Insights by Tudor Iordache

### Tudor Iordache **Chief Technology Officer at Thinslices** As the CTO at Thinslices, I've had the privilege of leading our talented team in delivering hundreds of innovative digital products. Over my 11 years here, I've grown from an iOS intern to various roles including iOS Developer, Lead Software Engineer, and Technical Lead. My expertise in SwiftUI and Flutter, combined with my passion for technology, has enabled me to drive our projects to success and ensure we meet our high standards of quality and innovation. Every project is a story waiting to be written. I like to think that I can contribute to the development of the characters involved and the plot of it. I’m still trying to figure out what’s the recipe to create a best-seller, but with every project passing I feel like I’m closer to the answer.

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
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.

Featured Publishing AI
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.

Trending Featured AI
Ilie Ghiciuc - 10 Jun 2026

AI browser agents: the gap between demo and production

Browser-use AI agents are most valuable when they are a component in a larger system, not when they are deployed as a complete solution. The teams getting durable value treat the agent as a navigation layer feeding into deterministic downstream processes, with serious operational infrastructure around it. The teams that struggle deploy the agent and expect the rest of the system to follow.

Technology Trending Featured AI
Ilie Ghiciuc - 2 Jun 2026

Edge AI inference: what it means for your product architecture

AI inference is moving toward the edge because centralized cloud processing introduces latency, egress costs and data residency constraints that compound as inference volume scales. The decision of where to run inference is determined by five workload characteristics: latency tolerance, data volume, compliance requirements, operational resilience needs and cost profile over time. Most production architectures resolve this by splitting responsibilities between cloud and edge, with the operational overhead of managing a distributed inference fleet remaining the primary factor that determines when the transition is viable.

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.

Technology Trending Featured
Tudor Iordache - 16 Apr 2026

Edge computing vs. cloud computing: how to choose your architecture

The edge vs. cloud decision is a workload classification problem, not a technology preference. Five factors determine placement: latency tolerance, data volume, compliance requirements, resilience needs and infrastructure cost. Misallocation in either direction carries measurable consequences, defaulting to cloud accumulates latency debt and egress costs at scale, while premature edge investment introduces operational complexity before the business case is established. Organisational readiness is an independent variable that shapes when edge adoption is viable, separate from whether it is technically warranted.

Technology Featured Cloud Architecture
Tudor Iordache - 9 Mar 2026

Best practices for embedding AI in SaaS Platforms

Embedding AI in a SaaS platform is primarily a product and systems challenge rather than a modeling exercise. Effective implementations start by identifying specific workflow problems, validating AI capabilities through controlled experiments, and only then integrating them into platform architecture designed to support evolving models, data dependencies, and operational constraints. Reliable outcomes depend on structured data foundations, transparent human oversight in critical workflows, and clear ownership for monitoring and iteration after release. When these conditions are in place, AI capabilities can evolve as part of the product rather than remaining isolated experiments.

Featured AI SaaS
Tudor Iordache - 16 Jan 2026

Fintech cost optimizations: microservices, AI and the cost curve

Cloud cost has quietly become one of the hardest problems fintech engineering teams face at scale. Not because teams lack visibility or tooling, but because cost is increasingly shaped by architectural choices made under pressure to move fast, stay compliant, and design for growth.

Technology Fintech Featured
Tudor Iordache - 12 Nov 2025

A realistic AI roadmap for healthtech scaleups

AI has become the default headline in every healthtech conversation. Almost every product pitch or investor deck now mentions it, and yet, in practice, most AI initiatives in healthcare still stall before delivering measurable outcomes.

Healthtech Featured AI