Category Visionaries How Narrativa leveraged COVID-19 as a market catalyst to accelerate AI adoption in pharma | Jennifer Bittinger ($2.4M Raised)
Narrativa is transforming pharmaceutical clinical trials by automating the documentation burden that consumes 40-60% of drug development timelines. Founded in 2015 in Madrid and expanded to the US market in 2021, Narrativa has raised $2.4 million to build AI automation for regulatory documents required in clinical trials. In this episode of Category Visionaries, we sat down with Jennifer Bittinger, President and Founder of Narrativa, to learn about the company's pivot from general content automation to becoming essential infrastructure for pharmaceutical companies looking to accelerate drug development from 10-16 year timelines to significantly faster cycles.
Topics Discussed:
- Narrativa's pivot from financial content automation to pharmaceutical clinical trial documentation
- The company's strategy of focusing on mandatory regulatory use cases for universal adoption
- Narrativa's discovery that CROs weren't the right customer due to business model conflicts
- The impact of COVID-19 on accelerating AI adoption in traditionally resistant pharmaceutical companies
- Narrativa's approach to building trust through relationship marketing in regulated industries
- The challenge of positioning AI as augmentation rather than replacement for medical writers
GTM Lessons For B2B Founders:
- Focus on mandatory, universal use cases to ensure scalability: Narrativa discovered that patient safety narratives—reports required for every clinical trial when patients experience adverse events—offered the perfect scalable entry point. Unlike one-off custom projects that looked attractive but weren't universally needed, these regulatory-mandated reports guaranteed market-wide adoption potential. Jennifer explains: "If we created this use case for a patient safety narrative, then guess what, everyone has to use it. It's an opportunity to scale quickly across every drug trial in the market."
- Leverage regulatory catalysts as market entry accelerators: COVID-19 created an unexpected opening for AI adoption in pharma by demonstrating the critical need for speed in drug development. The pandemic forced pharmaceutical companies to recognize that their traditional 10-16 year timelines needed acceleration, with 40-60% of that time spent on paperwork rather than actual research. This regulatory pressure became a powerful sales catalyst that opened previously resistant organizations to automation solutions.
- Identify business model disruptions to understand your real customer: The team initially targeted Clinical Research Organizations (CROs) as their primary market, assuming they'd want efficiency gains across multiple accounts. However, they discovered CROs were actually disincentivized to adopt their solution because they bill sponsors hourly for report writing. AI automation that reduces hours from days to minutes destroys their revenue model. This insight led them to pivot directly to pharmaceutical sponsors and biotech companies who benefit from faster, more accurate reporting.
- Use industry events for trust-building rather than traditional marketing: In the highly regulated pharmaceutical industry, relationship-building trumps conventional marketing tactics. Jennifer invested heavily in speaking at industry events and nurturing personal relationships—calling prospects to check on their families, taking them to dinner, and positioning herself as a trusted advisor rather than a vendor. This approach proved essential in an industry where professionals see themselves as saving lives and need to trust anyone handling patient data.
- Target resource-constrained segments with urgent timelines: Biotech companies emerged as ideal customers because they operate like well-funded startups with finite resources and urgent timelines. They're typically VC or PE-funded with limited time to develop molecules before selling to larger pharma sponsors. This creates natural urgency for efficiency tools that can accelerate their development process while maintaining the data accuracy required for eventual acquisition.
- Position AI as augmentation during the transition period: Early adoption required careful messaging about AI as a "medical writing sidekick" rather than a replacement. Jennifer learned this lesson when speaking to a room of 200+ medical writers who initially viewed the technology as an existential threat. The breakthrough came when one writer understood that automation could have given him more time for family moments—attending soccer games and ballet recitals instead of working late on reports.
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