The audit that found nothing wrong.

An FDA auditor points to a deviation from six months ago: "Show me what happened here."

You pull up DEV-2024-047. The screen shows the batch step where it occurred, the equipment status at that moment, the operator's training record, and the investigation trail. The auditor clicks through to the CAPA. It closed three months ago after effectiveness verification—zero recurrence in the following 50 batches, data attached.

The auditor moves on. No finding. No 483. This is what control looks like when you can prove it.

The documentation problem

Deviations in one system. Training records in another. Batch records in a third. To answer "was this operator trained when they executed this step during this deviation," someone assembles evidence from three sources. The answer arrives in hours, if the data exists at all.

The average 483 observation costs $200K-$500K to remediate. Warning letters cost $1-5M and take 12-18 months to resolve. Consent decrees can run into the hundreds of millions. None of this happens because someone did something wrong. It happens because someone couldn't prove they did something right.

This is how minor deviations become 483 observations. Not because of bad intent, but because the system couldn't prove control when asked.

Connected records

A deviation record in Seal contains the batch ID, equipment ID, process values, and operator training status at the time of the event—because those records already exist in the same database. The connection isn't assembled after the fact. It's structural.

When an auditor asks to see a deviation, they see it linked to the specific batch step, equipment calibration status at that moment, and the operator's verified training. Complete chain of evidence, retrieved in seconds.

Document control and training

An SOP is a controlled object, not a file. Operators access only the current effective version. Superseded versions are archived and inaccessible for execution.

When SOP-042 updates from version 2 to version 3, everyone trained on version 2 gets retraining assigned automatically. Until they complete training on version 3, they cannot execute tasks requiring that SOP. Training is a gate, not just a record—the system blocks execution before errors can occur.

21 CFR Part 11 compliance: immutable audit trails, meaning-based e-signatures (Author, Review, Approve), complete version history.

Deviation and CAPA management

Deviations capture in context. The record includes batch ID, equipment ID, and process values at the time of the event—not because someone typed them in, but because that data already exists in the same system.

"Inadequate CAPA" appears on warning letters because organizations close CAPAs on paperwork, not proof. Seal requires effectiveness criteria upfront: "Zero recurrence in the next 100 batches." The CAPA cannot close until verification is complete and evidence is attached. If verification fails, the CAPA reopens automatically.

Integrated quality events

Quality threads connect across systems. An OOS result triggers investigation workflow immediately. Deviations link to batches. Batches link to materials. Materials link to suppliers. When an auditor follows a thread, it never breaks.

Annual Product Quality Reviews compile from live data. The data exists because it was captured during execution. There is no preparation phase—audit readiness is the default state.

AI That Builds Your QMS

The hardest part of a QMS isn't using it—it's setting it up. Defining workflows. Writing SOPs. Configuring deviation categories. Creating training curricula. Traditional implementations take months because consultants manually configure every piece.

AI changes this fundamentally. Describe what you need in plain language: "We manufacture sterile injectables with three production lines and need to track deviations, CAPAs, and change control per FDA requirements." AI generates the configuration—deviation categories appropriate for injectable manufacturing, CAPA workflows with effectiveness verification, change control classification matrices.

SOP generation accelerates dramatically. AI drafts procedures from your process descriptions and regulatory requirements. "Write an SOP for aseptic gowning per EU Annex 1." AI generates a compliant draft with the required content structure. Your team reviews and refines rather than writing from scratch.

Training curricula build themselves. AI analyzes your SOPs and role definitions to create training matrices—who needs training on what, in what sequence, with what assessment criteria. Add a new SOP, and AI identifies who needs training based on their responsibilities.

Form design becomes conversational. "I need a deviation form that captures batch ID, equipment involved, process step, immediate actions taken, and initial classification." AI creates the form with appropriate field types, validation rules, and workflow triggers.

The system that used to take six months to configure is operational in weeks. Not because corners are cut—the configuration is just as thorough—but because AI handles the repetitive work of translating requirements into system configuration. Your quality team focuses on decisions; AI handles implementation.

Every AI proposal is transparent and reviewable. When AI drafts an SOP, you see exactly what it's proposing—every section, every step, every requirement. Configuration changes go through your standard review and approval process. New entities, updated workflows, revised procedures—all follow controlled change management. AI accelerates the work; humans own the decisions.

And AI doesn't stop at setup. Call it from within any workflow. A deviation opens—AI suggests root causes based on similar events. You see the suggestions and the reasoning; you decide what to investigate. A CAPA needs an action plan—AI drafts it from the investigation findings. You review, edit, approve. A batch record needs review—AI highlights anomalies so QA focuses on what matters. Every AI output is visible, editable, and subject to your approval workflow.

From Overhead to Prevention

Quality departments are often seen as cost centers. The team that creates paperwork. The department that says no. Every dollar spent on quality is a dollar that didn't go to R&D or sales.

This perception persists because traditional QMS systems are reactive. They document what went wrong. They don't prevent it. When the value of quality is "we wrote good reports about failures," quality looks like overhead.

Seal changes the equation. Training gates prevent untrained operators from touching critical steps—that's not documentation, that's prevention. Real-time deviation capture means problems surface before they become 483s. Effectiveness verification means CAPAs actually work. When quality prevents the $500K remediation instead of documenting it, quality becomes an asset.

The QA team that can answer any auditor question in seconds isn't overhead. The system that catches drift before excursion isn't bureaucracy. The training that blocks unqualified execution isn't paperwork.

Quality should be the department that protects the company. That requires a system designed for protection, not archaeology.

Getting Started

Most customers are migrating from either paper, SharePoint, or a legacy eQMS that stopped working for them. All three paths are different.

If you're on paper or SharePoint, the path is straightforward: define your document hierarchy, import your current SOPs, and start capturing quality events digitally. Training management can follow within weeks. You don't need everything configured to go live—start with what you use most, expand from there.

If you're on a legacy eQMS, you have a choice. You can run parallel for a period while you migrate critical data, or you can archive the old system as read-only and start fresh. Most customers choose the second path. You don't need to migrate 10 years of closed CAPAs—you need to manage the next 10 years properly.

Either way, the timeline is weeks, not months. Document control typically takes 1-2 weeks to configure. Deviation and CAPA management adds another 1-2 weeks. Training integration follows. AI accelerates all of this—what used to require consultants now requires descriptions.