Software engineers reviewing GxP validation documentation for a regulated life sciences customer

GxP Enablement for Software and AI Vendors

Your product works. Then a pharma customer asks for your validation package, your SDLC procedures, and proof you follow them. We build the quality system, the evidence, and the pharma-ready documentation that gets you through their review.

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How We Get You Pharma-Ready

A four-step path from an unanswered supplier questionnaire to a validation package your customers can leverage. Each step produces something you can hand to a prospect, not just an internal deliverable.

Assess

We map your product against the regulations it actually touches, review what documentation and process you already have, and produce a gap analysis with the effort and sequence to close it.

CSV assessment

Build

We put the quality system in place: the SOPs, the controlled development lifecycle, change control, release management, and defect handling that produce evidence as a by-product of how you already work.

Program development

Package

We assemble the customer-facing set: intended use, risk assessment, requirements traceability, test evidence, and the qualification protocols your customer can leverage instead of rebuilding.

System validation

Sustain

We stay on through your first supplier audits and keep the package current as you ship. Compliance decays quietly, and a package that describes last year's product is a finding waiting to happen.

Ongoing advisory

It Starts With a Questionnaire You Cannot Answer

Almost nobody arrives here through a regulation. They arrive through a customer. A pharmaceutical prospect moves from interested to serious, hands the deal to quality assurance and procurement, and a supplier questionnaire lands in your inbox asking for your validation package, your software development lifecycle procedures, your change control process, and your most recent audit results. The engineering is not the problem. The product already does what it promises. The problem is that a regulated buyer cannot purchase confidence, only evidence, and nothing in your organization was built to produce it.

The questions themselves look deceptively simple. Do you have documented requirements traceable to test cases? Who approves a release, and where is that approval recorded? How do you classify and handle defects found in production? What is your process for validating a change before it reaches a customer environment? Can you demonstrate that the procedure you just described was actually followed for the last three releases?

That final question is the one that separates vendors who pass from vendors who stall. Producing a policy document is a weekend of work. Producing eighteen months of records showing the policy was followed consistently is not something you can retrofit, which is why the sequencing of this work matters more than its volume. Teams that start when the questionnaire arrives are already six months behind teams that started when they decided pharma was a target market.

The commercial cost is rarely visible on a pipeline report, because deals in this state do not get marked lost. They get marked as still evaluating. The technical champion inside the customer still wants your product. Quality has simply not cleared it, and quality has no deadline. We have watched vendors sit in that state for three quarters while a competitor with a thinner product and a thicker binder took the account.

Vendor team reviewing a pharmaceutical customer supplier qualification questionnaire

GxP Is Elaborate, and Everyone Underestimates It

The consistent pattern across every vendor we have worked with is a serious underestimate of scope on first contact. GxP is not a checklist or a certification you obtain. It is an interlocking system of documented procedures, the training that proves people know them, the records that prove the procedures were executed, and the periodic review that proves the whole thing still reflects reality. If you have not spent years inside a regulated quality organization, there is no reasonable way to intuit how deep it goes. Most teams budget for a document and discover they need an operating model.

Consider what a single controlled change actually requires once you are inside a quality system. The change is requested and recorded. Its impact is assessed against validated functionality, and that assessment is documented and approved by someone qualified to make the call. Testing is specified in advance rather than improvised, executed against the specification, and the evidence is retained with the identity of who ran it and when. The release is approved by a defined role. Affected documentation is updated. If the change touches a validated system in a customer environment, the customer is notified in a way their own change control can consume.

None of those steps is unreasonable in isolation. Engineering teams already do most of them informally, which is exactly the trap. Informal execution produces no evidence, and in a regulated context the evidence is the deliverable. The ISPE GAMP 5 guide, now in its second edition, is the reference framework the industry uses to make this proportionate rather than infinite, and its central idea is that effort should scale with patient risk rather than being applied uniformly. Applied well, it protects you from over-documenting a low-risk feature. Applied badly, or not at all, it leaves you defending every decision from first principles in front of an auditor.

The FDA has been actively pushing the same proportionality through its Computer Software Assurance guidance, which asks teams to spend their effort on critical thinking and risk rather than on generating paper for its own sake. That shift is genuinely good news for vendors, but only for vendors who understand the underlying framework well enough to argue why a given control is or is not warranted.

Documented quality management procedures and validation records for GxP compliance

AI Products Hit This Wall First and Hardest

If your product uses machine learning or large language models, you are meeting these questions earlier and with less precedent to lean on than anyone else. Traditional validation assumes a system is deterministic: give it the same input, get the same output, script a test, capture the evidence. A language model satisfies none of that. Its behavior shifts when the underlying model is updated, its output space is far too large to test exhaustively, and your customer's quality group has often never assessed anything like it. They will ask how you validated it, and they may not know what a good answer sounds like either.

The workable approach moves the evidence from scripted determinism toward a defensible risk case. Intended use gets defined narrowly and honestly, because a system that claims to do everything can be validated for nothing. Performance is characterized against a held-out ground truth set with an accuracy threshold agreed in advance rather than reported after the fact. Models, prompts, and retrieval sources are versioned and controlled like any other configuration item, so that a change in behavior can be traced to a change in the system. Production monitoring watches for drift. Human oversight is specified explicitly for the decisions where being wrong actually matters, and that boundary is documented rather than assumed.

The standards landscape is moving underneath all of this. ISPE has published GAMP guidance addressing artificial intelligence and machine learning. The FDA issued draft guidance in January 2025 on the use of AI to support regulatory decision-making for drugs and biologics, built around a credibility assessment framework tied to context of use. The NIST AI Risk Management Framework and the EU AI Act add further expectations for vendors selling into Europe.

What none of this yet provides is a settled template, and that is precisely the opportunity. Vendors who can walk into a customer quality review with a coherent, defensible position on how their AI system was validated are not just clearing a hurdle. They are teaching their customer how to run a conversation that customer does not yet know how to run, and that is a durable commercial advantage over competitors who are still answering the question with a shrug.

AI and machine learning system undergoing GxP validation assessment in life sciences

GxP Is Broader Than Manufacturing

The most expensive misconception in this space is that GxP means factories. It does not. The regulation follows the regulated process your product touches, which means a CRM, a clinical data platform, a lab system, and an AI agent can each land in scope for different reasons. Here is where products actually fall.

GMP

Good Manufacturing Practice

Anything touching production, batch records, equipment, or release of product. Governed by 21 CFR Part 211 and EudraLex Volume 4. MES, LIMS, EBR, and increasingly the analytics layers sitting on top of them.

GCP

Good Clinical Practice

Systems supporting clinical trials: EDC, CTMS, eTMF, randomization, ePRO, and the AI tools now drafting protocols and narratives. Governed by ICH E6(R3) and 21 CFR Part 312. Subject safety and data integrity drive the risk here.

GLP

Good Laboratory Practice

Non-clinical safety studies and the instruments, ELNs, and data systems around them, under 21 CFR Part 58 and the OECD GLP principles. Raw data handling and audit trails are where vendors most often fall short.

GDP

Good Distribution Practice

Warehousing, cold chain, temperature monitoring, serialization, and traceability. Shaped by DSCSA in the US and the EU GDP guidelines. IoT and sensor platforms land here far more often than their builders expect.

GVP

Good Pharmacovigilance Practice

Adverse event intake, case processing, signal detection, and regulatory reporting, under the EMA GVP modules. A hot zone for AI vendors, because case triage is an obvious automation target with direct patient safety exposure.

Part 11 and Data Integrity

Cutting across all of the above: if your system creates, modifies, or stores a regulated record, 21 CFR Part 11 and EU Annex 11 apply. Audit trails, e-signatures, and ALCOA+ principles.

What We Build With You

Six workstreams. Which ones you need, and in what order, comes out of the assessment rather than a template. Vendors with an existing ISO certification and disciplined engineering often need less than they fear. Vendors with no formal quality system need more than they hope.

Quality Management System

The procedural backbone your customer will ask to see: document control, training records, deviation and CAPA handling, internal audit, and management review. Built proportionate to your size rather than copied from a 5,000-person pharma. If you already hold ISO 9001 or ISO 27001, we extend what exists instead of starting over.

CSV program development

Controlled SDLC and Change Control

A development lifecycle that emits evidence as a by-product of how your engineers already work, rather than a parallel bureaucracy they resent and route around. Requirements traceability, specified testing, defined release approval, and defect management, mapped onto your existing tooling in Jira, GitHub, or Azure DevOps.

21 CFR Part 11 development

The Pharma-Ready Validation Package

The customer-facing artifact and the reason most vendors call us: intended use, risk assessment, requirements traceability matrix, test evidence, and IQ/OQ/PQ protocols your customer can leverage rather than rebuild from scratch. This is the document set that shortens their implementation and makes you the cheaper option.

System validation

AI and Machine Learning Validation

For non-deterministic products where scripted test cases do not apply: intended use definition, ground truth benchmarking with pre-agreed thresholds, model and prompt version control, drift monitoring, and documented human oversight. Aligned to GAMP AI guidance, FDA draft guidance on AI in regulatory decision-making, and the NIST AI RMF.

Read our GAMP 5 AI guide

Supplier Audit Readiness

Preparation for the audit your customer's quality group will run on you. We assemble the documentation, rehearse the sessions so the right people answer the right questions, attend the audit, and help you respond to findings. Where an audit has already gone badly, we work backwards from the findings into a remediation plan.

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Customer-Facing Enablement

The differentiator almost nobody builds. Your customers often do not know how to validate a system like yours, particularly an AI one. We help you build the framework and materials to guide that conversation, so your team leads the validation discussion instead of waiting to be graded on it.

Discuss your product

Two Validations, Two Owners

The most useful thing we can tell a vendor on day one is that there are two distinct validation efforts, and confusing them wastes months. Your customer validates the system in their environment for their intended use, and under GAMP 5 that accountability is theirs and cannot be transferred to you. Your job is the supplier half: documented, controlled evidence of how the product was built and tested, produced to a standard your customer can rely on. Get that right and their effort shrinks, because they leverage your evidence rather than regenerating it. Get it wrong and they must test everything themselves, which makes your product the expensive one on the shortlist regardless of its price.

Talk through your situation

Yours

Supplier evidence: controlled SDLC, specified testing, release records, change control, and a leverageable package.

Theirs

System validation in their environment against their intended use, their SOPs, and their risk assessment.

Shared

A quality agreement defining who does what, plus change notification so their validated state survives your releases.

Where Your Product Lands Under GAMP 5

GAMP 5 software categories determine how much evidence your customer needs and how much of it they will expect from you. Most commercial products sit in Category 4, and most AI products are argued into Category 5 by default until the vendor makes a better case. Knowing your category before a customer assigns you one is worth doing.

GAMP Category	What It Covers	What Your Customer Expects From You	Typical Vendor
Category 1: Infrastructure	Operating systems, databases, and platform layers that support applications but hold no regulated logic themselves.	Qualification records for the platform. Usually handled by your cloud provider rather than by you.	Cloud and hosting providers
Category 3: Non-Configured	Commercial off-the-shelf products used as supplied, with no configuration of business logic beyond runtime parameters.	A supplier assessment and evidence the product does what its specification claims. The lightest burden of the four.	Simple instrument and utility software
Category 4: Configured	Commercial products configured to a customer business process. Where the large majority of B2B SaaS in life sciences sits.	A supplier audit, your development and testing evidence, and a validation package covering the configured use. Risk-based testing of configuration.	Most enterprise SaaS platforms
Category 5: Custom	Bespoke code written for a specific customer, plus anything the customer cannot assess as a known quantity. AI often lands here by default.	The full weight: design specifications, code review evidence, complete traceability, and the deepest testing burden of any category.	Custom builds, integrations, and unargued AI

How an Engagement Runs

We phase this work deliberately, because the failure mode is not doing too little. It is committing to a twelve-month quality program before anyone knows whether the commercial opportunity justifies it. Each phase ends with a decision point where stopping is a legitimate outcome.

Phase 1: Discovery and Gap Analysis

A short, bounded engagement. We review your product architecture, your existing development process, whatever documentation exists, and the specific customer commitment driving the timeline. You get a written gap analysis against the regulations that actually apply to your product, sequenced by what blocks revenue first rather than by what a textbook would tackle first.

• Regulatory scoping: which of GMP, GCP, GLP, GDP, GVP apply, and why
• GAMP 5 category determination and the argument supporting it
• Gap analysis against Part 11, Annex 11, and data integrity expectations
• Effort, sequence, and a defensible cost range before you commit further

Phase 2: Delivery Sprints

We work in short sprints against the gaps that matter, each producing a usable artifact rather than a milestone on a plan. Procedures are drafted with the engineers who will follow them, not written at them, because a procedure your team routes around produces exactly the contradiction an auditor is trained to find.

• SOPs and quality system components, written to your actual scale
• SDLC and change control mapped onto your existing engineering tooling
• Validation package assembly: traceability, test evidence, IQ/OQ/PQ protocols
• AI-specific evidence where the product is non-deterministic

Phase 3: Audit and Customer Readiness

The package meets its first real test when a customer quality group reads it. We prepare your team for that, rehearse the sessions, attend the audit, and help you write responses to findings. We also help you turn the package into a sales asset, because a vendor who can explain their validation position clearly closes faster than one who forwards a PDF and hopes.

• Supplier audit preparation and rehearsal with your team
• Audit attendance and support in the room
• Finding response and remediation planning
• Quality agreement and change notification design

Phase 4: Ongoing Advisory

Compliance decays quietly. You ship, the product changes, and the package that described it drifts out of date until a customer notices before you do. A retainer keeps the documentation current, gives your team somewhere to take judgment calls as they arise, and handles the next customer's questionnaire without another scramble.

• Package maintenance as the product evolves
• Support for each new customer questionnaire and audit
• Periodic review and internal audit execution
• Regulatory horizon scanning, particularly on AI

Engagements are scoped after Phase 1, when there is something real to scope against. If the assessment says the opportunity does not justify the work, we will tell you that, and Phase 1 will still have been worth it because you can now answer the question the next time a prospect asks.

Your GxP Enablement Lead

Amie Harpe, CSV Practice Lead

23 years at Pfizer across Quality Operations and Digital Solutions
Deployed a document management system to 65 manufacturing sites and 22,000 users
Led a Veeva QMS implementation for change control
PMP, SAFe Product Owner, Lean Six Sigma Green Belt

Amie has spent most of her career on the regulated side of the table, evaluating vendors rather than being one. That is the perspective most software companies are missing when they try to guess what a pharmaceutical customer will ask for. She works alongside engineers who build regulated software daily, so what you get is a plan your development team can implement rather than a folder of templates and a wish.

Book time with Amie

Amie Harpe, CSV Practice Lead at IntuitionLabs

Frequently Asked Questions

GxP enablement is the work of making an existing software or AI product sellable into regulated life sciences. It is not a change to your product. It is the quality system, the controlled development lifecycle, and the documented evidence that sit around your product so a pharmaceutical customer's quality assurance group can accept it. Most vendors discover they need it the moment a regulated prospect sends a supplier questionnaire asking for their validation package, their SDLC procedures, and proof that those procedures are actually followed. GxP is the umbrella term for the FDA and EMA good practice regulations, covering manufacturing (GMP), clinical (GCP), laboratory (GLP), distribution (GDP), and pharmacovigilance (GVP). Which ones apply to you depends on what your product touches, not on what industry you think you are in.

No, and this is the single most common misconception we correct. Manufacturing (GMP) is the most visible branch because of FDA plant inspections, but GxP spans the whole product lifecycle. If your software supports clinical trials it falls under GCP. Non-clinical safety studies fall under GLP. Warehousing and cold chain fall under GDP. Adverse event handling and safety signal management fall under GVP. A field CRM that captures interactions with healthcare professionals, an AI agent that drafts a regulatory document, and a data platform that supports bioprocess decisions can each land in scope. The regulation follows the regulated process your product touches. Any system that creates, modifies, or stores records supporting those processes is also subject to 21 CFR Part 11 and EU Annex 11 for electronic records and signatures.

Not exactly, and the distinction matters commercially. Under GAMP 5 the regulated company is accountable for validating the system in its own environment for its own intended use. That accountability cannot be outsourced to you. What your customer needs from you is the supplier side of the evidence: documented development and testing, a controlled change process, release records, defect management, and a validation package they can leverage rather than rebuild. A good package lets your customer reduce their own testing burden by relying on yours. A missing package means they must test everything from scratch, which lengthens their implementation, raises their cost, and makes you the expensive option. This is why vendor documentation is a sales asset, not just a compliance chore.

AI and machine learning break the assumption underneath traditional computer system validation, which is that the same input reliably produces the same output. A large language model is non-deterministic, its behavior shifts when the underlying model is updated, and its output space is too large to test exhaustively. So the evidence moves from scripted test cases toward a risk-based case: defining intended use narrowly, characterizing performance against a held-out ground truth set, controlling and versioning models and prompts, monitoring for drift in production, and specifying human oversight of the decisions that matter. ISPE has published GAMP guidance on AI and machine learning, the FDA has issued draft guidance on AI used to support regulatory decision-making, and frameworks like the NIST AI Risk Management Framework and the EU AI Act increasingly shape the conversation. Nobody has a settled playbook here yet, which is exactly why AI vendors are hitting this wall first and hardest.

A focused engagement typically runs three to six months from assessment to a defensible package, depending on how much already exists and how much your team can absorb alongside shipping product. Vendors with an ISO 9001 or ISO 27001 certification and a disciplined engineering process start further along than they expect, because much of the underlying discipline is already there and simply is not documented in the form a pharma auditor recognizes. Vendors with no formal quality system at all need longer, because procedures have to be written, adopted, and then run long enough to produce records. That last part is the piece teams consistently underestimate. A procedure with no evidence of execution behind it is worse than no procedure, because it tells an auditor you say one thing and do another.

Yes, and this is one of our most common entry points because it comes with a deadline attached. We prepare the documentation set, rehearse the sessions with your team so the right people answer the right questions, sit in on the audit itself, and help you write responses to any findings. Where an audit has already happened and gone badly, we work backwards from the findings into a remediation plan you can show the customer. Audit preparation is also the fastest way to discover what your real gaps are, because a customer quality group will ask precisely the questions that expose them.

Amie Harpe leads our CSV and quality practice. She spent 23 years at Pfizer across Quality Operations and Digital Solutions, deployed a document management system to 65 manufacturing sites and 22,000 users, and led a Veeva QMS implementation for change control. She has sat on the regulated side of the table evaluating vendors, which is the perspective most vendors are missing when they try to guess what their customer will ask for. She works alongside engineers who build regulated software, so the recommendations you get are ones your development team can actually implement rather than a stack of templates.

It depends entirely on whether a regulated customer is in front of you. If you have no pharma prospect yet, building a full quality system is premature and expensive, and we will tell you so. If you have a signed pilot or a prospect asking compliance questions, the work is already late, because their procurement and quality review will run in parallel with the technical evaluation and can quietly stall the deal for a quarter. The middle path most startups should take is a lightweight assessment that tells you what you would need and what it would cost, so the answer exists before a customer asks and you can size the commitment against the size of the opportunity.

Your Customer Is Waiting on a Document You Do Not Have

Book a call with our CSV practice lead. We will tell you which regulations actually apply to your product, what your customer will ask for, and whether the work is worth doing yet.

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