Case Study
Roko Labs built a custom AI compliance orchestration system for Canopy Life Sciences in six months. The system automates Medical, Legal, and Regulatory (MLR) review against FDA 202.1 and country-specific regulations. It runs as a distributed AI workflow inside the Veeva Vault platform.
Client
Industry
Services
Project Duration
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 its 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 Platform environment. Working as a fully integrated product and engineering team, we built on 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.
Execution Details
Roko Labs’ 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 is a 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.
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
“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. 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
“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
