Cwlwm Systems
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Case study 02

AI Quality Assurance & Coaching Platform

An AI-assisted quality system that turns high-volume customer or sales interactions into structured evaluations, coaching actions, management intelligence, and measurable follow-up.

Applicable to
Contact centersInside salesCustomer successInsuranceHealthcare schedulingFinancial services
01

Before

Manual review limits the percentage of interactions a manager can evaluate.

Free-form AI output is not reliable enough to become official business state without structure and validation.

Analysis, scoring, notifications, and coaching need separate failure boundaries so one issue does not break the entire workflow.

02

The system

Interactions enter a controlled processing pipeline, are validated for eligibility, analyzed into a structured schema, scored through deterministic business rules, saved as auditable evidence, and routed into coaching or follow-up workflows.

01Ingest→
02Validate→
03Analyze→
04Score→
05Route→
06Coach→
07Measure
03

Capabilities

Structured AI evaluation
Deterministic business scoring
Queue-based processing
Duplicate and retry protection
Eligibility and exclusion rules
Coaching workflows
Manager notifications
Effectiveness tracking
04

Operational outcome

1

Expand QA coverage without requiring every interaction to be manually reviewed.

2

Create more consistent evaluation evidence across reviewers and teams.

3

Turn a score into a closed-loop coaching workflow rather than a static report.

4

Keep AI inside an auditable, controlled operating process.

Case studies are presented as representative system patterns. Client-specific names, screenshots, testimonials, and measured results are only published with permission.

05

Technical foundation

The technology is selected to support the operating model, not the other way around. A system of this type may combine the following foundations:

Asynchronous workersStructured LLM outputRelational persistenceDeterministic scoringNotification workflowsOperational monitoring

Could this pattern fit your business?

Build the version that matches your workflow.

The case study shows a reusable operating pattern, not a boxed product. I can adapt the architecture to the people, data, systems, and rules already inside your business.

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Start with the problem

What is the business doing manually that it should not be?

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Technology experience

Tools I use to build.

Languages, frameworks, databases, cloud platforms, and development tools I use to design and build business systems.

Next.js
React
TypeScript
JavaScript
Python
HTML5
CSS
PostgreSQL
Supabase
Google Cloud
BigQuery
GitHub
Vercel
WordPress
Security & data practices

The stack matters. How it is configured matters more.

Business systems can contain sensitive operational and customer information. I design around practical controls such as least-privilege access, secure credential handling, authorization boundaries, environment separation, logging, and recovery planning appropriate to the project.

Read Security Practices →
01

Least-privilege access

Users and services are given only the access their role or workflow requires.

02

Secure credentials

API keys, service credentials, and environment secrets are kept out of public source and client-side code.

03

Protected data flows

Encrypted connections and appropriate authorization controls are used for production data paths where supported.

04

Environment separation

Development, staging, and production concerns are separated when the project risk and scope call for it.

05

Validation & auditability

Business rules, permissions, input boundaries, logging, and failure handling are treated as part of the system design.

06

Recovery planning

Backup, retention, and recovery responsibilities are defined according to the system and the agreed scope.

Security controls are scoped to the needs of each engagement. Formal compliance or certification requirements should be identified during discovery and documented in the applicable statement of work.