CAPAs and Freelong Health’s CAPA Engine™
CAPA Investigations
Most CAPA investigations fall into a familiar pattern:
The issue is known
The form exists
The timeline is tight
The root cause isn’t fully clear
And “human error” starts creeping in
That’s where quality breaks down. Not in the system, but in the investigation.
CAPA Engine is designed to:
Push beyond surface-level causes
Introduce structured thinking early
Highlight missing evidence
Align outputs with what auditors actually expect
Think of it like a prized junior investigator that follows best practice frameworks consistently.
A better starting point for CAPA investigations
CAPA Engine™ is an intelligent investigation system designed to help quality and regulatory teams turn raw nonconformance descriptions into structured, audit-ready CAPA records.
If you’ve ever stared at a blank CAPA form, you already understand the problem. The hardest part isn’t filling in the fields, it’s thinking through the investigation correctly, aligning to the right standard, and documenting it in a way that holds up under audit.
That’s typically why teams bring in consultants. Not because they can’t write a CAPA, but because they want confidence that the investigation, reasoning, and structure are sound.
CAPA Engine bridges that gap.
It doesn’t replace your quality system. It gives you a strong, structured starting point—built on the same frameworks experienced consultants use.
What it does
At its core, CAPA Engine takes a single nonconformance and processes it through a structured investigation pipeline:
Classification of the nonconformance (type, severity, regulatory context)
Root cause analysis using Ishikawa + 5-Why (or 8D where applicable)
Corrective action planning across multiple layers
Effectiveness checks with measurable acceptance criteria
All outputs are aligned to the selected industry and regulatory framework, not generic templates
What you get
Each CAPA generated includes:
Formal Problem Statement
Clean, audit-ready framing of the issueNonconformance Classification
Category (e.g., supplier, manufacturing, complaint)
Severity (critical / major / minor)
Regulatory context and applicable clauses
Root Cause Analysis
Ishikawa (Fishbone) breakdown across 6 categories
5-Why drilldowns
Ranked probable root causes (not just one guess)
Investigation plan and evidence gaps
Corrective Action Plan (4 Layers)
Containment
Short-term correction
Systemic corrective action
Preventive extension
Effectiveness Check Criteria
Quantitative acceptance criteria
Monitoring period
Pass/fail logic
Data sources and verification methods
Audit-Ready Export
Structured CAPA record (PDF or Word)
Mapped to common eQMS formats
The goal is not to “auto-complete” CAPAs. It’s to give you a structured, defensible draft that holds up under scrutiny.
CAPA Engine™ wasn’t built to automate quality.
It was built to support better thinking…faster.
Because most CAPA problems aren’t about tools.
They’re about structure, clarity, and getting to the real cause before it becomes a repeat finding.
CAPA Engine™ does not replace your eQMS
It generates structured investigation content that you can review, refine, and then enter into your existing system (e.g., Greenlight Guru, MasterControl, Qualio, ETQ).
CAPA Engine™ is not ChatGPT for CAPAs
CAPA Engine™ uses a sophisticated structured, multi-step architecture built specifically for CAPA workflows, including classification, root cause logic, corrective action structuring, and effectiveness validation.
It adapts outputs based on industry frameworks (e.g., ISO 13485 vs IATF 16949 vs GMP), which generic tools do not do.
Will an auditor accept this?
Auditors don’t evaluate who wrote the CAPA, they evaluate:
Was the root cause appropriately investigated?
Do actions address the actual cause (not symptoms)?
Are effectiveness checks measurable and justified?
CAPA Engine is designed to structure outputs around those expectations.
However, all outputs should be reviewed and approved by your quality team.
What about data security?
Data is encrypted in transit and at rest
CAPA records are stored in managed infrastructure (e.g., Supabase/PostgreSQL)
Your data is not used to train external AI models
You maintain control over your records, including deletion
The system is designed for professional use in regulated environments, where confidentiality and data integrity are expected.