What your biotech engineering team should never build
As a biotech company, every resource - minute, dollar, and FTE focus - counts in your race to reach market. While there are some things your engineers should be building internally, there are also tons of things they should not, as these suck away precious resources from the things that matter.
Even the most capable technical teams hit a wall when they realize they're accidentally building an entire software platform.
Last week, we chatted with an SVP of R&D Operations to learn more deeply about how their company uses Kaleidoscope. Their team is exceptionally technical – the kind of group that could build almost anything they set their minds to. And they had. Over the past year, they'd developed several internal tools to wrangle their R&D data, and honestly, they'd done impressive work.
But as we dug deeper into their setup, something interesting emerged. Despite their technical capabilities and the tools they'd already built, there were entire categories of functionality that their engineering team had kicked the can on or looked at and said "nope, we're not touching that." Not because they couldn't build it, but because they recognized that building it would mean committing to something much bigger than they'd bargained for.
Sure, we could build a database. But all the workflow management on top of that? The task assignment, the commenting, the coordination between teams? That's a whole platform.
This is the trap that even sophisticated biotech engineering teams fall into. What looks like a straightforward internal tool quickly reveals itself to be the tip of an iceberg. You start by solving one specific data visibility problem, and before you know it, you're building enterprise workflow software.
The reality is that there are certain categories of R&D infrastructure that no biotech should ever build in-house, regardless of technical capability. Not because it's impossible, but because it's a strategic mistake that will drain precious engineering resources away from what actually matters: your science.
The hidden platform beneath every "simple" R&D tool
When most biotech teams think about building internal R&D tools, they focus on the visible layer – dashboards, data views, maybe some basic reporting. But that's less than 20% of what actually needs to exist for the tool to be genuinely useful.
The other >80% is everything that has to happen beneath the surface. And this is where capable teams get sucked into building far more than they intended.
Take workflow management. Your initial thought might be: "we just need to track which experiments are assigned to whom." Simple enough. But then you realize that assignments need due dates. And due dates need notifications. And notifications need to be smart enough not to spam people. And people need to be able to comment on tasks. And comments need to trigger alerts to relevant team members. And suddenly you're not building a simple tracker – you're building Asana for biotech.
Or consider security. You start with basic user authentication, which feels straightforward. But then you need to share data with external collaborators – your CRO partners, pharma companies you're working with, investors who need due diligence access. Now you need granular permissions, secure external sharing, audit trails, and compliance frameworks (and in biotech, you need complete, unwavering peace of mind that these permissions are appropriately protecting access to your IP). Congratulations, you're now building enterprise-grade security infrastructure.
This is exactly what happens with many customer or prospect teams we speak with.Their engineers build an impressive initial viewer in about a year… but because they started thinking they were building something simple, they didn’t architect it for the complexity that would inevitably follow. And then the real problems emerged. As their science evolved, and their collaboration needs grew, they found themselves not just iterating, but throwing out entire systems and starting from scratch every few months. Each new requirement exposed the limitations of their initial architecture, forcing them to go “0 to 1” over and over again.
This is the hidden opportunity cost. Every month your engineering team spends rebuilding R&D infrastructure is a month they're not spending on the tools and systems that actually differentiate your company. Your engineers should be building IP, not workflow management platforms.
The categories you should never touch
Based on conversations with hundreds of biotech teams, here are the infrastructure categories that consistently turn into platform commitments:
Workflow management infrastructure. This includes task assignment, calendars, commenting systems, mentions, and all the human coordination that happens around your data. What starts as "simple task tracking" inevitably becomes a full project management platform. Your team will need sophisticated notification systems, approval workflows, and integration with external calendars
Security and permissions. User management might seem straightforward until you need to securely share data with external partners. Suddenly you're building role-based access control, audit logging, and compliance frameworks. One customer told us they initially planned to "just add some basic permissions," but ended up spending six months building security infrastructure that still didn't meet their pharma partner's requirements (think SOC2 audit-worthy).
Performance optimization. Pulling data from multiple sources, indexing it properly, and caching it so queries return in fractions of a second rather than minutes. When you're dealing with millions of compounds and thousands of assays, performance isn't optional. But building high-performance data infrastructure is a full-time job that requires database optimization expertise most biotech teams don't have.
Scientific user interfaces. Building dashboards that engineers can use is one thing. Building interfaces that are intuitive enough for scientists who don't want to write SQL queries is entirely different. This requires deep UX research, user testing, and iterative design work. Most importantly, it requires understanding how scientists actually work – something that takes years to get right.
Automation and triggers. "When new data appears that matches these criteria, assign it to this person and notify these stakeholders." Sounds simple. In practice, building a reliable automation system means handling edge cases, running job servers, managing failures gracefully, and creating user-friendly interfaces for non-technical team members to set up their own workflows.
Adaptive field types. Your data structures will change as your science evolves. What you track in discovery is different from what you track in preclinical, which is different from what you track in clinical development. Building a system that can adapt without requiring engineering work every time your science changes is a massive undertaking, and adds a technical blocker to moving forward.
Focus on what only you can build
The best biotech engineering teams we work with have a clear principle: they only build things that are core to their scientific IP or give them a genuine competitive advantage.
Is your novel approach to drug discovery proprietary? Build that. Is your machine learning model for predicting compound behavior unique to your target? Definitely build that. Is your method for analyzing specific assay data something no one else does? Build it.
But workflow management, security infrastructure, and performance optimization for multi-source data queries? These aren’t problems your team should be solving from scratch. At Kaleidoscope, we’ve invested years of engineering time and millions of dollars to build these exact solutions specifically for biotech R&D. We’ve built the platform that handles all the complexity your science demands - from secure external collaboration with CROs to lightning-fast queries across millions of compounds - so you don’t have to.
The companies that emerge strongest from the current market environment will be the ones that maintain laser focus on their core science while leveraging best-in-class tools for everything else. They'll be the ones whose engineering teams spent their time building competitive advantages, not reinventing workflow management.
When you're evaluating whether to build something in-house, ask yourself: "if we build this, are we committing to building a software platform that will cost millions of dollars a year to build and support?" If the answer is yes, and that platform isn't your core business, the decision should be easy.
Your engineering team is too valuable to spend on problems that have already been solved.
Kaleidoscope is a software platform for biotechs to robustly manage their R&D operations. With Kaleidoscope, teams can plan, monitor, and de-risk their programs with confidence, ensuring that they hit key milestones on time and on budget. By connecting projects, critical decisions, and underlying data in one spot, Kaleidoscope enables biotech start-ups to save months each year in their path to market.