Life Sciences, Pharma, and Biotech
Software Development
Nearshore engineering teams for the systems science actually runs on: clinical trial platforms, LIMS and instrument integrations, genomic data pipelines, and the models built on top of them.
Next starts here.
Engineering partner to Foundation Medicine since 2012, to Bayer’s Calantic radiology platform, and to Flatiron Health, a Roche affiliate.
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The Science Moves Faster Than the Systems Around It
The bottleneck in most life sciences organizations is not the science. It is that the assay data, the study data, and the commercial data live in systems that were never designed to meet, and every question that crosses them gets answered by hand. Meanwhile the evidence a regulator or a sponsor expects gets assembled after the fact, from records that were never built to produce it.
We start from what is actually broken. Nearshore engineers embed with your team to build the pipelines, integrations, and platforms that connect the lab to the clinic to the business, with traceability and documented change control treated as part of the deliverable rather than overhead bolted on before an inspection.
What We Build
for Life Sciences
Four practices that plug in wherever the work is stuck, from a single integration between two lab systems to a multi-year platform programme.
Life Sciences Data Engineering
Pipelines that move assay output, trial data, and real-world evidence into a model your scientists can query. We build the ingestion, the lineage, and the reconciliation layer, then hand over data engineering for life sciences your team can extend without calling us back.
Outcome: fragmented lab, trial, and commercial datasets become one queryable model with lineage you can show an auditor.
Clinical Trial Software Development
CTMS, eCOA and ePRO platforms, site portals, and the integration work between your EDC, safety, and document systems. Our clinical trial software development services treat the audit trail as a feature of the build, not a document assembled afterwards.
Outcome: sponsors and monitors get the trail they ask for because it was generated while the work happened.
LIMS, ELN, and Instrument Integration
Sample tracking and chain of custody, workflow automation, and the connectors between benchtop instruments and the systems of record. LIMS development that fits the lab you actually run, including migration off a legacy platform without losing historical sample data.
Outcome: the lab stops reconciling spreadsheets and starts trusting a single sample record.
AI, Analytics, and Discovery Platforms
Target identification, virtual screening, and patient stratification models, plus the analytics and BI layer that makes the output legible to people who are not data scientists. See AI drug discovery for life sciences.
Outcome: models that reach the scientists who need them, with the data plumbing underneath them maintained.
How These Engagements
Actually Run
STEP 01
Diagnose
We sit inside your delivery rhythm before we propose anything: where validation actually stalls, which integrations break under load, what the last inspection flagged. You get a written diagnostic naming the friction points and a sequenced plan, mapped against your study calendar rather than our sprint calendar.
STEP 02
Build
Senior nearshore engineers join your teams under the engagement model that fits. Every engineer has cleared four gates before meeting a client: technical assessment, live coding review, English proficiency, and culture fit. Fewer than 2% of applicants are hired, which is why we do not staff a life sciences build with generalists.
STEP 03
Operate
We hand back working practice, not a binder. Traceability and documented change control stay part of the deliverable, so the evidence your quality function needs keeps being produced after we step back. Most engagements graduate from a single squad into a longer partnership across the portfolio.
16+ years, 1,000+ engineers, 2,500+ projects, 98% client retention.
Specialist Life Sciences Solutions
Five practices with enough depth to warrant their own pages. Each one covers the delivery approach, the compliance posture, and the questions buyers in that area tend to ask before they commit.
Data Engineering
Biotech pipelines, clinical trial warehouses, real-world evidence platforms, and migration off legacy data stores. The foundation most life sciences AI work turns out to be missing.
Clinical Trial Software
Custom CTMS, eCOA and ePRO platforms, EDC integration, and patient recruitment portals, built for the audit trail a sponsor will ask to see.
LIMS Development
Sample tracking and chain of custody, workflow automation, instrument integration, regulatory reporting, and cloud migration for labs outgrowing their current platform.
Data Analytics & BI
Trial, commercial, and real-world evidence in one model, with regulatory reporting automated and the data-integrity trail intact behind it.
AI Drug Discovery
Target identification engines, virtual screening platforms, trial optimization, and the automated data pipelines that keep those models fed.
What Regulated Delivery Actually Requires
We provide support for GxP-validated environments and FDA-regulated software. That is a statement about how we build, not a certificate we hold on your behalf: the obligation stays with you, and our job is to produce evidence continuously so your quality function is never reconstructing it.
It helps to be precise about scope. FDA’s own guidance on 21 CFR Part 11 limits the rule to records a predicate rule requires and that you keep electronically in place of paper, or that you submit to the agency. Knowing which of your systems are actually in scope is the difference between a proportionate validation effort and a year of unnecessary paperwork.
The same precision applies in Europe, where the Clinical Trials Information System gives sponsors a single online application across EU and EEA member states under Regulation (EU) No 536/2014. Systems that feed it need to produce data in the shape it expects, which is an engineering problem long before it is a regulatory one.
Work We Can Point At
Three engagements in oncology, genomics, and radiology, each running for years rather than a quarter.
Foundation Medicine
A relationship running since 2012, powering genomic cancer profiling. We migrated the core reporting system from Node.js and Oracle to Postgres without disrupting live reporting, and delivered three flagship genomic products. The platform supports five unique cancer tests and has delivered millions of reports to physicians and patients.
Flatiron Health, a Roche affiliate
More than two years enhancing cancer research tools across five or more product teams. A nearshore team of software, quality, DevOps, and data engineers built the scalable pipelines that kept research tools reliable and accurate, turning fragmented datasets into decision-ready insight for pharma and clinical users.
Bayer, Calantic platform
Two and a half years enhancing a suite of AI-driven radiology solutions. Our embedded team expanded backend capability, introduced automation testing, and reinforced the Google Cloud foundation, while our Data Engineering practice covered the integrity and governance of the radiology data underneath it.
There is more in the same vein, including FLXplorer for Flexomics, which cut manual genomic analysis from hours to minutes, and our wider healthcare engineering work.
Engineering Depth by Technology
Language choice is not cosmetic in this sector. It shapes how you handle genomic scale, how you evidence an electronic signature, and how long a validated system stays maintainable. Internal pipelines increasingly have to interoperate with public resources as well: the National Cancer Institute’s Genomic Data Commons is a unified repository and knowledge base built to enable data sharing across cancer genomic studies, including TCGA and TARGET. Our blog on programming languages for biotech covers the trade-offs; the pages below cover how we work in each one.
For Life Sciences and Pharma
- Python — Bioinformatics, modeling, and pipeline work.
- Java — Enterprise systems and submission reporting tooling.
- .NET — Validated line-of-business and lab systems.
- Node.js — EDC, CTMS, eTMF, and safety-system integration.
- React — Trial management UIs and bioinformatics visualization.
- Angular — Structured, long-lived regulated front ends.
For Biotech
- Python — Genomics, drug discovery modeling, and LIMS work.
- Java — High-throughput processing and platform backends.
- .NET — Instrument-adjacent and enterprise biotech systems.
- Node.js — LIMS and ELN integrations, instrument watchers.
- React — Genome browsers and bioinformatics dashboards.
- Angular — Audit-trail viewers and compliance dashboards.
Hiring for a named role instead? We place data engineers, Python developers, data scientists, AI engineers and QA engineers into life sciences teams, and our Data & AI Center of Excellence backs them.
Four Ways to Work
with Our Life Sciences Teams
You are not buying hours. You are choosing the shape of delivery that matches where the programme actually is. Compare them on our engagement models page, or read how this fits the wider case for software development outsourcing and nearshore delivery.
Outcomes
We own a defined result end-to-end: discovery, build, and release.
Best for: a stalled platform or a discovery programme that needs someone accountable for shipping it.
Teams
A self-managed nearshore squad embedded in your engineering organization. We bring the team; you bring the scientific context.
Best for: adding delivery capacity without adding another management layer.
Capacity
Senior engineers added to the teams you already have. You manage; we strengthen.
Best for: a specific gap, such as data engineering or automated validation testing.
Managed Services
We run the operation end-to-end: delivery, support, and continuous improvement under defined SLAs.
Best for: long-lived lab or trial platforms that need to stay up and stay current.
Why Life Sciences Teams Stay With Us
The Nearshore Advantage That Delivers Results
We combine the cost benefits of offshore with the collaboration ease of onshore.
Our teams across the Americas corridor work in your timezone, speak your language, and understand your culture.
Timezone Collaboration
Work with teams who share your working hours. No late-night calls or delayed responses.
Rapid Team Assembly
Get your dedicated team up and running in 2–4 weeks, not months. We match talent to your specific needs.
Enterprise Quality
Our engineers are rigorously vetted and trained in best practices used by Fortune 500 companies.
Scalable Solutions
Start small and scale up seamlessly. Our flexible engagement models adapt to your changing needs.
Cultural Alignment
Strong cultural affinity with US business practices ensures smooth communication and collaboration.
24/7 Operations
Your success is our priority. Get dedicated account management and 24/7 operations when you need it.
Verified Reviews
Average Review Rating
Recognition
Inc. 5000
Fastest Growing Companies
10 time recipient
Portland's Fastest Growing
Portland Business Journal
10 years in a row
Clutch
Top 5 Nearshore Software Developers in Mexico
2025
Partners
AWS Partner Network
Amazon Web Services
Databricks Partner
Data + AI Platform
Ready to Unblock the Systems Behind the Science?
Book a 30-minute working session with a senior engineer who has shipped in regulated life sciences. No deck. Bring one specific problem, a stalled LIMS migration, a trial integration that keeps breaking, a genomic pipeline that will not scale, and we will tell you how we would approach it.
