Fiserv Hospitality Operations | BPO Customer Support | AI-Assisted Workflow

How a Manual Quality Process Became the Blueprint for TrueReply

This case study shows how a fragmented conversation-review process was transformed into a daily operating system built around structured data, measurable quality, customer-risk detection, modular business rules, and human validation—and how that proven manual framework became the directional blueprint for an AI-enabled solution.

The central lesson was simple: AI does not eliminate the need for process discipline. It rewards it. By first understanding the data, defining the workflow, measuring the baseline, and iterating manually, the team created a reliable foundation that AI can now accelerate at scale.

The problem behind the problem. High-volume customer conversations were generating valuable operational evidence every day, but the evidence was trapped in fragmented review habits, inconsistent data structures, and reactive escalation. That made it difficult to see risk early, coach consistently, or translate interaction patterns into repeatable improvement.

How TrueReply addresses it. The solution turns conversation data into a governed operating loop: normalize the input, parse who said what, apply modular business rules, score risk and confidence, route exceptions to human review, and feed the resulting insight into daily KPI management and continuous improvement. AI accelerates the system without removing operational control.

Structured CRM DataConversation ParsingRisk ProfilingModular Rule SetsHuman-in-the-Loop QADaily KPI AccountabilityThree-Week Sprint

Executive Storyline

  • Define the operational problem and establish one source of truth
  • Structure raw conversation data so AI can interpret it consistently
  • Build and refine the process manually before automating it
  • Use modular rules, templates, risk indicators, and confidence review
  • Elevate associates into Customer Excellence contributors
  • Measure customer outcomes, quality, efficiency, and learning velocity
  • Scale through a controlled three-week TrueReply sprint
  • Normalize raw conversation exports into a consistent structure that preserves participants, chronology, metadata, and customer context
  • Separate reusable business rules, exception logic, and customer-risk criteria from one-off prompt instructions
  • Create a daily operating cadence that ties reviewed conversations to measurable quality, coaching, risk, and process decisions
  • Measure whether accepted observations and rule improvements reduce future exceptions, rework, and repeat-contact risk
  • Scale automation progressively only after the manual workflow, validation standards, and human escalation controls are proven

1. The Problem

The team supported web-based edits and updates for hospitality clients, predominantly restaurants. Customer conversations lived in Front, but review and accountability were fragmented. Leaders lacked a consistent daily baseline, a common method for identifying risk, and a scalable way to convert conversation insight into coaching, process improvement, and better customer outcomes.

Reactive State
FragmentedConversation ownership and review
InconsistentCoaching and quality evaluation
LimitedVisibility into customer risk
UnstructuredCRM output for reliable AI use

Operational Conditions

  • No single source of truth for the previous day’s conversations.
  • Unclear ownership over who was reviewing which interactions.
  • Customer risk often surfaced after escalation rather than before it.
  • Coaching depended too heavily on isolated examples.
  • Process decisions were harder to connect to daily evidence.

AI Readiness Conditions

  • Raw Front exports were not consistently formatted for GPT analysis.
  • Conversation boundaries, participants, metadata, and message sequence required normalization.
  • A large prompt alone could not reliably compensate for inconsistent input structure.
  • Business context needed to be separated into reusable rule modules rather than embedded in one monolithic instruction set.

2. What Was Done

The team created a daily conversation-intelligence operating model. The process exported prior-day conversations, converted them into a predictable structure, parsed customer and Fiserv messages, evaluated them through modular rules, assigned risk and confidence indicators, and fed the findings into the morning stand-up for accountability and action.

Operating Model
Conversation ExportPrior-day Front data
Data PreparationConsistent AI-ready layout
Conversation ParserCustomer vs. Fiserv
AI InterpretationSentiment, intent, recap
Business RulesRisk, policy, required language
Human ValidationOutliers and coaching
Daily ImprovementProcess, prompt, modules

Single Source of Truth

Every morning began with the same prior-day dataset, the same KPI view, and the same method for identifying exceptions.

Risk-Based Review

Higher-risk or lower-confidence conversations were prioritized for manual review instead of treating every interaction equally.

Continuous Learning

Each outlier became an opportunity to refine the data structure, parser, prompt, rule set, template, coaching, or process.

3. How It Was Done

The methodology followed a practical continuous-improvement sequence: understand the process, stabilize the data, measure the baseline, refine the rules, validate exceptions, and only then design the automation test.

Manual First
1
Understand the Data

Mapped the CRM output, message sequence, participants, metadata, and formatting conditions that affected AI comprehension.

2
Structure the Input

Created a predictable conversation layout so the model could focus on meaning instead of deciphering inconsistent formatting.

3
Separate the Functions

Kept conversation parsing distinct from recap, sentiment, scoring, and response guidance so each part could be refined independently.

4
Build Modular Rules

Added reusable logic for cancellation, escalation, frustration, repeated requests, required language, risk conditions, and other scenarios.

5
Score Risk and Confidence

Directed manual review toward conversations with higher risk, lower confidence, or unusual edge cases.

6
Iterate Daily

Used stand-up review, one-on-one remediation, trend analysis, and prompt refinement to tighten performance over roughly four to six weeks.

Why the Architecture Matters

AI was intentionally assigned the work it performs well, while business rules and human judgment governed policy, context, risk, and quality.

Layered Design

AI Interprets Language

  • Sentiment and tone
  • Conversation recap
  • Customer intent
  • Unanswered questions
  • Draft response language

Rules Apply Business Context

  • Cancellation and manager requests
  • Repeated customer contacts
  • Frustration and urgency
  • CSAT, NPS, or LTR risk
  • Required messaging and next steps
  • Supervisor review conditions

People Protect the Customer

  • Validate that nothing was missed
  • Correct the output before sending
  • Identify meaningful exceptions
  • Improve modules and templates
  • Coach and remediate recurring behaviors

4. What Was Reviewed and Measured

The daily stand-up created a disciplined operating cadence. The team reviewed prior-day KPIs, sentiment and conversation intelligence, outliers, root causes, remediation, process changes, and AI-rule improvements. Measurement expanded from traditional productivity into customer excellence, AI quality, and organizational learning.

KPI Framework
Measurement AreaExample KPIsWhy It MattersStatus
Customer ExperienceCSAT, NPS/LTR risk recovery, escalations, repeat contactsConfirms whether the experience is improving from the customer’s perspective.Core
Operational Health31+ day conversations, aging cohorts, replies per resolution, time to resolutionShows backlog health, rework, and where conversations require too many cycles.Outcome
AI QualityAcceptance rate, edit rate, critical misses, risk detection accuracy, template complianceMeasures whether TrueReply is accurate, complete, consistent, and safe to use.Test
Associate ExcellenceQuality validation, high-risk catches, accepted improvement observations, meaningful rule contributionsAligns associate performance more directly to customer quality and enterprise improvement.People
Learning VelocityObservation-to-production time, approved changes, recurrence rate, module deployment speedMeasures how quickly the operation can learn and convert frontline insight into better future outcomes.Emerging

Daily Accountability Rhythm

The stand-up became the control mechanism connecting data, people, process, and AI improvement.

Daily Control

1. KPI Review

  • Previous-day performance
  • Top-level metrics
  • Outliers and trends
  • Root-cause analysis

2. Conversation Intelligence

  • Sentiment and risk
  • High-priority conversations
  • Coaching opportunities
  • Prompt and rule refinement

3. Action and Control

  • Assign ownership
  • Remediate one-on-one
  • Modify process or modules
  • Monitor for recurrence

5. Results

The manual operating model produced sustained gains before TrueReply automation. These outcomes established confidence that the process was directionally sound and gave the team a measurable baseline for the controlled AI test.

Proven Baseline
Support CSAT
100%
More than 60 consecutive days of positive day-to-day support feedback
31+ Day Conversations
0%
Reduced from an average baseline of 3.8%
Sustained Aging Control
46+ Days
No conversations crossing the 31-day threshold
Prompt Maturity
~5 Weeks
Reached a state where only infrequent edge cases required meaningful edits

Final presentation update: replace the duration and response-count language above with the exact verified totals before presenting. The current values reflect the project narrative supplied during development.

The manual process did not merely prepare the team for automation. It created the baseline, identified the most meaningful levers, proved the daily control model, and showed which KPIs the AI test should attempt to improve further.

6. The Associate Story: From Ticket Handler to Customer Excellence Contributor

TrueReply changes what high performance means. The AI prepares a structured recap and draft response; the associate applies judgment, validates completeness, protects quality, identifies risk, and contributes improvements that can benefit every future interaction using the same module.

Human + AI

Traditional Contribution

  • Draft each response from scratch
  • Resolve the ticket in front of them
  • Performance centered on throughput
  • Knowledge often remains individual

Customer Excellence Contribution

  • Validate TrueReply for completeness and precision
  • Catch customer risk before the response is sent
  • Submit structured improvement observations
  • Improve a reusable module or template
  • Scale individual judgment across future conversations

A great associate no longer improves only the ticket they personally handle. One accepted observation can improve thousands of future customer interactions.

Structured Associate Feedback Loop

1
Observe

Associate identifies a missed detail, rule conflict, new risk pattern, or template opportunity.

2
Submit

The observation enters a defined escalation and ticketing process with source and classification tracking.

3
Review and Vet

Operations and the KMS/Crescendo team determine whether the issue requires a rule, template, prompt, process, or training change.

4
Deploy

The modular architecture allows approved changes to cascade into the workflow quickly—targeting approximately one business day where feasible.

5
Measure Impact

The team tracks quality, recurrence, customer outcomes, and time from observation to production.

Emerging Associate KPIs

  • Accuracy of AI validation
  • Critical customer risks identified before send
  • Meaningful observations accepted
  • Rule or template improvements implemented
  • Measured reduction in exception recurrence
  • Impact of an improvement across future conversation volume
  • Quality contribution to CSAT, rework, and repeat-contact reduction
Guardrail: measure accepted, validated, and outcome-linked contributions—not raw suggestion volume—to avoid incentivizing unnecessary changes.

7. The TrueReply AI Model

The Crescendo KMS team is developing TrueReply using its own technical framework while incorporating the operational lessons proven through the manual process: modular rules, templated inclusions, risk tagging, measurable exceptions, and a repeatable feedback loop.

AI Enablement

Parse and Understand

Review the conversation history, distinguish customer and Fiserv messages, identify unresolved questions, detect tone and intent, and produce a usable recap.

Apply Context and Standards

Use modular business rules and templated language for cancellation, escalation, frustration, repeated contacts, cache-related edits, next steps, and other defined scenarios.

Assist and Escalate

Draft the response, flag customer or ticket risk, recommend supervisor involvement, and identify conversations for additional review and measurement.


TrueReply is not designed as an autonomous replacement for associate judgment. It is a governed assistance layer that provides speed, consistency, and scale while people provide empathy, creativity, context, and final accountability.

The Continuous Learning Flywheel

Every validated output builds confidence. Every exception improves the framework. Every deployed improvement strengthens the next customer interaction.

Scalable Learning
Customer Conversation
TrueReply Output
Associate Validation
Observation Logged
Module Updated
Change Deployed
Better Future Outcomes

8. Next Steps: Three-Week Controlled Sprint

The test is intentionally short, measurable, and controlled. Two initial associates become core template experts, their work is cohorted separately from the broader operation, and the team measures both business outcomes and the speed of learning.

Three Sprints
Sprint 1 | Week 1

Establish and Validate

  • Launch with two core associates
  • Train on validation and escalation
  • Separate test cohort data
  • Measure edits, misses, acceptance, and risk flags
  • Capture initial module and template observations
Sprint 2 | Week 2

Refine and Expand

  • Vet and deploy approved improvements
  • Track observation-to-production time
  • Compare test cohort to baseline
  • Refine templates and rule modules
  • Expand controlled participation as quality allows
Sprint 3 | Week 3

Prepare to Scale

  • Confirm training and QA standards
  • Finalize dashboard and reporting
  • Document governance and ownership
  • Assess KPI movement and readiness
  • Recommend rollout to all associates

Test Questions

The sprint is designed to learn, not to assume. It will determine how much additional improvement AI enablement can create beyond the proven manual baseline.

What Needs to Be Learned

Quality

  • Does TrueReply capture all customer questions?
  • Does it apply required messaging consistently?
  • Does it accurately identify risk?

Efficiency

  • Does it reduce drafting and clarification effort?
  • Does it reduce replies per resolved conversation?
  • Does it improve cycle-time cohorts?

Learning

  • How quickly are observations reviewed?
  • How quickly can approved changes reach production?
  • Do exceptions recur after a rule change?

9. Executive Takeaway

The project is not simply a response generator. It is a repeatable operational learning system that combines structured data, process discipline, modular business logic, measurable outcomes, and frontline expertise.

Replicable Model
AI was not used to replace a process. Operational discipline was used to build a process that AI could reliably accelerate.

People

Associates provide judgment, empathy, creativity, accountability, and the observations that improve the system.

Process

Daily review, root-cause analysis, modular governance, and measurable controls keep the operation aligned to customer outcomes.

AI

TrueReply provides speed, consistency, language understanding, scalable application of rules, and a faster path from learning to execution.


The larger opportunity is cultural: make excellence visible, make frontline expertise reusable, make improvement measurable, and give every associate a meaningful role in strengthening the customer experience.
🚀
Founder / Operator-Builder

About Peter

Peter DeCaro
Peter DeCaroSenior AI Architect | Operations Transformation & Workflow Optimization Expert
Management and Strategy Institute Continuous Improvement Professional Award Winner

Peter DeCaro, currently Senior AI & Business Operations Consultant at Vantage Solutions Group, is an operations and technology-focused product builder with more than 25 years of experience improving, automating and scaling complex business operations. Across his career, he has worked for and with eight publicly traded companies and has operated at the intersection of customer operations, revenue operations, process improvement, technology implementation and organizational scale. His experience includes leadership and transformation work associated with companies including Fluent, LLC, IAC Applications, AOL and KIT Digital, as well as consulting and product-development work through Vantage Solutions Group and Vantage Product Labs.

His career has consistently centered on a practical question that now sits at the heart of MyRocket Studio: how can technology remove operational friction, create repeatable decision systems and allow people to produce better outcomes with less manual work? Long before generative AI became a mainstream operating tool, that work included process redesign, workflow automation, KPI governance, CRM and ERP implementation, customer-success operating models, vendor and workforce management, executive reporting and the rapid stabilization and scaling of growing businesses.

Peter has overseen revenue operations in excess of $50 million annually, built programs supporting customer-success and service teams of approximately 50 to 100 people, and led operational improvement initiatives across high-volume, technology-enabled organizations. His broader operating background includes large-scale customer experience environments, offshore and multi-site operations, fulfillment and service transformation, sales and revenue operations, automation, performance management and executive-level operating cadence. He is Six Sigma / Lean Six Sigma trained and has spent much of his career applying continuous-improvement principles to real operating environments rather than treating process design as an academic exercise.

In 2023, Peter was recognized by the Management and Strategy Institute (MSI) for continuous improvement, reflecting a career built around measurable operational change. That discipline has increasingly been applied to software and AI-enabled product development: translating operating problems into modular applications, measurable workflows and repeatable systems.

Most recently, through Vantage Product Labs, Peter has focused on building practical AI-enabled applications and reusable product engines. Those projects include a flight-monitoring application designed to continuously track fare changes across travel providers; ResumeRocketPro, an ATS-oriented resume analysis, scoring and optimization platform; KDP AI Secrets, a structured information-product and publishing asset creation system; and the broader MyRocket Studio / RocketCore architecture described in this document.

These products reflect a consistent operator's perspective: software should not merely generate output—it should organize work, preserve evidence, reduce repetitive decisions, create quality controls and make the next operating cycle better than the one before it. MyRocket Studio is the culmination of that approach, combining Peter's background in operational transformation with hands-on AI-assisted product development to create a modular system for moving from market evidence to commercially testable assets and then back to measurable learning.

Original Source Profile Context — Preserved Verbatim
Founder / Operator-Builder | Senior AI & Business Operations Consultant | Operations, Automation, Continuous Improvement, and AI-Enabled Product Development
Operator + Builder

Senior AI & Business Operations Consultant | Vantage Solutions Group

Peter DeCaro, currently Senior AI & Business Operations Consultant at Vantage Solutions Group, is an operations and technology-focused product builder with more than 25 years of experience improving, automating and scaling complex business operations. Across his career, he has worked for and with eight publicly traded companies and has operated at the intersection of customer operations, revenue operations, process improvement, technology implementation and organizational scale. His experience includes leadership and transformation work associated with companies including Fluent, LLC, IAC Applications, AOL and KIT Digital, as well as consulting and product-development work through Vantage Solutions Group and Vantage Product Labs.

His career has consistently centered on a practical question that now sits at the heart of MyRocket Studio: how can technology remove operational friction, create repeatable decision systems and allow people to produce better outcomes with less manual work? Long before generative AI became a mainstream operating tool, that work included process redesign, workflow automation, KPI governance, CRM and ERP implementation, customer-success operating models, vendor and workforce management, executive reporting and the rapid stabilization and scaling of growing businesses.

Peter has overseen revenue operations in excess of $50 million annually , built programs supporting customer-success and service teams of approximately 50 to 100 people , and led operational improvement initiatives across high-volume, technology-enabled organizations. His broader operating background includes large-scale customer experience environments, offshore and multi-site operations, fulfillment and service transformation, sales and revenue operations, automation, performance management and executive-level operating cadence. He is Six Sigma / Lean Six Sigma trained and has spent much of his career applying continuous-improvement principles to real operating environments rather than treating process design as an academic exercise.

In 2023, Peter was recognized by the Management and Strategy Institute (MSI) for continuous improvement, reflecting a career built around measurable operational change. That discipline has increasingly been applied to software and AI-enabled product development: translating operating problems into modular applications, measurable workflows and repeatable systems.

Most recently, through Vantage Product Labs, Peter has focused on building practical AI-enabled applications and reusable product engines. Those projects include a flight-monitoring application designed to continuously track fare changes across travel providers; ResumeRocketPro , an ATS-oriented resume analysis, scoring and optimization platform; KDP AI Secrets , a structured information-product and publishing asset creation system; and the broader MyRocket Studio / RocketCore architecture described in this document.

Professional Development / AI / Continuous Improvement

Certifications

Peter's certifications reflect the two disciplines that converge in MyRocket Studio: formal continuous-improvement methodology and hands-on development of AI-enabled operating systems. The combination supports an operator-builder approach in which automation, process control, prompt engineering, AI agents and production application design are treated as connected capabilities rather than isolated technologies.

Professional Development & CertificationsContinuous Learning
Six Sigma / Lean Process Excellence
Six Sigma Black BeltContinuous Improvement / Process Excellence
Six Sigma Green BeltContinuous Improvement / Process Excellence
Six Sigma Yellow BeltContinuous Improvement / Process Excellence
Lean Six SigmaLean + Six Sigma Process Improvement
AI, Prompt Engineering, Agents & Application Development
Prompt Engineering CertificationQuantum Leap Academy
No-Code AI Prompting: Websites and ApplicationsUdemy
OpenAI Codex Full Course 2026: AI Coding, Automation, AgentsUdemy
OpenAI Codex Masterclass: Build Your AI Operating SystemUdemy
Advanced Master AI Prompt EngineeringUdemy
ChatGPT for Customer SupportGreat Learning
Building AI Voice Agents for ProductionDeepLearning.AI
ChatGPT Prompt Engineering for DevelopersDeepLearning.AI
Academy Accreditation - AI Agent FundamentalsDatabricks Academy
Generative AI FundamentalsDatabricks Academy
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Development / AI / Data / Delivery Stack

Technologies Utilized

Project-specific additions for TrueReply are shown first below; the complete Vantage Product Labs technology inventory from the authority document is preserved after them.

MyRocket Studio and its predecessor applications have been developed through a deliberately mixed technology stack: frontier AI models for reasoning and generation; AI-assisted development environments for implementation; structured web, database and hosting technologies for production applications; source-control and workflow systems for disciplined build management; and modular data-acquisition providers for RocketCapture and RocketIQ research workflows.

Project-Specific Operating Technologies
FrontCustomer-conversation source and operational workspace used by the case-study workflow.
Microsoft ExcelBaseline analysis, reconciliation, structured review, measurement and exception handling.
Google ChromeBrowser environment for standalone review interfaces and web-based operating workflows.
LLM / AI Models & AI Development
GPT / OpenAIAI reasoning, generation, analysis and multimodal workflows
Claude / AnthropicAI-assisted architecture, coding, review and long-context development
Gemini / GoogleMultimodal AI reasoning and Google-connected development workflows
Grok / xAIAI research, reasoning and comparative model workflows
DeepSeekAI model experimentation, reasoning and technical workflows
MoonlitAI / development experimentation and supporting workflow tooling
CursorAI-assisted software development and codebase implementation
BoltRapid AI-enabled application prototyping
LovableRapid product/UI prototyping and application experimentation
Automation & Orchestration
MakeVisual workflow automation and systems integration
n8nWorkflow orchestration, API automation and agentic process integration
ZapierSaaS workflow automation and event-driven integrations
Research, Data & Provider Layer
Official APIsStructured source access where supported
ApifyModular scraping and web-data acquisition
RapidAPIExternal API marketplace and provider integration
Bright DataCommercial web-data infrastructure and acquisition
OxylabsCommercial proxy and web-intelligence infrastructure
MySQLRelational application and analytics data storage
Application Engineering & Delivery
PythonCore application logic, automation and data processing
StreamlitInteractive Python application interfaces
PHPServer-side web application and hosting workflows
HTML5Standalone interfaces, reports and product experiences
CSSResponsive interface styling and visual systems
JavaScriptBrowser-side behavior and interactive experiences
GitHubSource control, repositories, versioning and deployment workflows
GitLocal and remote source-version management
HostingerWeb hosting, databases and production deployment
Workspace, Campaign & Operating Tools
Google WorkspaceDocs, Sheets, Drive and collaborative operating files
Google DriveShared artifacts, build packages and project continuity
GetResponseEmail marketing and campaign-delivery workflows
TrelloWorkflow/board orchestration and build-task management

Product and company marks are shown for technology-identification purposes. Availability and use vary by product module and build stage.