30% productivity lift
The use-case deck reports a 30% productivity increase after workflows moved through LULU.
customer use case / field operations
The Field Operations AI Agent That Improved Productivity by 30%
How LULU turns natural-language operations requests, production dashboards, WA incident groups, installation evidence, and anomaly checks into a controlled AI operations layer.
what changed
The field team kept its tools. LULU added the operating layer.
The deck frames LULU as a human intelligent layer: automate the repeatable process, surface the right operational data, track the metrics that matter, and let supervisors ask for work status in natural language.
Better technician pacing
The deck reports that 25% of technicians moved from yellow status to green after the operating loop was introduced.
14% fuel savings
Weekly gasoline spend moved from 700 pesos to 603 pesos in the reported dashboard result.
Faster exception detection
The AI-driven workflow slide references detecting up to 70% of relevant issues with a response-time target around 24 hours.
01 / story
The field team already had the signals. They were trapped in the wrong places.
A field operation rarely fails because nobody reports what happened. It fails because the report lands as a WA incident, a dashboard row, a photo, a route comment, or a supervisor question that somebody has to manually connect.
The LULU deployment treated those fragments as operational input. WA groups continued to be one place where crews reported incidents, while dashboards, evidence, and metric questions became part of the same LULU workspace: shared context, queued tasks, intelligent routing, and human review.
The useful shift was not replacing supervisors. It was giving them a control layer that could monitor technician production, read incident context, compare real work against expected pace, and propose the next action before the delay became invisible.
before the deployment
Before LULU, supervisors had to read the room one message at a time.
Technicians reported blocked work, missing equipment, gasoline issues, personal delays, and completed visits across WA groups. The same operation also depended on dashboards, route data, installation photos, contract references, and compensation rules.
Each data source was understandable by itself. The drag came from making a decision across all of them quickly enough: who is behind pace, what is blocking them, whether an incident is real, and which supervisor or radio operator should act.
02 / operating problem
The signal was real. The decision path was too slow.
The work was not a single automation. It was a field operating loop that crossed messages, metrics, routes, photos, and human approval. A supervisor needed to know what happened, whether it was unusual, and who should act next.
LULU made the loop legible by turning scattered operational inputs into a queue of reviewable decisions. The model supplied analysis, but the operating layer supplied context, ranking, routing, and approval.
01Natural-language request
02Production data
03Incident and evidence check
04Supervisor action
WA groups became the incident room
Incidents, missing equipment, blocked locations, and gasoline updates were reported in groups that were fast to write into but slow to analyze.
Dashboards needed interpretation
Production charts showed real versus expected work, but supervisors still had to connect the chart to the technician, route, and incident context.
Evidence review was manual
Installation photos and FAT terminal evidence required careful counting, comparison, and contract-to-port reasoning before a decision could be trusted.
Incentives needed auditability
Bonus rules depended on visits, installations, and timing, which made anomaly detection a process question rather than a simple metric.
key takeaway
The value came from shortening the distance between a field signal and a trusted operational decision.
03 / deployment pattern
What we implemented, and how the workflows ran.
What was deployed
We implemented LULU as an operations copilot for field work, not as a generic assistant. The deployment connected the deck's four use-case families: dynamic dashboards from multiple data types, AI-driven workflows, anomaly detection and process correction, and new ways of working.
Every implemented workflow uses the same operating path: collect the live signal, compare it to operational truth, prepare the decision, request approval where needed, and route the work to the right human owner.
01Listen
We turned operations chatter into input
02Compare
We checked real work against expected work
03Propose
We turned each signal into a decision
04Route
We routed the work to the right owner
from field noise to controlled work
We built one operating loop across every LULU workflow.
We kept the field organization inside the tools where work already happened, then connected the operating layer behind them. LULU listened to the live signal, compared it with the trusted baseline, prepared the next decision, and routed the case to the person allowed to approve it.
- A natural-language interface for asking production questions without hunting through dashboards.
- WA incident parsing that separates location, technician, equipment, gasoline, and blocked-work context.
- Dynamic dashboards that compare real production against expected pace by time window, location, and technician.
- Anomaly detection that checks bonus behavior and process drift against bitacora, supervisor context, and georeferenced visits.
- Human approval gates for exceptions, route changes, compensation analysis, and evidence-based decisions.
REAL / EXPECTED / PACE
Daily production monitoring
We implemented this for supervisors who needed to know whether today's field production was ahead, behind, or blocked.
The workflow starts with a simple question: are we on pace? LULU answers by combining expected production, real completions, location-level variance, and technician comments into one operating view.
01Ask for production02Compare pace03Find blockers04Send action list
stop here
We kept target changes, excuse approvals, and production-record changes behind supervisor confirmation.
how it runs
The workflow started when a supervisor asked for real versus expected production, or the dashboard crossed a variance threshold. LULU produced the current production picture and called out the locations, technicians, and blockers that needed review.
Load the baseline
Use the expected production window and six-week comparison instead of judging the day from raw totals alone.
Read the variance
Highlight the total network delta, technician delta, and locations where real work is materially behind expected pace.
Attach the reason
Bring route comments and incident context into the summary so the supervisor sees the likely cause, not only the number.
field note
Production analysis
We made production questions answerable in natural language with the current real-versus-expected picture for the day.
- Compare accumulated ONTs against the six-week expected baseline.
- Highlight underperforming locations and technician-level pace.
- Attach route comments so the metric does not lose operational context.
WA / ISSUE / OWNER
Incident routing
We implemented this for field updates arriving in WA groups with mixed language, abbreviations, locations, and equipment codes.
A WA group is fast for reporting but weak as a queue. LULU reads the thread, extracts the operational facts, and separates routine updates from cases that need supervisor or radio action.
01WA update02Extract facts03Rank urgency04Route owner
stop here
We kept discipline, route approvals, and incident closure behind human review.
how it runs
The workflow started when a crew reported missing equipment, gasoline, blocked access, no work, personal issues, or a stalled location. LULU converted the message into a structured incident with location, affected crew, issue type, urgency, and proposed owner.
Normalize the message
Translate abbreviations and short field updates into clean issue categories such as no equipment, no gasoline, blocked, or no work.
Connect the context
Attach the message to known locations, crews, route status, and production impact.
Route for action
Send the issue to the right supervisor or radio operator with the supporting context already summarized.
field note
Incident routing
We turned WA group incident messages into a structured queue of blocked work, equipment issues, gasoline needs, and personal exceptions.
- Extract location, technician, crew, equipment, gasoline, and blocker details.
- Separate repeated incidents from new exceptions.
- Route the case to supervisor or radio review instead of leaving it in a group chat.
PHOTO / PORT / CONTRACT
Installation evidence review
We implemented this for field decisions that depended on photos of a FAT terminal, port usage, before-and-after state, or contract association.
Photo review is useful only when the agent can say what it saw and what it did not see. LULU turns the image into a structured observation and flags uncertainty before the work is accepted.
01Before photo02After photo03Port count04Confidence note
stop here
We kept final evidence decisions blocked when the photo was cropped, blurry, incomplete, or missing terminal context.
how it runs
The workflow started when a supervisor asked LULU to inspect installation photos and identify terminal state, port usage, and contract association. LULU extracted visible evidence, compared before and after, and summarized what could be trusted from the images.
Count what is visible
Identify total visible ports, occupied ports, available ports, labels, and any cable intervention visible in the image.
Compare the state
Explain how the after photo differs from the before photo and whether the change supports the requested conclusion.
State confidence
Mark medium or low confidence when the crop prevents an exact port, contract, or terminal match.
field note
FAT evidence review
We implemented before-and-after installation evidence review so the system could summarize what was visible and what still needed verification.
- Count visible ports, occupied ports, and available capacity.
- Compare the before and after state of the terminal.
- Mark uncertainty when the photo does not show enough evidence.
RULE / BEHAVIOR / REVIEW
Incentive anomaly audit
We implemented this where attendance or productivity bonuses depended on visit counts, installations, timing, and route evidence.
Incentive rules can create edge cases. LULU checks behavior against the approved policy and gathers the evidence a manager needs before deciding whether a pattern is legitimate or suspicious.
01Bonus policy02Technician history03Route evidence04Manager review
stop here
We did not let LULU accuse, penalize, or change compensation automatically; it prepared a review packet for the manager.
how it runs
The workflow started when a technician's production pattern appeared optimized around the bonus threshold or inconsistent with route evidence. LULU compared work logs, route data, supervisor notes, and georeferenced visits against the incentive rule.
Read the rule
Translate bonus conditions into checks such as two installations, one installation plus visits, or a required visit count.
Find anomalies
Look for isolated spikes, timing patterns, route inconsistencies, or repeated threshold behavior.
Prepare review
Summarize the evidence, counter-evidence, and open questions for the manager who owns the decision.
field note
Bonus anomaly audit
We implemented bonus anomaly checks against technician behavior, attendance rules, route context, and review evidence.
- Evaluate installation and visit counts against the approved bonus policy.
- Compare suspicious patterns against route logs, supervisor notes, and georeference data.
- Escalate possible manipulation for human review instead of accusing automatically.
04 / control system
Let LULU read the evidence. Keep the final judgment human.
Control came from keeping people in the loop.
LULU was allowed to analyze and propose, but the system kept sensitive operational decisions under human review. That was especially important for route changes, technician performance, compensation, and photo evidence.
01Operational signal
02Agent analysis
03Human approval
04Routed action
Production request
LULU, give me today's real versus expected production analysis.
Loads the current production window, compares actual ONTs against expected pace, and identifies locations that are materially behind.
Supervisor receives a ranked action list with production deltas, route comments, and suggested follow-up owners.
WA incident
Location update: two crews have no equipment, one crew has no gasoline, and another route is blocked.
Extracts issue type, affected locations, likely owner, and urgency instead of treating the message as ordinary chat.
Radio or supervisor receives a structured incident queue with the next decision clearly marked.
Evidence review
Review the before and after FAT photos and tell me whether the installation changed the port state.
Counts visible ports, compares the two images, notes uncertainty, and avoids final approval when the after photo is cropped.
Owner receives an evidence summary with confidence and the exact part that still needs human verification.
operating controls
Keep LULU inside the approved workflow.
The operating controls define which data sources can be used, which metrics are authoritative, and who can approve a routed action.
- Data scopeWA groups, production dashboards, route notes, evidence photos, and bonus policy context
- Decision rightsAgent proposes; supervisor or owner approves sensitive action
- Metric authorityExpected pace, real production, route comments, and approved bonus rules
- Routing policySupervisor, radio operator, or owner based on issue type and urgency
- Review recordSummaries preserve the signal, evidence, confidence, and recommended owner
automation policy
Make the stopping rules explicit.
The guardrails prevent the agent from turning analysis into discipline, compensation changes, or operational commitments without review.
- The request asks for production status or dashboard interpretationAnswer with metrics, deltas, and context using approved operational data.
- The thread includes blocked work, missing equipment, or gasoline issuesCreate an incident summary and route it to the right human owner.
- The case involves technician performance, compensation, or possible manipulationPrepare an audit packet and require manager review before any action.
- Photo evidence is incomplete, cropped, blurry, or ambiguousState uncertainty and request verification instead of finalizing the conclusion.
first deployment
Start with one production question and one real incident flow.
The first launch should start with one production dashboard, one WA incident flow, and one evidence-review workflow before adding broader anomaly detection and process correction.
- 01
Pick one daily production question
Start with the question supervisors already ask every morning, such as real versus expected production by location and technician.
- 02
Collect real incident threads
Use actual WA group examples for blocked work, missing equipment, gasoline, no work, route problems, and personal exceptions.
- 03
Define the approval boundary
Write down what LULU can answer directly, what it can propose, and which actions require supervisor or owner approval.
LULU field operations implementation details
Operating problem
The work was not a single automation. It was a field operating loop that crossed messages, metrics, routes, photos, and human approval. A supervisor needed to know what happened, whether it was unusual, and who should act next.
LULU made the loop legible by turning scattered operational inputs into a queue of reviewable decisions. The model supplied analysis, but the operating layer supplied context, ranking, routing, and approval.
The value came from shortening the distance between a field signal and a trusted operational decision.
LULU field operations playbooks
Daily production monitoring
We implemented this for supervisors who needed to know whether today's field production was ahead, behind, or blocked.
The workflow starts with a simple question: are we on pace? LULU answers by combining expected production, real completions, location-level variance, and technician comments into one operating view.
- Ask for production
- Compare pace
- Find blockers
- Send action list
- Trigger
- A supervisor asked for real versus expected production, or the dashboard crossed a variance threshold.
- Agent job
- LULU produced the current production picture and called out the locations, technicians, and blockers that needed review.
- Boundary
- We kept target changes, excuse approvals, and production-record changes behind supervisor confirmation.
- Load the baseline: Use the expected production window and six-week comparison instead of judging the day from raw totals alone.
- Read the variance: Highlight the total network delta, technician delta, and locations where real work is materially behind expected pace.
- Attach the reason: Bring route comments and incident context into the summary so the supervisor sees the likely cause, not only the number.
Example: Production analysis. We made production questions answerable in natural language with the current real-versus-expected picture for the day.
Incident routing
We implemented this for field updates arriving in WA groups with mixed language, abbreviations, locations, and equipment codes.
A WA group is fast for reporting but weak as a queue. LULU reads the thread, extracts the operational facts, and separates routine updates from cases that need supervisor or radio action.
- WA update
- Extract facts
- Rank urgency
- Route owner
- Trigger
- A crew reported missing equipment, gasoline, blocked access, no work, personal issues, or a stalled location.
- Agent job
- LULU converted the message into a structured incident with location, affected crew, issue type, urgency, and proposed owner.
- Boundary
- We kept discipline, route approvals, and incident closure behind human review.
- Normalize the message: Translate abbreviations and short field updates into clean issue categories such as no equipment, no gasoline, blocked, or no work.
- Connect the context: Attach the message to known locations, crews, route status, and production impact.
- Route for action: Send the issue to the right supervisor or radio operator with the supporting context already summarized.
Example: Incident routing. We turned WA group incident messages into a structured queue of blocked work, equipment issues, gasoline needs, and personal exceptions.
Installation evidence review
We implemented this for field decisions that depended on photos of a FAT terminal, port usage, before-and-after state, or contract association.
Photo review is useful only when the agent can say what it saw and what it did not see. LULU turns the image into a structured observation and flags uncertainty before the work is accepted.
- Before photo
- After photo
- Port count
- Confidence note
- Trigger
- A supervisor asked LULU to inspect installation photos and identify terminal state, port usage, and contract association.
- Agent job
- LULU extracted visible evidence, compared before and after, and summarized what could be trusted from the images.
- Boundary
- We kept final evidence decisions blocked when the photo was cropped, blurry, incomplete, or missing terminal context.
- Count what is visible: Identify total visible ports, occupied ports, available ports, labels, and any cable intervention visible in the image.
- Compare the state: Explain how the after photo differs from the before photo and whether the change supports the requested conclusion.
- State confidence: Mark medium or low confidence when the crop prevents an exact port, contract, or terminal match.
Example: FAT evidence review. We implemented before-and-after installation evidence review so the system could summarize what was visible and what still needed verification.
Incentive anomaly audit
We implemented this where attendance or productivity bonuses depended on visit counts, installations, timing, and route evidence.
Incentive rules can create edge cases. LULU checks behavior against the approved policy and gathers the evidence a manager needs before deciding whether a pattern is legitimate or suspicious.
- Bonus policy
- Technician history
- Route evidence
- Manager review
- Trigger
- A technician's production pattern appeared optimized around the bonus threshold or inconsistent with route evidence.
- Agent job
- LULU compared work logs, route data, supervisor notes, and georeferenced visits against the incentive rule.
- Boundary
- We did not let LULU accuse, penalize, or change compensation automatically; it prepared a review packet for the manager.
- Read the rule: Translate bonus conditions into checks such as two installations, one installation plus visits, or a required visit count.
- Find anomalies: Look for isolated spikes, timing patterns, route inconsistencies, or repeated threshold behavior.
- Prepare review: Summarize the evidence, counter-evidence, and open questions for the manager who owns the decision.
Example: Bonus anomaly audit. We implemented bonus anomaly checks against technician behavior, attendance rules, route context, and review evidence.
LULU AI agent control system
LULU was allowed to analyze and propose, but the system kept sensitive operational decisions under human review. That was especially important for route changes, technician performance, compensation, and photo evidence.
- Data scope
- WA groups, production dashboards, route notes, evidence photos, and bonus policy context
- Decision rights
- Agent proposes; supervisor or owner approves sensitive action
- Metric authority
- Expected pace, real production, route comments, and approved bonus rules
- Routing policy
- Supervisor, radio operator, or owner based on issue type and urgency
- Review record
- Summaries preserve the signal, evidence, confidence, and recommended owner
- The request asks for production status or dashboard interpretation
- Answer with metrics, deltas, and context using approved operational data.
- The thread includes blocked work, missing equipment, or gasoline issues
- Create an incident summary and route it to the right human owner.
- The case involves technician performance, compensation, or possible manipulation
- Prepare an audit packet and require manager review before any action.
- Photo evidence is incomplete, cropped, blurry, or ambiguous
- State uncertainty and request verification instead of finalizing the conclusion.
build the first workflow
Start with the field decision your team repeats every day.
Bring one production question, the dashboards that define the truth, and the field messages that currently force a supervisor to connect the dots by hand.