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What a product design phase needs from you before it starts

A product design phase is a four to six week engagement in which a product manager, a UX designer and a software development lead turn a product idea into a scope that can be estimated and built. Those three roles run the work, but several decisions that shape the outcome belong to the client and are made before it starts: who holds decision authority, how much time domain and technical experts can give, how much of the technical role survives price negotiation and what "complete" is taken to mean. This article sets out what each role does, what each needs from the client and how to check readiness before signing.

Ionut Lomer - 23 Sep 2026

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Stefan Sarbu - 3 Aug 2026

How to choose a product design agency in 2026

Choosing a product design agency comes down to three things that are hard to read from a proposal: how the team thinks about problems, who actually does the work and whether the scope matches what the budget can carry. Price ranges are useful for spotting an outlier, but they rarely separate a good fit from a bad one. The more reliable signal is whether an agency asks difficult questions before it shows you screens.

Featured Startup Product design
Alex Marciuc - 30 Jul 2026

Why a product design phase comes before the build budget

A product design phase is four to six weeks of structured work sitting between an approved idea and a committed build budget. It produces a validated prototype, a prioritised feature breakdown with ranged estimates, a technical architecture with integration risk resolved, and a release plan with cost scenarios attached. The output is not a set of design artifacts. It is a narrower cone of uncertainty, and a scope that can be cut without breaking. Teams that skip the phase rarely save the money. They move it into the build, where it gets spent on rework nobody planned for.

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Tudor Iordache - 2 Jul 2026

Six principles for AI systems that work in production

The AI projects that work in production share a small number of structural decisions. None of them is about the model. All of them are about the system around the model: the threshold defined before the build begins, the constraint treated as architecture rather than an obstacle, the surface designed as the actual product, the MVP scoped as the first instance of the platform, the human kept in the loop as a permanent feature rather than a transitional one. The discipline of building AI well is the discipline of getting these decisions right early enough for the model to do its job, and of refusing to defer them until the cost of deferral becomes unavoidable.

Technology Featured AI
Stefan Sarbu - 29 Jun 2026

Five MVP decisions that turn an AI MVP into an enterprise AI platform

The MVP-to-platform transition fails not because the technical architecture is wrong, but because most teams treat it as a scaling problem when it is actually a redesign problem. The MVP gets you to one customer. The platform requires you to be honest about what was specific to that customer and what is genuinely transferable. The teams that get this right do not generalize reactively. They build the MVP knowing which decisions will need to be revisited at the platform stage, and they revisit them deliberately.

Technology Featured AI Architecture
Tudor Iordache - 24 Jun 2026

Human-in-the-loop AI systems where judgement stays in the workflow

Human-in-the-loop is not the version of AI you build while waiting for the model to get good enough to replace the human. It is the version of AI that actually works in domains where being wrong is expensive. The teams treating it as a transitional design are building systems that will eventually be replaced. The teams treating it as the destination are building systems that compound trust and improve over time.

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Stefan Sarbu - 18 Jun 2026

How do you build RAG systems that work in production?

Most RAG implementations are built to produce answers. The ones that work in high-stakes environments are built to produce answers with calibrated confidence, source attribution the user can verify, and behavior that changes when confidence is low. The hard engineering work sits in four places most teams underestimate: chunking strategy, embedding quality, confidence calibration, and how the system surfaces its own uncertainty. The teams that get RAG into production treat confidence as a product feature rather than a model property, and they treat "I do not know" as a legitimate answer the system needs to be trusted to give.

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