Case Studies
Real roles, real teams, real outcomes
A few examples of the kinds of teams Silicon Development supports and the work those engineers were responsible for once they were inside the team.
Selected client work
Each case shows the operating context, the role Silicon Development played, and what changed.
Challenge
A biopharma company used high-performance cloud computing and AI to analyze RNA sequencing data. Their platform identified drug targets, biomarkers, and neoantigens for cancer and neurodegenerative disease research. They had a working MVP but needed engineering capacity to turn it into a full production platform. The work required engineers who could understand scientific domain concepts, collaborate directly with research scientists, and build data-heavy features for a specialized user base.
Silicon Development's role
Silicon Development provided the primary engineering team and worked directly with product leadership to build and scale the platform inside the existing product effort.
Key work delivered
- Transformed the platform from a minimum viable product to a full-featured AI drug discovery system
- Learned key drug discovery concepts to collaborate effectively with the client team
- Built data visualizations that surfaced research insights for decision-making
- Migrated the SaaS application from virtual machines to an Azure-native cloud service with DevOps automation
Outcome
The platform moved from an early-stage MVP to a production system. The work required real domain understanding and close collaboration with the scientific group, not just generic feature delivery.
Team: Lead developer, senior developer, project manager, and quality assurance
Read full case study →BioPharma
BioPharma AI Platform
A biopharma platform needed more engineering support to move from an early MVP into a production system used for drug discovery research.
Read case study →LegalTech / FinTech
Enterprise Litigation Platform
A litigation platform serving financial institutions and insurers needed more delivery capacity inside the existing engineering organization.
Read case study →HealthTech
Healthcare SaaS Platform
A healthcare SaaS platform needed added engineering capacity to improve throughput, maintain delivery, and support a later acquisition.
Read case study →Consumer Tech / Data Platform
Pragma.AI
Pragma.AI needed a team that could build the product across web, mobile, data, and machine learning as it moved from concept into a working platform.
Read case study →Kinds of work these teams needed
The common thread is embedded engineering work in environments where quality and context matter.
Full-stack SaaS platform build
Consumer Tech / Data Platform
Silicon Development was the primary engineering team for a consumer data company, building the full SaaS platform, data ingestion pipeline, and supporting ML models. The team built web and mobile applications from start to finish, working directly alongside the client’s product team.
Rules engine migration
HealthTech / Performance
Migrated a critical rules engine from a monolithic Ruby application to a standalone Java Drools service for a healthcare SaaS platform. Achieved a 19x performance improvement, reducing processing time from 10 hours to under 90 minutes.
AI-powered document redaction
LegalTech / FinTech
Built a file upload processing system that uses AI to automatically redact sensitive information for an enterprise litigation platform. Part of a broader embedded engineering engagement that increased team velocity by over 50%.
Cloud migration and DevOps
BioPharma / Cloud
Migrated a SaaS application from Virtual Machines to a native cloud application service on Microsoft Azure with automated DevOps deployment. Part of a multi-year engineering partnership supporting a scientific computing platform.
Research data visualization
BioPharma / Data
Built a collection of data visualizations for a drug discovery platform to surface research insights for scientific decision-making. Required engineers who could learn domain-specific concepts and collaborate with research scientists.
Acquisition-ready engineering
HealthTech / M&A
Played an integral role in the engineering work that helped a healthcare SaaS platform get acquired by a larger health tech firm. Embedded engineers contributed to feature delivery, code quality, and performance optimization throughout the acquisition process.
What these examples show
The same operating pattern shows up across very different client environments.
Engineers worked inside the client's team and workflow. Across every engagement, Silicon Development engineers used the client's tools, process, and communication rhythm rather than operating as a separate delivery unit.
The work was technically demanding. Rules-engine migrations, cloud platform architecture, AI-assisted document processing, scientific data visualization. These are roles where domain understanding and engineering judgment matter.
Complex environments were normal, not unusual. Healthcare data platforms, litigation systems handling sensitive information, drug discovery tools processing clinical data. Silicon Development's strongest engagements tend to be in environments where quality, security, and domain fit are non-negotiable.
The work produced visible outcomes. 19x performance improvements. 50%+ velocity increases. MVP-to-production platform builds. Acquisition support. Those outcomes came from engineers who were embedded deeply enough to make real contributions.
Good engagements tend to expand. Clients that start with Silicon Development often extend the work or add roles once the model proves itself inside the team.
These examples show what good embedded work actually looks like
Complex environments, real ownership, measurable outcomes. That is where the model is strongest.