Case Study

Building AI-Powered Compliance Tools for Life Sciences

Case Study

Building AI-Powered Compliance Tools for Life Sciences

Case Study

Building AI-Powered Compliance Tools for Life Sciences

Client

Canopy Life Sciences

https://www.canopy.ai/

Industry

Pharmaceutical Life Sciences Regulatory Consulting

Services

Analysis & Design Source Document Vectorization Prompt Engineering Retrieval Augmented Generation (RAG)

Project Duration

6 Months

Client

Canopy Life Sciences

https://www.canopy.ai/

Industry

Pharmaceutical Life Sciences Regulatory Consulting

Services

Analysis & Design Source Document Vectorization Prompt Engineering Retrieval Augmented Generation (RAG)

Project Duration

6 Months

Client

Canopy Life Sciences

https://www.canopy.ai/

Industry

Pharmaceutical Life Sciences Regulatory Consulting

Services

Analysis & Design Source Document Vectorization Prompt Engineering Retrieval Augmented Generation (RAG)

Project Duration

6 Months

Problem

Canopy Life Sciences, a provider of advanced services for regulatory and promotional content review for the pharmaceutical industry, sought to enhance the speed and consistency of their compliance workflows. Their clients in life sciences face increasing demands for efficient, compliant claims substantiation and document validation processes - often under tight timelines and strict FDA regulations. Manual review processes were time-consuming, prone to inconsistencies, and hard to scale. Canopy’s goal was to introduce an AI-driven system to reduce the burden on reviewers, improve metadata accuracy, and ensure regulatory adherence — all while maintaining strict data segregation and customer-specific models.

Our Vision

Roko Labs partnered with Canopy to co-design and deliver a scalable, compliant AI system that works seamlessly within the Veeva Vault environment. Working as a fully integrated product and engineering team, we leveraged Canopy’s existing APIs and regulatory expertise while applying our own experience in enterprise AI infrastructure. The team followed a sprint-based, iterative delivery process with deep collaboration on architecture and model design. Key decisions were reviewed jointly to ensure compliance, performance, and customer-specific isolation across language models and tenants.

Problem

Canopy Life Sciences, a provider of advanced services for regulatory and promotional content review for the pharmaceutical industry, sought to enhance the speed and consistency of their compliance workflows. Their clients in life sciences face increasing demands for efficient, compliant claims substantiation and document validation processes - often under tight timelines and strict FDA regulations. Manual review processes were time-consuming, prone to inconsistencies, and hard to scale. Canopy’s goal was to introduce an AI-driven system to reduce the burden on reviewers, improve metadata accuracy, and ensure regulatory adherence — all while maintaining strict data segregation and customer-specific models.

Our Vision

Roko Labs partnered with Canopy to co-design and deliver a scalable, compliant AI system that works seamlessly within the Veeva Vault environment. Working as a fully integrated product and engineering team, we leveraged Canopy’s existing APIs and regulatory expertise while applying our own experience in enterprise AI infrastructure. The team followed a sprint-based, iterative delivery process with deep collaboration on architecture and model design. Key decisions were reviewed jointly to ensure compliance, performance, and customer-specific isolation across language models and tenants.

Problem

Canopy Life Sciences, a provider of advanced services for regulatory and promotional content review for the pharmaceutical industry, sought to enhance the speed and consistency of their compliance workflows. Their clients in life sciences face increasing demands for efficient, compliant claims substantiation and document validation processes - often under tight timelines and strict FDA regulations. Manual review processes were time-consuming, prone to inconsistencies, and hard to scale. Canopy’s goal was to introduce an AI-driven system to reduce the burden on reviewers, improve metadata accuracy, and ensure regulatory adherence — all while maintaining strict data segregation and customer-specific models.

Our Vision

Roko Labs partnered with Canopy to co-design and deliver a scalable, compliant AI system that works seamlessly within the Veeva Vault environment. Working as a fully integrated product and engineering team, we leveraged Canopy’s existing APIs and regulatory expertise while applying our own experience in enterprise AI infrastructure. The team followed a sprint-based, iterative delivery process with deep collaboration on architecture and model design. Key decisions were reviewed jointly to ensure compliance, performance, and customer-specific isolation across language models and tenants.

Solution

Roko labs developed a custom AI-powered compliance orchestration system to automate the Medical, Legal, and Regulatory (MLR) review. The system enables business users to submit documents, which are then analyzed against country-specific regulation guidelines (e.g., FDA 202.1) and product reference documents. Annotated results are returned, highlighting compliance issues for expert review

Solution

Roko labs developed a custom AI-powered compliance orchestration system to automate the Medical, Legal, and Regulatory (MLR) review. The system enables business users to submit documents, which are then analyzed against country-specific regulation guidelines (e.g., FDA 202.1) and product reference documents. Annotated results are returned, highlighting compliance issues for expert review

Solution

Roko labs developed a custom AI-powered compliance orchestration system to automate the Medical, Legal, and Regulatory (MLR) review. The system enables business users to submit documents, which are then analyzed against country-specific regulation guidelines (e.g., FDA 202.1) and product reference documents. Annotated results are returned, highlighting compliance issues for expert review

Execution Details

Roko’s AI engineers designed a robust system architecture centered around a fine-tuned single model, incorporating Retrieval Augmented Generation (RAG). This approach enabled the system to effectively process domain-specific knowledge, ensuring precise extraction of key information from complex medical and regulatory documents.

Execution Details

Roko’s AI engineers designed a robust system architecture centered around a fine-tuned single model, incorporating Retrieval Augmented Generation (RAG). This approach enabled the system to effectively process domain-specific knowledge, ensuring precise extraction of key information from complex medical and regulatory documents.

Execution Details

Roko’s AI engineers designed a robust system architecture centered around a fine-tuned single model, incorporating Retrieval Augmented Generation (RAG). This approach enabled the system to effectively process domain-specific knowledge, ensuring precise extraction of key information from complex medical and regulatory documents.

MLRAI Admin UI

Application for administrators to configure regulatory guidelines, upload product references, and view results.

MLRAI Web API

Central hub for all requests, secured with Basic Auth. Connects Document and Admin UI to backend processing, queues jobs, and persists results.

MLRAI Process Service

Service that orchestrates the document analysis pipeline: content extraction, AI analysis, annotation, and persistence.​

Pagination & OCR Lambdas

Split PDFs into pages (mlr-ai-paginate) and convert page images into structured text and spatial data.

Technology

The system is powered by a suite of specialized AI components that work together.

Technology

The system is powered by a suite of specialized AI components that work together.

Technology

The system is powered by a suite of specialized AI components that work together.

Distributed AI Workflow

We delivered a system where OCR, retrieval, and language models each play a specialized role, working in coordination to provide layered analysis without relying on a single monolithic service.

Config-Driven Intelligence

All AI behavior is governed by prompts, guidelines, and references that can be updated instantly—no retraining or redeployment required—significantly reducing maintenance overhead.

Orchestration & Control Layer

A centralized process service manages sequencing across SQS queues, Lambdas, and APIs, ensuring reliable execution, fault tolerance, and consistent results.

Agile Testing & Iteration

Through Swagger APIs and admin tools, prompts and workflows can be quickly adjusted, tested, and validated, enabling fast cycles of improvement.

Cloud-Native Scalability

The platform leverages AWS-managed services (ECS Fargate, Lambda, S3, Bedrock, OpenSearch, PostgreSQL) to scale automatically and adapt easily to new regulations, products, and AI models.

Testimonials

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“I'm very impressed with the team.  They have picked up on the problem domain very quickly, and I find them incredibly easy to work with.  Glad we engaged with you."

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Shout-out to the AI team -- they gave us a very professional and detailed demonstration of the workings of the AI product. It was exactly what I had hoped for!”
Kevin Collins,
Chief Technology Officer

Canopy Life Sciences

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“Our internal team had great feedback and was very positive about the improvements. I’m really pleased with how it’s all turned out!”
Bouchra Lahnin,
Project Manager,

Canopy Life Sciences

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