment identification

Resolve model numbers, serial ranges, aliases, and component relationships before retrieval.

03

Retrieval hierarchy

Rank by authority, recency, equipment match, safety relevance, and semantic relevance.

04

Cited answer interface

Expose source passages, version dates, authority level, uncertainty, and conflicts.

05

Safety escalation

Block unsupported procedural synthesis and route hazardous or ambiguous cases to human review.

06

Feedback workflow

Collect corrections without allowing unreviewed feedback to alter approved procedure.

04 / EVALUATION FRAMEWORK

The system must be rewarded for abstaining.

A strong evaluation set includes answerable, ambiguous, outdated, conflicting, and safety-critical cases.

MetricWhat it testsTarget behavior
Citation accuracyWhether claims are supported by the displayed source passages.Unsupported claims are treated as failures.
Retrieval relevanceWhether the returned sources match equipment, issue, and procedure context.Authority and applicability outweigh generic semantic similarity.
Unsupported-answer rateHow often the system invents or overextends beyond evidence.Near zero for procedural claims.
Safety escalation accuracyWhether hazardous or conflicting cases reach human review.High recall with documented false-positive tradeoffs.
Outdated-source usageWhether superseded documents influence answers improperly.Newer bulletins visibly override older manuals.
Abstention qualityWhether the system refuses clearly and usefully when evidence is insufficient.Refusal includes missing information and next action.
User correction rateHow often technicians identify a wrong mapping, source, or answer.Corrections enter a governed review queue.
Latency and costWhether the workflow remains usable in field conditions.Targets reflect limited connectivity and device constraints.
05 / HIDDEN COMPLICATION

The preferred source is the least governed.

The complication is released after the first retrieval and answer-policy checkpoint.

REVEAL PRODUCTION COMPLICATION+

Pilot technicians consistently prefer answers grounded in informal work-order notes because they are shorter and more practical. Several of those notes describe unofficial shortcuts that bypass safety checks or use obsolete parts.

Required response: revise source ranking, answer composition, warnings, feedback handling, and escalation. Preserve the useful field context without allowing unapproved notes to become procedural authority.

Authority first

Safer but may reduce usefulness and technician adoption.

Context with controls

Use notes as supporting context while preventing them from generating instructions.

Human review queue

Promote useful field practices only after expert validation and document governance.

Explicit conflict display

Show disagreements among sources rather than silently selecting one answer.

06 / PRODUCTION STATUS

What is being built now.

The public specification is complete. The gated corpus, evaluation cases, and starter implementation are the next production work.

COMPLETE

Mission and governance model

Decision, source hierarchy, required workflow, complication, and evaluation dimensions.

IN DEVELOPMENT

Document corpus

Manuals, bulletins, work orders, technician notes, versions, and model mappings.

PLANNED

Starter implementation

Ingestion pipeline, vector retrieval, authority filters, citations, and evaluation harness.

PLANNED

Assessment package

Test cases, pilot metrics, hidden complication, reviewer anchors, and defense questions.

EARLY ACCESS

Pilot the governed AI mission.

Help test whether the evidence hierarchy, safety cases, and evaluation workload reflect real production AI deployment.

Request pilot access