Now accepting early access partners

Validation that moves
at the speed of science.

EVOLV | The Validation Factory

The first AI-native Computer System Validation platform built for GAMP 5, 21 CFR Part 11, and FDA QMSR. Generate compliant URS, automate risk assessment, and govern AI model changes — in minutes, not months.

Request Early Access ▶ Try Live Demo
GAMP 5 21 CFR Part 11 21 CFR Part 820 QMSR ISO 13485:2016 FDA PCCP Guidance Aug 2025 GMP Annex 11 EU GMP

The Problem

CSV validation still runs on
spreadsheets and email chains.

Life sciences teams spend months on documentation that should take days. Regulatory guidance has changed. Most platforms haven't.

📋

Weeks to generate a URS

Requirements written manually in Word, reviewed in meetings, rewritten after audit findings. No regulatory traceability. No version control. Just documents.

⚠️

Regulations change. VMPs don't.

FDA's QMSR replaced the QSR on February 2, 2026. The PCCP final guidance landed August 2025. Most validation plans still cite rules that no longer exist.

🤖

AI in GxP with no governance

Drug interaction predictors and defect classifiers are being deployed with no PCCP, no change control, no audit trail. FDA inspectors are starting to notice.

The Platform

One platform.
The entire validation lifecycle.

From first requirement to post-market governance — every step compliant, auditable, and powered by AI that knows GAMP 5.

📄

Validation Factory

Generate GAMP 5-compliant URS from plain English. SMART refinement, PCCP verification, UR/FR decomposition, CSA test scripts, and PDF export with e-signature — in one guided workflow. What took days takes minutes.

URS Generation SMART Refinement UR/FR Decomposition CSA Test Scripts PDF + E-Signature
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Change Control

ServiceNow change requests cross-referenced against your system portfolio in real time. GxP classification, GAMP-aware risk calculation using Severity × Occurrence × Detectability, and automatic revalidation flags. No spreadsheet required.

ServiceNow Integration GxP Classification GAMP Risk Matrix Revalidation Flags
🤖

AI Model Governance

Validated AI models treated as GAMP Category 5 assets. PCCP-aware change assessment aligned to FDA final guidance August 2025. Human-in-the-loop approval queue with 21 CFR Part 11 compliant audit trail.

PCCP Assessment HITL Governance FDA Aug 2025 21 CFR Part 11
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Portfolio Dashboard

Every system, every site, every phase — one view. RAG status, action items, regulatory due dates. Register new systems with GxP classification and track their full validation journey from plan to monitor. Built for QA Heads and CTOs.

RAG Status System Registry Lifecycle Tracking Multi-Site

See It In Action

Watch EVOLV handle in 4 minutes
what takes your team weeks.

Three real workflows. No voiceover scripts. No slides. Just the platform doing what it's built to do.

Demo 01

Portfolio Dashboard

Your entire validated system landscape — GAMP category, GxP status, risk level, revalidation due dates — in one view. Built for QA Heads and CTOs who need portfolio-wide compliance visibility instantly..

Demo 02

Change Control & GxP Classification

ServiceNow CR received → portfolio cross-reference → GAMP risk calculation → revalidation decision. Automatic.

Demo 03

AI Model Governance

AI model change in a validated system → PCCP assessment → Governance Hub → human-in-the-loop approval → 21 CFR Part 11 audit trail. Built for FDA's final guidance (Aug 2025).

Try the Validation Factory — live, right now.

Generate a real GAMP 5-compliant URS from your own requirement. No login required.

▶ Open Live Demo

Built For

EVOLV speaks the language
of regulated industries.

Whether you're defending a validation package in an inspection or governing 150 systems across 25 sites — EVOLV gives you the records to back it up.

🧪 QA Head · CSV Manager

Stop writing in Word. Start validating.

EVOLV generates, verifies, and manages your entire validation portfolio with a complete audit trail FDA inspectors can review on the spot.

  • URS from plain English in minutes
  • SMART requirements, auto-verified against GAMP 5
  • Full lifecycle tracking — Plan to Monitor
  • Audit-ready PDFs with e-signature

📋 VP Regulatory Affairs

FDA updated the rules. EVOLV already knows.

QMSR is mandatory. PCCP guidance is final. EVOLV cites the right regulations, routes the right changes, and produces the records you need when an inspector walks in.

  • Aligned to FDA PCCP Guidance Aug 18, 2025
  • 21 CFR Part 820 QMSR / ISO 13485:2016
  • SHA-256 tamper-evident audit trail
  • 21 CFR Part 11 electronic records

💻 CTO · CDO

Your AI models are GAMP Cat 5 assets.

EVOLV gives you the governance infrastructure to deploy AI in GxP contexts without regulatory exposure — with a full HITL approval layer and immutable decision records.

  • AI model registry with version history
  • PCCP-aware change assessment
  • Human-in-the-loop governance hub
  • Portfolio-wide RAG visibility

Regulatory Coverage

GAMP 5 (2nd Edition) 21 CFR Part 11 21 CFR Part 820 QMSR ISO 13485:2016 FDA PCCP Guidance Aug 18, 2025 GMP Annex 11 EU GMP GLP ISO 17025 GDPR 21 CFR Part 820.35 QMSR FDA CSA Guidance 2022

Early Access

Join our first cohort of life sciences partners.

We're working with a small group of pharma and biotech companies before general availability. Early access partners get priority onboarding, direct access to the founding team, and founding-tier pricing locked in permanently.

🔒  We'll respond within 1 business day. Your information is never shared.

Debunking Myths in Data Analytics for Biotech Firms

Dec 09, 2025

Introduction

In the rapidly evolving world of biotech, data analytics is a game-changer. However, several myths cloud its potential. Understanding these misconceptions can help biotech firms harness the full power of data analytics to drive innovation and efficiency.

data analytics computer

Myth 1: Data Analytics Is Only for Large Companies

One common myth is that only large biotech firms can benefit from data analytics. In reality, data analytics is scalable and can be tailored to fit the needs of small to medium-sized enterprises. With the rise of cloud-based solutions, even smaller firms can access powerful analytics tools without significant infrastructure investment.

By leveraging these tools, smaller biotech companies can gain insights into operational efficiency, product development, and market trends, enabling them to compete with larger players.

Myth 2: Data Analytics Requires a Massive Budget

Another misconception is that data analytics requires a huge financial commitment. While traditional systems can be costly, modern solutions offer more affordable options. Open-source tools and pay-as-you-go models have democratized access to cutting-edge analytics capabilities.

biotech budget

These options allow firms to start small and scale their analytics capabilities as needed, ensuring that cost is no longer a barrier to harnessing data-driven insights.

Myth 3: Data Analytics Is Only About Numbers

It's easy to assume that data analytics is solely about crunching numbers. However, it encompasses so much more. By analyzing qualitative data, firms can gain insights into patient feedback, regulatory environments, and even competitive landscapes.

This holistic approach helps biotech companies understand the broader context of their operations and make better strategic decisions.

qualitative data analysis

Myth 4: You Need a Team of Data Scientists

While having a dedicated team of data scientists can be beneficial, it's not always necessary. Many modern analytics platforms offer user-friendly interfaces that allow non-experts to generate valuable insights. These platforms often come with built-in algorithms and visualization tools that simplify the process.

Moreover, training programs and online courses can empower existing staff to make the most of these tools, ensuring that every team member contributes to data-driven decision-making.

Conclusion

Dispelling these myths is crucial for biotech firms looking to stay competitive. By embracing data analytics, companies of all sizes can enhance their capabilities, drive innovation, and ultimately, improve patient outcomes. As technology continues to advance, understanding and utilizing data analytics will only become more critical in the biotech industry.