Build the foundation once.
Reuse it across AI projects.
A shared engineering framework gives a pharmaceutical company reusable components for structured, validated language-model workflows.
A leading pharmaceutical company
The challenge
A leading pharmaceutical company needed a scalable, future-proof framework to support the rollout of multiple AI projects. The main challenge was to build a consistent foundation that could automate and improve various business processes, while ensuring reliability, reusability and interoperability across different use cases.
They were looking for a solution that would reduce duplicated effort, speed up project delivery and keep output quality high, especially in critical contexts where LLMs are involved.
The solution
We developed a generic, reusable AI framework, designed specifically to support the client's future GenAI initiatives.
At its core is an LLM-minded API framework for Python, optimised for structured, validated and reliable language model integration. Key features:
- Structured LLM output using Pydantic models and dataclasses, for clean integration between LLM reasoning and business logic
- A validation-first architecture: each LLM response is checked automatically and improved if it doesn't meet the defined criteria
- Support for both closed-source and locally hosted models, so it works with providers like OpenAI and Google, or with internal deployments
- Scalable, production-grade API deployment, built on FastAPI for high concurrency and easy integration with external services
- Rate limit handling at the component level, for robustness in real-world, high-volume environments
- Engineered components that complement the LLMs, reducing hallucinations by combining generative output with structured, rule-based validation
We also packaged several ready-to-use components for reuse across projects: a table interrogator, a multi-agent system, a translator, a content tagger and an evidence generator.
The result
A unified, robust platform that standardises how AI capabilities are developed, validated and deployed within the organisation.
The framework makes AI projects more efficient, maintainable and reliable. New solutions can be built faster, with less overhead and more confidence in their quality.
By combining the flexibility of LLMs with solid engineering, the framework lets the client scale GenAI across domains, from internal automation to external-facing solutions, while staying in control and consistent.