About artificial intelligence in practice: models, e-commerce, and SaaS.
One of the most practical Polish business books: the ladder of first customers, pricing, crises and, above all, people. It has two flaws, though – too much corporate English and too much focus on selling the company.
Biased data, black boxes, blurred accountability, and privacy leaks. A look at documented cases, current regulations, and practices that let you build models that are both effective and fair.
The predictive analytics market is growing around 20% a year, and machine learning is changing how SaaS products get planned. What already works, what's still missing, and where your team should start.
Online gardening is not bags of soil and a pair of shears. Customers ask about hardiness zones, clay soil, and whether a plant will survive a courier. Why garden stores need knowledge you cannot dump into a generic chatbot.
In aquaristics, a wrong purchase can kill a reef system that took years to build. Why specialized online stores need contextual, knowledgeable AI support — not another generic chatbot.
How small businesses can implement AI without an IT team and large budget. Analysis of cloud-based solutions, pricing models, and real ROI metrics.
The origin story of Asellio from an e-commerce practitioner and software developer perspective. Why generic chatbots fail in specialized niches and how RAG transforms sales consultation.
Practical guide to business automation using AI. Discover 5 key processes that yield immediate returns, from invoice parsing to lead sourcing.
How to leverage NLP, ML, and predictive analytics to train AI agents that autonomously resolve customer queries 24/7 with full personalization.
Analysis of pet industry e-commerce challenges. Why generic chatbots fail and how to responsibly combine AI, RAG architecture, EU AI Act compliance, and GDPR with Asellio.
Deep technical analysis of autonomous AI agents in e-commerce: from four-plane architecture to A2A protocols and token cost optimization.
New benchmarks show: hybrid approach (RAG + fine-tuning) delivers 86% accuracy vs 75% for base GPT-4. How to choose in practice?
Pinecone for speed, pgvector for cost control, or Lakehouse for governance? Concrete metrics and case studies for small SaaS teams.
Artificial intelligence is ceasing to be a tool, becoming a full-fledged team member. A review of key concepts and challenges in building hybrid teams.
Deep technical analysis of four leading AI coding agents. Comparison of architecture, benchmarks, and real-world production use cases.
Per-token prices of closed and open-weight models can differ by more than 25x. Learn the routing, caching, and compression strategies that can cut your bills dramatically.
Major AI labs are transitioning from language models to World Models. Analysis of NVIDIA's Cosmos architecture and the future of physical AI.
Embedding technology is revolutionizing product search and recommendations without the need for massive AI models. See how this method works.
How AI, computer vision, and intelligent sensors are revolutionizing marine aquarium monitoring by detecting problems before they cause losses.
How to build an efficient data science pipeline in a small SaaS company? A practical guide to architecture, tools, and challenges for teams of 1-2 engineers.
The legal asymmetry between training legality and the distribution ban of copyright-protected datasets is blocking the creation of true open source AI. Check out this analysis of challenges and solutions.
Analysis of AI deployment approaches in production shows that system durability and stability require a data-first strategy. See why data quality surpasses model size.
With AI capability doubling every six months, learning to prompt is no longer enough. Discover the 6-level AI Skills Stack and how to build lasting professional value.
Running frontier LLMs in production is expensive and slow. Learn how LoRA fine-tuning of a 2B Vision Language Model on an NVIDIA L4 GPU achieves parity with GPT-5 at a fraction of the cost.
Traditional workflow automation fails in complex, non-linear enterprise product launches. Multi-Agent Systems like IFPOF coordinate pricing, catalog, and compliance autonomously.
Is the ROI of AI agents real or just hype? A comparison of Omdia and Deloitte reports, combined with real B2B and retail case studies.
Retrieval-Augmented Generation (RAG) is transforming e-commerce, but simple linear search is no longer enough for complex expert niches. We need Agentic RAG and Multi-Agent systems.
We are observing a structural shift in enterprise software: transitioning from AI copilots to fully autonomous Multi-Agent Systems, redefining SaaS business models and data architectures.
We have officially entered a phase where artificial intelligence is no longer just a novelty tool. The transition from micro-automation to full-fledged agentic workflows brings an unexpected challenge: the Joy Paradox.
By April 2026, the talent landscape in technology and business has undergone radical transformation. It’s no longer sufficient to have "an ML Engineer" or "a Data Scientist." Modern enterprises now seek specialists with precisely defined roles that blend technical depth with business strategy.
Without hyperbole: April 2026 will be remembered as an inflection point. Not because one breakthrough model emerged, but because the entire ecosystem simultaneously shifted toward something that previously seemed impossible—autonomous, reliable agentic systems running in production.
84% of executives expect to deploy AI agents within the next 18 months, but only 23% say they know how to do it effectively. That gap matters. It suggests the main challenge is no longer access to AI tools. The real challenge is operational: how do you organize people and agents so they work together in a controlled, measurable, and useful way?
In the world of machine learning systems, a thoughtful shift is taking place. Instead of betting on gigantic, difficult-to-scale models, we increasingly opt for "agile" ones. Small Language Models (**SLMs**) promise lower costs and faster adaptation to project specifics. Sounds good, but concrete challenges stand behind success.
April 2026 brought the Zhipu GLM-5.1 model – an open-source system with 754 billion parameters that, on paper, wins against solutions such as Claude Opus 4.6 or GPT-5.4.
Between late March and early April 2026, three AI announcements appeared that are worth knowing – but before we jump into testing, we need to look at the broader context and possible challenges.