Sanai AI pilot at a top-20 biopharma: 54,120 hours saved a year in R&D

Executive summary

A top-20 biopharma company partnered with Sanai to address inefficiencies across its Process Development and R&D operations. Its subject matter experts were spending thousands of hours a year on manual document authoring and data retrieval, time that could go into laboratory work and the clinical pipeline.

Sanai deployed two capabilities: an Automated Authoring engine for technical report generation, and a Query Engine for search and analysis across the company's Benchling, Veeva and SQL databases. The pilot showed 54,120 hours saved a year, $5.1M in baseline annual savings for 150 seats, and a projected $4B in accelerated revenue over eight years.

The challenge

Operational bottlenecks were limiting how fast the company could advance its cell therapy pipeline.

• Subject matter experts' time. Teams spent thousands of hours a year drafting documents by hand. At about 3 hours a page, and 535 reports of 26.5 pages on average at one site in 12 months, creating the first draft alone took roughly 80 hours per report and pulled senior scientists out of the lab.
• Inaccessible historical data. Experimental data sat in Benchling, Veeva and SQL databases with no unified search. Teams unknowingly repeated studies that were already done, at a cost of hundreds of thousands of dollars per duplicated study.
• Slow automation workflows. Automation teams took a long time to turn process steps into executable code, including the calculations and controls for fully automated manufacturing.
• Cross-functional bottlenecks. Collaboration across Process Development, Analytical Development, MSAT, Validation and Regulatory contributed to clinical timeline delays, including slipping regulatory filings.
• Stalled programs. Several pre-clinical projects were on hold because of bandwidth, not science.

The solution

Sanai deployed two integrated capabilities, both built for on-premises deployment and compliant with ICH, GxP and SOC2 standards.

• Automated Authoring. Template-driven AI generation produces first drafts of technical reports, protocols, submissions and other key documents. Initial creation fell from 80 to 300 hours to minutes, with a human in the loop for review. Collaborative editing with targeted AI-assisted revisions cut iteration time by 50%, and standardized prompt engineering kept outputs compliant.
• Query Engine. Retrieval-augmented generation across the company's technical data, linked directly into Benchling, Veeva and SQL databases. It answers with analysis and source citations, turning hours of multi-source searching into minutes. It also runs statistical calculations, builds visualizations and compares historical datasets, a 5 to 10 times improvement in analytical output.

Pilot results

The company's own subject matter experts evaluated the pilot across 45 structured evaluations. They spanned Bloom's taxonomy difficulty levels, from basic recall to complex creation tasks, and covered data retrieval, document identification, data analysis and comprehension across process areas including Tech Transfer, Manufacturing Summary and Investigations.

The company had earlier invested over $10M in an internal AI search tool built with a major technology provider. Sanai's Query Engine was benchmarked directly against it.

Authoring performance and testing rigor

Automated Authoring averaged a composite score of 83% on first-pass generation, before any human editing, which sharply cut the time experts needed to produce publication- and submission-ready documents.

Across the evaluation set, experts gave the Query Engine an average overall score of 8.5 out of 10: thoroughness averaged 2.6 of 3.0, accuracy 2.7 of 3.0 and citation quality 1.8 of 2.0.

Quantified impact

Annual figures are baseline estimates for 150 seats.

Pipeline and strategic impact

Beyond direct savings, the deployment is projected to speed up the company's clinical pipeline.

• Clinical timelines shorter by 6 to 12 months per phase. Automating document generation and drafting filing documents instantly supports faster, higher-quality regulatory submissions. The combined time-to-market advantage across the current pipeline is projected at 13.9 years over the program lifetime.
• Portfolio up 15 to 20% without added headcount. With 25.5 FTEs moved from administrative tasks back to lab and development work, the company expects to add 1 to 2 clinical programs with the same team.
• 20% faster from research to development. AI-driven process improvements speed up development and raise throughput across clinical, development and regulatory work.
• $4B in accelerated revenue projected over 8 years. Compressing clinical timelines pulls revenue forward by an estimated $385M to $3.9B per drug, depending on phase, through faster submissions and earlier market entry.
• Parallel submissions. The company can process multiple overlapping regulatory submissions at once, which matters for a multi-program pipeline.

Conclusion

The pilot showed that Sanai's platform delivers measurable, immediate value in biopharma R&D operations. It outperformed a $10M+ internal build on quality (94% against 60% pass rate, 8.7 against 5.3 composite score), which shows purpose-built AI for pharma can exceed what a large internal technology investment delivers.

With $5.1M in baseline annual savings, a payback period of just over a month, and 360+ hours returned per user per year, the pilot makes a strong case for enterprise-wide deployment. It is a shift in how biopharma organizations can scale R&D without scaling headcount at the same rate.

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