Case Study
Applying Computer Vision and LLMs to Regulatory Review in Life Sciences
Roko Labs helped a cloud-based life sciences platform improve medical image processing and automated data extraction at scale. The team implemented a computer vision pipeline to process and analyze medical images, enabling accurate data extraction while meeting strict performance and reliability requirements. The platform now delivers near real-time insights, reducing manual effort and improving the speed, accuracy, and scalability of life sciences workflows.
Case Study
Applying Computer Vision and LLMs to Regulatory Review in Life Sciences
Roko Labs helped a cloud-based life sciences platform improve medical image processing and automated data extraction at scale. The team implemented a computer vision pipeline to process and analyze medical images, enabling accurate data extraction while meeting strict performance and reliability requirements. The platform now delivers near real-time insights, reducing manual effort and improving the speed, accuracy, and scalability of life sciences workflows.
Client
A SaaS platform serving life sciences organizations
Industry
Healthcare & Life Sciences
Services
AI Systems Engineering Computer Vision LLM Architecture
Project Duration
6 Months
Client
A SaaS platform serving life sciences organizations
Industry
Healthcare & Life Sciences
Services
AI Systems Engineering Computer Vision LLM Architecture
Project Duration
6 Months
Client
A SaaS platform serving life sciences organizations
Industry
Healthcare & Life Sciences
Services
AI Systems Engineering Computer Vision LLM Architecture
Project Duration
6 Months
Problem
Regulatory compliance in life sciences marketing is not just a business challenge, it is a multimodal reasoning problem. Marketing materials combine structured and unstructured text, visual elements such as charts and layouts, and cross referenced claims tied to supporting documentation. All of this must be evaluated against strict and constantly evolving regulatory frameworks, including FDA guidelines. Document structure is often ambiguous. Multi-column layouts, embedded visuals, and inconsistent formatting complicate interpretation. Claims must be validated across multiple documents, while non-verbal cues such as emphasis, layout, and imagery can introduce compliance risk. Because of this complexity, traditional automation approaches fall short. Organizations instead rely on time-intensive expert review cycles that can take days to complete for a single document, creating bottlenecks in life sciences marketing workflows.
Our Approach: AI as a System, Not a Prompt
Rather than treating regulatory review as a prompt engineering exercise, Roko Labs approached the challenge as a distributed AI systems engineering problem. The goal was to design an AI regulatory compliance system capable of: • Decomposing complex, multimodal documents into analyzable components • Reconstructing document structure and meaning across text and visuals • Grounding outputs in regulatory frameworks and source evidence Equally important, the system needed to integrate seamlessly into existing enterprise workflows. For our client, this meant direct integration with Veeva, the platform used by their marketing material reviewers. The architecture was also designed for regulated environments, where: • AI outputs are inherently non deterministic • Human oversight is mandatory • Traceability and explainability are critical requirements
Problem
Regulatory compliance in life sciences marketing is not just a business challenge, it is a multimodal reasoning problem. Marketing materials combine structured and unstructured text, visual elements such as charts and layouts, and cross referenced claims tied to supporting documentation. All of this must be evaluated against strict and constantly evolving regulatory frameworks, including FDA guidelines. Document structure is often ambiguous. Multi-column layouts, embedded visuals, and inconsistent formatting complicate interpretation. Claims must be validated across multiple documents, while non-verbal cues such as emphasis, layout, and imagery can introduce compliance risk. Because of this complexity, traditional automation approaches fall short. Organizations instead rely on time-intensive expert review cycles that can take days to complete for a single document, creating bottlenecks in life sciences marketing workflows.
Our Approach: AI as a System, Not a Prompt
Rather than treating regulatory review as a prompt engineering exercise, Roko Labs approached the challenge as a distributed AI systems engineering problem. The goal was to design an AI regulatory compliance system capable of: • Decomposing complex, multimodal documents into analyzable components • Reconstructing document structure and meaning across text and visuals • Grounding outputs in regulatory frameworks and source evidence Equally important, the system needed to integrate seamlessly into existing enterprise workflows. For our client, this meant direct integration with Veeva, the platform used by their marketing material reviewers. The architecture was also designed for regulated environments, where: • AI outputs are inherently non deterministic • Human oversight is mandatory • Traceability and explainability are critical requirements
Problem
Regulatory compliance in life sciences marketing is not just a business challenge, it is a multimodal reasoning problem. Marketing materials combine structured and unstructured text, visual elements such as charts and layouts, and cross referenced claims tied to supporting documentation. All of this must be evaluated against strict and constantly evolving regulatory frameworks, including FDA guidelines. Document structure is often ambiguous. Multi-column layouts, embedded visuals, and inconsistent formatting complicate interpretation. Claims must be validated across multiple documents, while non-verbal cues such as emphasis, layout, and imagery can introduce compliance risk. Because of this complexity, traditional automation approaches fall short. Organizations instead rely on time-intensive expert review cycles that can take days to complete for a single document, creating bottlenecks in life sciences marketing workflows.
Our Approach: AI as a System, Not a Prompt
Rather than treating regulatory review as a prompt engineering exercise, Roko Labs approached the challenge as a distributed AI systems engineering problem. The goal was to design an AI regulatory compliance system capable of: • Decomposing complex, multimodal documents into analyzable components • Reconstructing document structure and meaning across text and visuals • Grounding outputs in regulatory frameworks and source evidence Equally important, the system needed to integrate seamlessly into existing enterprise workflows. For our client, this meant direct integration with Veeva, the platform used by their marketing material reviewers. The architecture was also designed for regulated environments, where: • AI outputs are inherently non deterministic • Human oversight is mandatory • Traceability and explainability are critical requirements
Why a Monolithic LLM Approach Fails
Early experimentation confirmed that feeding entire documents into a single large language model was insufficient for reliable AI compliance review. Large documents quickly exceed effective reasoning capacity. OCR generated text alone strips away layout and structural cues that are essential for interpretation. Without grounding in external references, claims cannot be validated, and visual elements are either ignored or misinterpreted. To overcome these limitations, the team at Roko Labs designed a multi-stage, multimodal AI pipeline, where each step produces structured outputs that feed into downstream reasoning and validation.
Why a Monolithic LLM Approach Fails
Early experimentation confirmed that feeding entire documents into a single large language model was insufficient for reliable AI compliance review. Large documents quickly exceed effective reasoning capacity. OCR generated text alone strips away layout and structural cues that are essential for interpretation. Without grounding in external references, claims cannot be validated, and visual elements are either ignored or misinterpreted. To overcome these limitations, the team at Roko Labs designed a multi-stage, multimodal AI pipeline, where each step produces structured outputs that feed into downstream reasoning and validation.
Solution
Partnering closely with the client and their internal workflows, the team at Roko Labs built an AI compliance platform composed of coordinated processing pipelines and a layered reasoning system. Key capabilities include: • Computer vision for layout detection and document structure reconstruction • LLM based claim extraction and regulatory reasoning • Evidence grounded validation against supporting documentation The platform integrates directly into the client's existing workflow environment, allowing users to initiate compliance analysis within familiar tools. AI generated annotations appear alongside human feedback, while structured reports can be reviewed independently. Rather than issuing binary pass/fail decisions, the system mirrors the behavior of expert reviewers by: • Highlighting potential compliance risks • Providing contextual annotations • Validating claims with traceable evidence This approach positions AI as a decision support layer, accelerating regulatory review without replacing human judgment.
Solution
Partnering closely with the client and their internal workflows, the team at Roko Labs built an AI compliance platform composed of coordinated processing pipelines and a layered reasoning system. Key capabilities include: • Computer vision for layout detection and document structure reconstruction • LLM based claim extraction and regulatory reasoning • Evidence grounded validation against supporting documentation The platform integrates directly into the client's existing workflow environment, allowing users to initiate compliance analysis within familiar tools. AI generated annotations appear alongside human feedback, while structured reports can be reviewed independently. Rather than issuing binary pass/fail decisions, the system mirrors the behavior of expert reviewers by: • Highlighting potential compliance risks • Providing contextual annotations • Validating claims with traceable evidence This approach positions AI as a decision support layer, accelerating regulatory review without replacing human judgment.
Engineering Challenges and System Tradeoffs
As noted by Roko Labs' Lead AI Architect Renato Zeleznjak:
“While it seemed that the project might be relatively easy, it was quite complex. The key challenge was figuring out how to break the workflow into individual steps that would produce meaningful results.”
One of the primary challenges was decomposing the workflow into independent, tractable stages. Each stage needed to be:
• Small enough for reliable processing
• Structured enough for downstream reasoning
• Efficient in terms of cost and latency
Model selection played a critical role in balancing performance, scalability, and cost across the AI system. Different pipeline stages placed very different demands on underlying models.
To optimize efficiency, engineers at Roko Labs:
• Separated system instructions from dynamic inputs to enable caching
• Reduced payload size through preprocessing
• Assigned models based on task complexity
For high volume OCR post processing and layout reconstruction, lighter weight models were used, achieving approximately 3× cost reduction while maintaining acceptable performance. More complex tasks such as regulatory validation and multimodal reasoning continued to rely on higher capability models to ensure accuracy and reliability.
Results: Faster, More Consistent Regulatory Review
The AI compliance system reduced initial regulatory review time from multiple days to approximately 15 minutes per document. This enabled: • Scalable and repeatable pre review workflows • Earlier identification of compliance risks • Improved consistency across reviews Most importantly, the system was intentionally designed as decision support, ensuring that final judgment remains with human regulatory experts.
Results: Faster, More Consistent Regulatory Review
The AI compliance system reduced initial regulatory review time from multiple days to approximately 15 minutes per document. This enabled: • Scalable and repeatable pre review workflows • Earlier identification of compliance risks • Improved consistency across reviews Most importantly, the system was intentionally designed as decision support, ensuring that final judgment remains with human regulatory experts.
Business Outcomes and Impact
This architecture demonstrates how AI can be applied responsibly and effectively in highly regulated industries. The team at Roko Labs helped the client navigate the uncertainty of applying LLMs to compliance workflows and build a production ready AI regulatory review system. The resulting foundation is extensible to: • Additional regulatory markets • Other compliance driven industries such as financial services and insurance
Business Outcomes and Impact
This architecture demonstrates how AI can be applied responsibly and effectively in highly regulated industries. The team at Roko Labs helped the client navigate the uncertainty of applying LLMs to compliance workflows and build a production ready AI regulatory review system. The resulting foundation is extensible to: • Additional regulatory markets • Other compliance driven industries such as financial services and insurance
A Trusted Partner for Regulated AI Systems
Building AI systems for regulated environments requires more than technical skill, it requires engineering judgment under constraints. Roko Labs brings deep expertise in multimodal AI architecture, LLM optimization, and compliance aware system design. By balancing innovation, cost efficiency, and regulatory requirements, Roko helps organizations move from experimentation to real world, compliant AI solutions.
A Trusted Partner for Regulated AI Systems
Building AI systems for regulated environments requires more than technical skill, it requires engineering judgment under constraints. Roko Labs brings deep expertise in multimodal AI architecture, LLM optimization, and compliance aware system design. By balancing innovation, cost efficiency, and regulatory requirements, Roko helps organizations move from experimentation to real world, compliant AI solutions.
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Copyright © 2026 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001
Copyright © 2026 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001
Copyright © 2026 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001
Copyright © 2026 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001