At BertIA, we’ve been working with Claude, Anthropic’s artificial intelligence model, as a real working tool rather than a passing novelty for some time now. Today that work has official recognition: BertIA is part of Anthropic’s Claude Partner Network.For you, if you’re considering bringing artificial intelligence into your company, this means working with a team […]

Any company that works seriously with data knows the problem: to cover the whole cycle (ingest, store, transform, analyze and visualize) it ends up chaining together several different tools, each with its own console, pricing model and permissions. The result is duplicated data, fragile integrations between products, and teams that spend more time connecting pieces

There are organizations that have spent years investing in data and yet can’t get their business teams to trust the reports they produce. Analysts spend more time looking for which table to use than analyzing. No one knows for sure which version of a dataset is the right one. Machine learning projects start, stall and

There’s a statistic that should come up in any conversation about enterprise AI: according to Gartner, more than half of machine learning projects never reach production. And of those that do, a significant share start to degrade in performance within the first months, with no one noticing until the damage is done.The reason, almost always,

What synthetic data is Synthetic data is artificially generated datasets that reproduce the statistical properties and patterns of real data, without containing information about specific people or entities. It isn’t randomly invented data: it’s produced from models trained on original data, with the aim of generating a set that is statistically representative and functional for

There are processes in your company that depend on someone looking. On an operator inspecting a part, a technician checking a line, someone verifying that what comes off a conveyor is what should come off it. It’s necessary work, but it has a clear limit: human attention gets tired, doesn’t scale and doesn’t generate structured

Artificial intelligence is undergoing a radical transformation. Where we’ve seen systems capable of generating content or answering questions, Agentic AI represents a qualitative leap: systems that don’t just understand instructions, they plan, decide, and act autonomously to achieve specific goals.According to Gartner, 33% of enterprise software applications will incorporate Agentic AI techniques by 2028, up