Introduction
Chemical safety management in large consumer goods companies is complex, mission-critical, and increasingly resource-intensive. With every new ingredient requiring rigorous toxicological screening and regulatory validation, manual workflows are struggling to keep pace with innovation. Regulatory scrutiny requires that every new ingredient or substance be screened against toxicological data, summarized into structured endpoints, and its risk assessed before product development can proceed to consumer sales.
For one leading global consumer goods company, the process had reached a threshold of capacity and so was becoming a key bottleneck to overcome. Regulatory toxicologists and experts were tasked with conducting exhaustive literature reviews across more than 80 public databases, manually screening publicly available data-rich PDFs, and creating toxicological endpoint summaries from these. The manual, complex, and repetitive nature of the current workflow, while effective in parts, has introduced variability over time, making it increasingly prone to unintentional discrepancies and inconsistent outcomes. The company recognized that this approach was unsustainable and risked slowing down its innovation pipeline.
To address these bottlenecks, the organization turned to Evalueserve IP and R&D, a leading provider of tailored AI solutions across industries. The mandate was clear: digitally transform the chemical safety workflow, optimising manual processes with efficient, automated solutions that enhance human expert workflows.
We deliver tailored AI workbenches designed around each client's unique processes. It’s not about selling a ready-made tool that is force-fitted to a process, but rather it’s about unlocking smarter, faster ways of working, with AI embedded where it matters most and adds value.
Diagnose: Pinpointing the Structural Barriers
Before thinking about applying AI to the manual process, Evalueserve worked closely with Toxicologists and Regulatory experts to map the existing process in detail. This diagnostic phase revealed three core pain points:
- Inefficient Literature Searches – Analysts were spending days manually reviewing large data sets, triaging relevant data from large amounts of noise. Without intelligent automation, the process increases cognitive load, frustration, and mental fatigue, leaving the researcher feeling like they’re constantly scanning, filtering, and second-guessing—knowing the insight is there somewhere, but buried under layers of distraction.
- Endpoint-Based Data Extraction Bottlenecks – Regulatory PDFs were manually evaluated to capture discrete toxicology endpoint data. This repetitive task, when done at high volume, slowed throughput and often led to variability in accuracy.
- Inconsistent Summarization – Toxicological summaries varied in style, quality, and format depending on the producer. Long-term manual repetition breeds divergence—even with clear guidelines, people interpret tasks differently over time, especially under pressure or fatigue. And when cognitive effort is spread across many individuals, consistency erodes, revealing that human variation, while valuable, can challenge quality and coherence.
The more chemicals screened, the more human resources were consumed, with no improvement and worse a deterioration in quality or consistency.
Modular AI Opportunities Identified
- Intelligent Search & Summarization – Automate literature search and triage to reduce cognitive load.
- Structured Data Extraction – Parse data documents to capture endpoint data with consistency.
- Standardized Summarization – Generate uniform toxicological summaries with style and format controls.
Predicted Transformation Impact
By embedding modular AI into these workflows, we predicted that the client teams could dramatically reduce manual effort, improve consistency, and unlock expert time for higher-value analysis, turning repetitive cognitive strain into streamlined, insight-driven productivity.
Design: Building the Right System
Rather than simply digitizing existing tasks, Evalueserve applied a design thinking framework to reimagine the workflow from end to end. This approach ensured that the solution addressed not just efficiency, but also usability, adoption, and long-term scalability.
- Empathize – Through interviews and surveys, the team identified the daily frustrations of toxicologists, lab researchers, and regulatory reviewers. This step created a detailed picture of personas and use cases.
- Define & Ideate – The problems were clearly defined: too much time on low-value work, inconsistent outputs, and limited ability to scale. Brainstorming sessions generated solution concepts that strike a balance between AI automation and expert oversight.
- Prototype & Test – Early prototypes of literature search automation and extraction tools were tested with real datasets. User feedback guided refinements, ensuring the tools worked in practice, not just in theory.
- Implement & Improve – Once validated, the solutions were deployed into live workflows, with continuous monitoring and iterative improvements to refine accuracy and user experience.
Deploy: AI-Enabled Workflow in Action
The final system combined three AI-powered modules, each targeting a critical pain point:
- Automated Literature Search – AI systems scanned across 80+ databases, filtering results for relevance and context. The outcome was a 100% validation success rate, meaning all AI-identified references were confirmed as accurate by human experts. Importantly, what had been a slow and exhaustive manual step became a high-throughput capability.
- Endpoint-Based Data Extraction – Machine learning models were trained to capture specific toxicological endpoints directly from PDFs containing toxicological data. Validation tests confirmed an accuracy rate of over 90%, reducing the burden on scientists and ensuring consistent results.
- AI-Assisted Summarization – Natural language models generated structured toxicological summaries, standardized across endpoints. Validation demonstrated a success rate of≥85%, with outputs that are significantly faster than those produced manually and require minimal post-editing.
By combining these AI modules, Evalueserve effectively restructured its chemical safety workflow into a semi-automated pipeline. We had deployed a modular, agent-based AI solution that kept critical “human in the loop” activities. Client scientists could focus on interpreting results and shaping regulatory strategy, rather than spending hours on data entry or formatting, thereby enhancing the overall quality and efficiency of the regulatory process, while also significantly increasing their job satisfaction and professional growth.
Impact: Time Back to the Scientists
The transformation was not just technical—it was strategic:
- Thousands of hours saved annually, eliminating repetitive transcription, burdensome QC checks, and manual data collection.
- Increased consistency and reliability across toxicological endpoints, giving regulatory teams confidence in their submissions.
- Faster cycle times in chemical safety evaluations, enabling the company to maintain speed-to-market without compromising compliance.
- Higher-value contributions from experts, as toxicologists, could now dedicate more time to risk assessment, research, and regulatory engagement rather than mechanical tasks.
What began as a search for efficiency evolved into a strategic capability in AI and digital transformation. By reimagining its chemical safety workflow, the company is now equipped to handle growing volumes of evaluations while maintaining the highest regulatory standards—and freeing its experts to focus on work and decisions that matter most.
Conclusion
This case demonstrates how AI and design thinking can fundamentally reshape high-stakes regulatory workflows. The final solution combined three modular AI-powered components—automated literature search, endpoint-based data extraction, and AI-assisted summarization—each directly targeting a critical pain point. With validation rates ranging from 85% to 100%, the solution transformed slow, manual tasks into high-throughput, reliable processes.
By embedding automation into chemical safety workflows, the client not only gained efficiency but also established a repeatable and scalable process that can adapt to internal innovation needs and external pressures such as evolving regulations and global market demands.
Evalueserve IP and R&D continues to partner with organizations to transform complex, manual processes into insight-driven, future-ready capabilities—freeing experts to focus on the decisions that matter most.
