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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.

Stefan Sarbu - 29 Jun 2026

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

Tudor Iordache - 24 Jun 2026

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Tudor Iordache - 18 Jun 2026

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Tudor Iordache - 10 Jun 2026

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Ilie Ghiciuc - 2 Jun 2026

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Ilie Ghiciuc - 25 May 2026

Why AI automation ROI is highest on repetitive, high-volume processes

Document extraction accuracy at scale is a sequence of failure modes, not a single problem. Fine-tuning an open-weight visual-language model on domain-specific data closes most of the distance from a general-purpose baseline, but rarely reaches the threshold a business case actually requires. Pushing past that ceiling depends on three engineering techniques applied in sequence, each addressing a failure mode the others cannot. There is a question that comes up early in almost every AI conversation we have with founders and product leaders: "Is our process a good candidate for this?" It sounds like a simple question. It is not. A recent MIT study reports that 95% of enterprise generative AI pilots fail to deliver measurable business impact, and that the primary cause is not the technology itself but the absence of workflow integration and a defined outcome before the build begins. Most teams answer the question by focusing on the technology first, evaluating what a particular model or agent framework can do, and then searching for a process to apply it. That sequence produces many promising pilots but leaves production systems in short supply.

Technology Featured AI
Ionut Lomer - 20 May 2026

The agentic AI starter kit: minimum viable setup for software teams

Part 2 of 2. This article follows "Claude is not a chatbot: how to use it on real software projects". Agentic AI is like a new machine. A powerful one. But nobody shipped a user manual with it, and every company in the room is currently trying to figure out which button does what. That is the honest state of things in 2026. Anthropic is shipping new features faster than most teams can absorb them. Documentation reads like walking into a store where every shelf has something new and there is no map. The instinct is to explore everything. That instinct is the problem.

Technology Trending Featured AI
Tudor Iordache - 12 May 2026

What Gartner's 2026 tech trends mean for product teams, not CIOs

Of Gartner's ten 2026 technology trends, four matter disproportionately for product builders: AI-native development, multiagent systems, domain-specific language models, and digital trust. AI-assisted development works only when grounded in structured context, not clever prompts. Multiagent systems are already in 80% of enterprise apps shipped in Q1 2026, yet 88% of agent pilots never reach production, because the bottleneck is product design, not model quality. Domain-specific models outperform general ones for targeted use cases, but only when a pre-development business case has set accuracy and cost thresholds. Trust is becoming a visible part of the product surface, and in regulated and European markets it is now a baseline requirement. Teams that win in 2026 will pick the two or three trends that intersect with their roadmap, not try to act on all ten. Gartner published its top strategic technology trends for 2026 last October, presenting ten trends grouped under three themes: The Architect, The Synthesist, and The Vanguard. The recap wave that followed was predictable. Within weeks, dozens of consultancies and vendors had published their own breakdowns, each walking through the same ten trends with broadly similar commentary aimed at the same audience: enterprise CIOs.

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Ilie Ghiciuc - 8 May 2026

AI document extraction accuracy: why fine-tuning alone is not enough

Document extraction accuracy is not a single problem but a sequence of failure modes resolved in order. Fine-tuning an open-weight visual-language model closes most of the gap from a general-purpose baseline, but rarely the gap that matters: the one between early performance and the threshold a business case requires. Closing that distance is a separate engineering effort, and the techniques that get there compound on each other rather than substitute for each other.

Technology Featured AI
Ionut Lomer - 29 Apr 2026

Claude is not a chatbot: how to use it on live product builds

Most teams have adopted AI assistants in some form by now. Few have built systems around them. A single conversation with Claude is transactional. You type a question, you get an answer, and the session ends. That has real value, but it scales like a calculator: only as fast as you type. A system built with Claude is different. Context is loaded before the work starts. The model knows the project methodology, the team constraints, and the deliverable format before a single instruction is given. Output is consistent across sessions. The re-briefing tax disappears.

Technology Team Featured AI
Ilie Ghiciuc - 22 Apr 2026

How regulated companies build AI without third-party APIs

At some point in almost every AI project we work on with a regulated client, someone on the team says some version of the same thing: "We can't send that data outside." It usually lands like a problem. The obvious implementation path, connect your data to a capable hosted model, get results back, iterate, suddenly has a wall across it. SOC 2 compliance, data residency requirements, security perimeters built around credentials and sensitive client information: any of these can make the default approach to AI architecture a compliance violation rather than a technical decision.

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