Introducing Qualigon

Starting With Conversations

When we started Qualigon, we spoke with more than 150 quality professionals and manufacturing leaders across pharma, biotech, and CDMOs — deviation investigators, QA managers, site quality heads, QC scientists, batch record reviewers, process engineers, and many more. The conversations spanned manufacturing environments from small 50-person CDMOs to large-cap pharma companies including Novo Nordisk, Amgen, and Merck.

We asked them what a typical deviation investigation looked like. How long it took. Where time went. What kept batches on hold. What tools they used. What they wished they had.

The answers converged faster than we expected.

The Problem That Keeps Coming Back

Deviation investigations in GMP manufacturing are among the most knowledge-intensive, time-sensitive tasks in quality operations. When a batch parameter goes out of specification or a process deviation is observed, an investigation must be opened, documented, and closed before the batch can be released. 21 CFR and EU GMP require it — a deviation sitting in the backlog means no batch going out of the door.

Although not prescribed by regulations, ideally a deviation would be closed within 14 to 30 days, depending on the severity and complexity of the deviation.

In practice, many take longer.

Performing a root cause analysis is time-consuming

Here is what the first part of a deviation investigation looks like at most sites:

• Pull the batch record
• Locate the relevant SOPs and technical documentation
• Interview SMEs and operators on the production floor
• Assess the risk level of the deviation

After the initial assessment, the case proceeds to formal root cause analysis:

• Analyze relevant process parameters from historian data
• Check maintenance and calibration logs for the equipment involved
• Cross-reference supporting systems (LIMS, MES, etc.)

This data lives in fragmented systems, none of which talk to each other. Assembling it is entirely manual, and by the time it is done, more than a day has passed before anyone has written a sentence of analysis. Then comes the iterative work of applying frameworks like Ishikawa or FMEA to identify the root cause — followed by a 2,000–8,000 word investigation report that must be structured, evidenced, and defensible under inspection.

It requires deep process expertise to do it well

Completing a high-quality RCA that holds up under an FDA or EMA audit is not just a documentation exercise. It requires knowing which failure modes are plausible for this type of process, which data points actually matter, how to scope a Phase I versus Phase II OOS, and how to build a logical evidence chain from raw data to a defensible conclusion. That knowledge takes years to develop. It lives in experienced investigators — and it is not evenly distributed across a team. A junior investigator assigned a complex deviation has the same obligation and the same blank page, with a fraction of the context.

Where the Existing Tools Fall Short

There are three categories of tools that touch this problem. None of them solve it.

QMS platforms — Veeva, MasterControl, TrackWise and their equivalents — are essential infrastructure. They provide the documentation backbone for GMP manufacturing. But they are documentation tools, not reasoning tools. They capture what happened: deviation opened, assigned, closed, CAPA linked. They do not help you understand why it happened. There is no root cause analysis draft assistant in a QMS. There is no data synthesis. The system knows the deviation exists. It does not know the nuance of the batch record, what the historian recorded at the relevant timestamps, or which failure mode is most plausible.

Manual workflows work in the hands of experienced investigators. A QA professional with fifteen years on the same type of line knows which historian tags to pull, which operator to call first, and which failure modes to rule out immediately. But that knowledge is not documented, not transferable, and not consistently applied across a team. When a less experienced investigator opens a complex deviation, there is no structured guidance. The quality of the output depends heavily on who happens to be assigned that day.

Generic AI tools like ChatGPT are used informally across the industry — for writing structure, organizing thoughts, and thinking through hypotheses. But almost no company allows production data to be submitted to external models, and most explicitly prohibit it. The appetite is there; a safe, compliant path isn't.

The problem runs deeper than data policy. Generic AI has no regulatory grounding. It doesn't know what 21 CFR requires, how FDA guidance defines the Phase I / Phase II OOS structure, or what your SOPs say. It is a general-purpose tool being asked to do specialized work it was never built for.

What We're Building

Qualigon is an AI-assisted investigation platform built specifically for GMP-regulated manufacturing environments. It exists to do one thing: help investigators close deviation investigations faster, more completely, and in a form that is defensible under inspection.

The mission is not to replace the investigator. Human oversight is not a feature — it is a regulatory necessity and a scientific one. AI systems cannot sign a deviation report. A qualified QA professional reviews and approves every conclusion. Our job is to make that approval possible in hours rather than days.

The design principle that underpins everything: regulations are not a constraint to work around. They are the foundation. When we designed how Qualigon structures an investigation, we started from FDA 21 CFR, from the OOS investigation guidance framework, from EU GMP and Annexes, from the ICH risk management logic. The system is not general-purpose AI with a GMP label applied afterward. It is an investigation workflow built from the regulatory framework up.

The ultimate reason this matters is not cycle time. It is that a batch sitting on hold is a product that has not reached the patient who needs it. Faster, more defensible investigations accelerate batch releases. That is the outcome we are building toward.

How Qualigon Works

When a deviation is opened, Qualigon begins by assembling the relevant context automatically. It connects to your existing data sources — QMS, historian, LIMS, MES, maintenance logs — and pulls what is relevant to the specific deviation at hand. The investigator does not have to request historian tags, locate the applicable SOP, or cross-reference LIMS manually. That assembly happens before the investigation begins.

From there, Qualigon guides the investigator through a structured root cause analysis. It identifies what evidence exists, what is missing, what hypotheses the data supports, and what questions still need physical or laboratory follow-up. It is not prescriptive about methodology — it works within your SOP's investigation framework — but it surfaces the right questions at the right time and flags when the analysis has gaps.

The output is a structured deviation investigation report, grounded in the actual batch data and site SOPs, drafted in a format ready for QA review. The investigator reviews, adjusts, and approves. That report is then loaded directly into your QMS as a Word document — it moves into your existing documentation process without friction.

Every step is logged. Every AI-assisted action is timestamped and traceable to its source. The audit trail is defensible under inspection.

What Working With Us Looks Like

We are in early deployment with a small number of customers. At this stage, we work directly with each site — 1:1.

That means we configure the system to your SOPs and investigation procedures, not a generic template. We connect securely to your real data sources: your historian, your QMS, your LIMS. We define together what success looks like for your site — whether that is investigation cycle time, CAPA quality, first-pass approval rate, or something else — and we measure it.

We are not selling just a software license. The sites we work with are true partners, and we treat the engagement that way.

If you work in quality at a pharma or CDMO site and what we have described sounds like a problem you recognize, we would like to talk. Not to pitch — to understand what you are dealing with.