From Research Code to Revenue-Ready Protein Design

Executive Summary

Our product and engineering team supported a breakthrough biotechnology company in transforming their AI-driven protein design model into a scalable SaaS platform built for real-world impact. The scientific core—a powerful AI model capable of designing and optimizing proteins with unmatched efficiency—had already demonstrated its potential in early-stage research. What was missing was the infrastructure to turn this capability into a commercially viable platform. Over a multi-year collaboration, our teams helped engineer a cloud-native SaaS product, integrate MLOps pipelines, and evolve the platform into a credible, investor-ready asset. The result: a scalable solution that shortened the path from scientific discovery to real-world therapeutic and materials innovation—and helped secure €2 million in investment to fuel further growth.

Challenge

The founding team created a state-of-the-art AI model that designs and optimizes proteins with far fewer lab experiments. The science was proven, but the surrounding product was fragile: notebooks sat in disparate repositories, experiments were hard to reproduce, onboarding new scientists required weeks of hand-holding, and investors struggled to see a scalable business. The company needed a product and infrastructure partner to translate research code into a secure, reliable SaaS platform without slowing ongoing scientific discovery.

Solution

Our engagement began as a close collaboration between domain experts and our product, AI, and engineering teams. From the outset, we focused on creating a platform that would not only be technically robust but would also clearly communicate the value of the underlying science to users and investors alike.

The first step was architecting the SaaS platform from the ground up. We assembled a dedicated cross-functional team including lead engineers, cloud infrastructure specialists, QA, DevOps, and product managers to ensure that every component—from UI to backend to deployment—met enterprise-grade standards for security, reliability, and scale. The application was built cloud-native, supporting elastic scale and future integrations, while enforcing strict data governance and role-based access controls suitable for sensitive research environments.

Alongside the platform development, we worked hand-in-hand with the biotech team to fine-tune the existing AI models and implement new ones to support emerging use cases. We integrated full MLOps pipelines to allow for rapid iteration, continuous training, and production-grade deployment of new models with minimal friction. This made it possible to safely evolve the platform’s intelligence layer without compromising stability or performance.

Throughout the process, we prioritized communication between the scientific and engineering sides of the project. Our team acted as a translational layer—ensuring that complex biochemical processes and requirements were accurately reflected in the product workflows. The SaaS platform ultimately became more than a tool; it functioned as a living, extensible system for applied research and experimentation, accessible to users with varying degrees of technical expertise.

By the end of the initial development cycle, the biotech company had in hand a production-ready SaaS platform that could support customer onboarding, live model experimentation, and investor demos. The maturity of the system played a critical role in helping the company secure €2 million in investment, enabling it to grow its operations and expand into new application areas across healthcare and life sciences.

Product & Platform Foundation

• Designed a multi-tenant SaaS architecture with strict tenant isolation, role-based access control, and audit trails suited for regulated labs.
• Built a TypeScript/React front end with guided workflows that speak the language of bench scientists while exposing powerful parameter controls.
• Instrumented analytics and billing hooks to support enterprise licensing models.

Integrated MLOps Pipeline

• Containerised the core protein models, wrapping them with reproducible FastAPI services and automated dependency management.
• Implemented Kubeflow pipelines for training, evaluation, and deployment across staging and production clusters.
• Added experiment tracking with MLflow and lineage reporting so scientists and regulators can replay every result.

Translational Research Workflows

• Created project workspaces that link simulation runs to wet-lab verification tasks, capturing decisions, assumptions, and notebook outputs.
• Embedded domain-specific templates for enzyme design, antibody affinity, and materials discovery, accelerating onboarding for new use cases.
• Established customer onboarding kits with secure data ingestion, validation, and monitoring to keep IP safe.

Outcomes

• 65% faster from simulation to lab validation as experiment handoffs became structured and trackable.
• 4× increase in model deployment frequency, enabling faster iteration on new scientific breakthroughs.

Why It Matters

Breakthrough science needs more than state-of-the-art models—it needs a product that partners can trust. Our product, ai and engineering team bridged the gap between research and commercialization, giving scientists intuitive tools while giving executives the confidence to scale, sell, and deploy their platform globally.

What You Can Expect Working with Dreamloop Studio

When you engage with Dreamloop Studio, you gain access to a product and AI engineering team that knows how to bring scientific breakthroughs to life. We specialize in helping deep-tech founders and biotech innovators go from concept to commercial product—combining robust infrastructure, thoughtful product design, and integrated AI capabilities into a single platform that’s ready for real-world use. Our teams are fluent in both code and science, enabling seamless collaboration with research-heavy teams while delivering scalable, secure technology. Whether you're building novel therapeutics, bioengineering tools, or next-gen diagnostics, we help you bridge the gap between lab and launch—so your innovations can ship faster, work reliably, and earn the trust of partners, users, and investors alike.