Case Study · BioPharma · AI Drug Discovery
BioPharma AI Drug Discovery Platform
10 engineers. 400+ tickets. Research tooling to production SaaS.
A biotech company needed to turn its RNA splicing research into a commercial platform that pharmaceutical clients could use for drug discovery. The internal team had the science and the ML pipeline. They did not have the engineering capacity to build the production application around it. Silicon Development embedded a 10-person team to build the platform from scratch.
Engagement
12+ months
Full platform build
Engineers
10
Cross-functional team
Tickets delivered
400+
Features, bugs, infra, UX
Relationship
8+ years
Still in contact with founders
What the team needed
The client had ML algorithms that analyzed RNA splicing across a large proprietary event database. The research worked. But the tooling was built for scientists, not for pharma partners who needed to run experiments, manage datasets, and review results inside a real product.
They needed a multi-tenant SaaS platform on Azure. The platform had to handle 100+ TB-class genomic datasets, render scientific visualizations in-browser, support branded environments for multiple pharma accounts, and enforce role-based access controls across organizations.
The engineers had to work directly with the life sciences team. The domain is specialized enough that you cannot hand off a spec and expect useful output. The team needed to understand splicing events, case-control experiment design, and how bioinformaticians actually work with data.
Stack
Backend
Ruby on Rails
Frontend
Bootstrap, AG-Grid, Plotly.js
Infrastructure
Microsoft Azure, Docker
Integrations
Genome browser context, scientific data sources, error tracking
What Silicon Development built
The platform that shipped as the client's commercial product.
Experiment engine
Scientists configure and run RNA splicing analysis jobs against the client's ML pipeline, review results inside the product, and iterate on experiments without falling back to research-only tooling.
Results data grid
A high-volume results workspace built for filtering, sorting, search, and export across large RNA splicing result sets, with workflow support for scientific review.
Scientific visualizations
Per-result reports with statistical charts, reproducibility views, genome browser context, and links to supporting scientific references. Built so the interface still degrades cleanly when data is incomplete.
Dataset pipeline
Multiple ingestion paths for 100+ TB-class genomic datasets, along with status tracking, notifications, and preparation steps to make those datasets usable inside the product workflow.
Multi-tenant access control
Multi-tenant account architecture with role-based permissions, project-level access controls, and branded environments for different client organizations.
Infrastructure
Dockerized application. Dev, staging, and production environments on Azure. Error tracking, static analysis, deployment automation. Upgraded the full stack (Ruby, Rails, Bootstrap) during the engagement.
What changed for the client
What happened after the platform reached production.
Platform shipped as the company's flagship commercial product
Client closed a $2M+ seed round after launch
Client secured a research collaboration with a major global biotech
Client raised a Series A from tier-1 venture firms
Client grew total funding to $45M+ across multiple rounds
What this shows about the model
The team learned RNA splicing biology well enough to work directly with research scientists, not just take specifications.
SD owned the full build: experiment engine, data visualization, multi-tenant architecture, dataset pipeline, cloud infrastructure.
400+ tickets across 12+ months. Ten engineers shipping against a real product roadmap.
The platform became the commercial product the client used to raise funding and sign pharma partnerships.
The founding team is still in contact eight years later.
Where to go next
Regulated product teams
How Silicon Development matches engineers into healthcare, biotech, fintech, and other security-sensitive environments.
See regulated teams →How we vet
What Silicon Development checks before an engineer reaches your team, and why the bar changes by role and environment.
See the vetting process →More case studies
Other examples of embedded engineering work across healthcare, legaltech, biopharma, and data-heavy product teams.
See all case studies →A full platform build with embedded engineers
The team learned the domain, built the product, and shipped production software the client still uses commercially. That is how embedded engineering is supposed to work.