Startups are launched under conditions of limited resources, intense competition, and constant uncertainty. They move fast, operate lean, and are under constant pressure to deliver results at any cost — often at the expense of engineering depth. At this stage, the pursuit of product-market fit seems more critical than scalability, infrastructure, or technical strategy.
However, both research and industry practice suggests otherwise: everything that isn’t carefully thought through at the beginning — architecture, systems, decisions — will cost much more later. According to the Flexera State of the Cloud (2023), 54% of companies faced scalability issues due to technical decisions made early on. These are not isolated cases but a consequence of lacking a systematic engineering approach — a lack of R&D.
This article explains what it means to embed R&D into the foundation of a startup, why an MVP without engineering discipline is a dead end, and how early product decisions can become intellectual property rather than technical debt.
1. What a Product Really Is
A product is not just code or development speed. It is a working solution to a user's real problem. It can be digital (a web service, an API), physical (a device), or service-based (e.g., 24/7 support). The key is that it delivers value to the end user.
Implementation must not be detached from product intent. When an engineer understands the business goal and context of use, they stop merely writing code and start building a product. This is the essence of R&D.
Companies often assign a product manager to oversee the execution of tasks by the R&D team. However, the development team should also be directly involved in preparing the PRD.
Relying solely on a finalized document does not eliminate responsibility for the outcome. It is essential not only to understand the technical implementation but also to consider the business value of the product and, when necessary, to adjust or refine the requirements to ensure the project’s success.
2. MVP Without Engineering Is a Dead End
The minimum viable product (MVP) is often mistaken for a stripped-down version of the final solution. In reality, it’s not about the number of features — it’s about focus: validating a hypothesis, not impressing with polish.
But for an MVP to truly serve its purpose, it must be built not just quickly but deliberately. This is where R&D becomes essential. A strong engineering perspective helps define a technical minimum that won’t block future scaling and enables architectural decisions that support growth if the hypothesis proves valid. In this model, engineers must be involved from the ideation stage — so that the MVP doesn't become a dead-end.
An effective MVP:
- solves a real problem,
- is built in weeks, not months,
- is oriented toward feedback, not polish.
Yet many MVPs are built hastily, with no plan for how they will evolve. As a result, they are fragile and unscalable, requiring full rewrites.
A practical example is the development of a time-tracking system based on facial recognition technology. At the MVP stage, the priority should not be building a user dashboard or integrating with an ERP system — these elements are not critical for validating the core hypothesis.
The main objective is to verify how facial recognition works: its accuracy, speed, and reliability under real conditions. To achieve this, it is sufficient to implement two basic endpoints: one for adding new employees and another for recording check-in and check-out events. This functionality can be tested using an existing photo dataset or manually captured images (for example, photos of office staff) to simulate realistic usage. All additional infrastructure can be developed later as needed; this approach has been proven effective in practice many times.
Revolut is a clear example of how an MVP can work in practice. In the early days, the team focused on testing a core hypothesis: users needed a simple and convenient multi-currency card with instant transfers. To validate this, they built a minimal product — a mobile application offering only basic currency exchange and a virtual card. More complex elements, such as full banking integrations and broad country support, were deliberately postponed until later stages. This approach made it possible to quickly observe user behavior and to confirm real market demand before investing resources in building the complete infrastructure.
3. R&D as a Decision-Making Infrastructure
An engineer involved from day one doesn’t just complete tickets — they shape the system. This means contributing to goal-setting discussions, proposing scalable technical solutions, and defining the minimal architectural core. This approach reduces the risk of having to rebuild from scratch after early user growth and allows technical efforts to align with business value.
Engineering culture is not a set of processes but a level of engagement. If engineers are only executing tickets without influencing decisions, the product loses adaptability. If they understand goals and can challenge decisions, the entire team benefits.
Just like in aviation, where the probability of accidents decreases when the co-pilot has the right to speak up, development also requires this dynamic. Engineers must be able to say, "This is going the wrong way," and be heard.
The DORA Report (2023) confirms that teams where engineers are involved in architecture deliver more stable and maintainable solutions. According to McKinsey's Developer Velocity Index (2020), such teams bring products to market 20–30% faster and faceless critical technical debt.
Sometimes, a team gets a 50-page PRD that needs to be approved and put into development by the next day — reading through such a document alone can be challenging, and understanding it well enough to plan the implementation is even harder. In one of our projects, an engineer did exactly that and also spotted that part of the requirements were unnecessary: it was about adding extra monitoring for a new feature. He suggested removing it — and he was completely right.
This decision saved three weeks of development and sent a clear signal to the whole team: a PRD is not set in stone and can be questioned. The redundant requirements were dropped immediately, the feature worked as expected, and the team quickly moved on to the next task. One engineer’s careful approach saved time and money and showed everyone that an engineer’s input truly shapes the final product.
4. Architecture as an Asset: Building IP, Not Just Code
Early-stage startups often default to a “just make it work” mindset. However, architectural decisions made in the first months either become growth drivers or expensive liabilities. R&D thinking is not about perfectionism — it's about predictability: designing a system that won’t collapse under pressure.
Key questions: Can the current implementation scale? Where are the bottlenecks? How will migration work if the hypothesis proves true?
At the earliest stage, it’s the team’s responsibility to choose a technology stack that won’t limit growth later on. It’s worth considering the long-term impact: the database selected today will eventually become deeply integrated into dozens of services maintained by different developers. Is it really the best fit for the actual use cases? Should it be SQL or NoSQL? How expensive will it be to replace once the entire codebase depends on it? The reality is simple — replacing core infrastructure later costs far more than making the right choice upfront.
That’s why it’s important not just to pick technologies with growth in mind but to implement them in ways that keep future options open. For example, building a separate service layer responsible for all database interactions from day one can save significant time and money down the line. If the team ever needs to migrate to a different database, the switch will be far easier and far less disruptive.
It is not enough for the product to work — it must become a defensible asset. Architecture design, data handling, and automation become technological capital. An MVP without engineering logic remains a demo. An MVP with engineering logic becomes the foundation for scalable, protectable intellectual property.
5. Agile Is a Mindset, Not a Ritual
Methodologies like Scrum or Kanban only work when used to accelerate feedback and team alignment. Otherwise, they become empty rituals.
Effective Agile in a Startup:
- breaks down hypotheses into testable parts,
- adapts architecture for change,
- enables fast action based on feedback.
According to Atlassian's State of Teams (2022), teams using Agile as a mindset perform better in adaptability, response speed, and engineering talent retention.
In many traditional projects, the architecture is designed in full upfront, and the team is expected to follow it exactly. Robert C. Martin (known as Uncle Bob) explains that the Agile approach works differently: the team designs only what is needed for the current stage and refines it step by step as the project evolves.
Instead of spending months on a detailed architecture, Agile teams break it down and adjust it continuously. This makes it easier to handle change and keeps the process flexible by default.
Uncle Bob also highlights the value of short feedback loops. The faster a team ships and tests code, the sooner issues are found and the less they cost to fix. Releasing once a month is risky and expensive; deploying daily keeps errors small and manageable. This is a core Agile principle: shorter cycles reduce the cost of mistakes.
Conclusion
Engineering thinking is not a constraint. It reduces rework, supports growth, and enables long-term value creation. An MVP without R&D is a temporary fix. An MVP built with engineering logic is a technological asset.
A startup capable of turning an idea into a functioning system gains not only hypothesis validation but also a foundation for scaling and protection. This is why R&D should not be an auxiliary function but part of a product’s DNA — from day one.