Artículos, guías y las últimas novedades del equipo de DABO.
Get ChatGPT-style answers, document Q&A and file generation without sending company data to a public cloud — and without per-token fees.
Turn PDFs, Word files, spreadsheets and scanned paper into an assistant that answers in seconds, cites the page it used, and never uploads anything.
Public AI tools are useful, but pasting contracts, code or customer data into them creates real risk. Here is how to keep the productivity and lose the exposure.
Tokens, context windows and agents make cloud AI bills hard to predict. Learn how the pricing works and when a fixed-cost Local AI makes more sense.
Data-protection laws make cloud AI tricky for personal data. Running the AI on your own servers removes the hardest questions about transfers and processors.
AI agents can now use the apps on an employee's computer. Here is what they are good at, where the risks are, and how DABO AI Desktop keeps a human in control.
You do not need a data centre to run private AI. Here is what a single workstation can do, how VRAM limits model size, and when to scale up.
Live translated subtitles and reply suggestions in every participant's language — processed on your own server instead of a cloud service.
Let AI look things up, summarise, build the file and email the result on a schedule — with templates for common jobs and nothing leaving your network.
Analyse stocks, indices, FX, gold, oil and crypto with indicators and professional charts — numbers computed from real price data, interpreted by AI, kept in-house.
Keeping the AI server inside the building doesn't automatically make the data safe. Encryption and role-based access control are two protections that still matter, even when everything already sits within the company's own walls.
"Data never leaves the business" is only half the story. If the AI system shares a network with every employee device without proper isolation, one infected laptop is enough to reach the AI's data.
Choosing the right physical server platform matters just as much as choosing the right AI model. A look at what four major server makers offer for enterprise Local AI in 2026.
The bigger the AI model and the more concurrent users, the sooner a single GPU runs out of memory or processing speed. Combining multiple GPUs the right way is the key to scaling Local AI without rebuilding from scratch.
No matter how powerful the GPU is, it means little if the storage layer behind it isn't fast or safe enough. SAN, NAS, and DAS are three common storage architectures — each suited to a different stage of a Local AI rollout.
A single AI server is a single point of failure — if it goes down, your AI assistant goes down with it. Replication and a standby backup server are how you keep the system running even when hardware fails.
LLM is the language brain behind every AI assistant — but running it efficiently is the real challenge. vLLM is the technology that solves it, letting a single GPU serve more users at a lower cost.
On-premise AI runs directly on a business's own infrastructure instead of sending data to an outside server — suited for businesses with sensitive data, a need for predictable long-term cost, or regulatory requirements on where data is stored.
More than a chat box — DABO's internal AI assistant understands context, cites its sources, and shows up wherever your team already works.
Three waves are reshaping enterprise AI — and all of them lean toward self-reliance.
The heart of a Local AI system is the GPU server — the right configuration decides both performance and cost.
The question today is no longer "should we use AI", but "where will your data live when you use it".
Turn thousands of scattered documents into an expert that answers instantly, with citations.
A solid Local AI system is more than a model — it is a well-architected infrastructure layer.
AI running right next to your data — private, fast, and independent of the Internet.
Medical records are the most sensitive data in any industry — and doctors still spend too much time on paperwork instead of patients.
Designs, source code and R&D data are assets that must never leak — even while using AI to speed up development.
Proprietary formulas, clinical trial data, and strict GxP compliance — three requirements no public AI can meet.
From Europe to Asia, the Middle East and Latin America, a new generation of personal data protection laws is tightening the rules on "cross-border data transfer" considerably — and this is exactly where Local AI shows its clearest advantage.
SCADA/OT data must stay fully isolated from the Internet, while reactive maintenance still causes costly disruption.
Manufacturing know-how must be protected, unplanned downtime is costly, and OT systems are inherently disconnected from the Internet.
Backlogged claims, extremely sensitive health data, and insurance fraud that's hard to spot in a huge volume of records.
You do not need an expensive closed model — open models are now strong enough to self-host.
Tens of thousands of daily support calls, massive network data, and strict localization requirements — telecom's unique challenge.
The sheer volume of records forces auditors to sample — AI changes that equation entirely.
In law, the accuracy of a citation determines professional reputation — RAG over real documents solves exactly this problem.
Overloaded lecturers, students lacking after-hours support, and student records needing absolute protection — Local AI solves all three at once.
RAG combines document retrieval with answer generation, so AI responds accurately and verifiably.
Manually optimized routes, inaccurate demand forecasts, and slow paperwork — a familiar challenge for every logistics company.
Thousands of daily questions, inaccurate inventory forecasts, and generic product suggestions — three familiar bottlenecks in retail.
Massive project records, confidential bidding data, and a decision delayed by even a few days is enough to blow a project's budget.
A knowledge and automation platform running fully inside enterprise infrastructure, launching in 2026.
Every minute of delayed response is a lead lost to a competitor — while transaction data still needs absolute confidentiality.
Foreign-invested enterprises must balance the host country's data-localization law with parent-company policy — Local AI makes that possible without trade-offs.
SMBs don't need an in-house AI team to compete — just the right tool, the right cost, and data that never leaves the business.
Cloud is cheap to start but costly to scale; on-premise costs more upfront but cheaper as usage grows.