Corporate Blog: Artificial Intelligence

Agentic development is not just about adopting AI coding tools. Learn how specifications, review, security, metrics, and delivery processes determine whether your team is ready to delegate more software work to AI agents.

See how SCAND used AI-assisted discovery to reduce business analysis and prototyping effort to 84 hours, compared with a traditional estimate of 220 hours. Explore the workflow, project results, and lessons from a real fintech SaaS project.

The article explains whether Lovable is secure, what security protections and risks businesses should be aware of, and where its managed backend and infrastructure may fall short for production use.

Technical documentation becomes harder to manage as products, versions, teams, and content volumes grow. Explore the main types of technical documentation software and tools in 2026, their strengths and limitations, and when it makes sense to move from simple editors to structured enterprise documentation systems.

This article explores TypeSafe AI Jev, a decision-focused AI model built for structured business decisions. It covers how Jev works, its use cases, cost, limitations, and how it could fit into enterprise AI workflows.

AI adoption often grows faster than oversight, leaving companies with scattered pilots, rising costs, and unclear governance. Learn how an enterprise AI platform brings data, models, agents, and security into one scalable foundation, and when to build or buy one.

Explore the top AI chatbot development companies in the USA for 2026 and find providers that deliver advanced, scalable conversational AI solutions for modern businesses.

AI governance consulting helps enterprises move beyond written frameworks into technical controls that actually enforce compliance. This piece breaks down the five core deliverables, how governance differs from ethics and compliance, and when advisory work alone isn’t enough versus full technical implementation.

Agritech is growing fast, but farm infrastructure often lags behind. This guide covers what counts as agriculture software today, why field conditions break standard assumptions, what platforms actually cost, and when custom development makes sense.

Public LLMs are convenient, but not secure. If your business works with sensitive data, it’s time to take control. We tell you how to build your LLM: from choosing a model to implementing it in products.