Artículos
Notas de campo sobre llevar IA dentro de sistemas reales.
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Your first AI project: pick the win you can measure
The first AI project decides whether there is a second one. Start behind the firewall, on data you already have, with a human gate — and pick something you can measure in weeks.
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Documentation that keeps itself current: the agent proposes, your team approves
Docs drift because updating them is nobody's job. Give the job to an agent that detects the drift, drafts the fix, and opens a pull request — and keep the approval where it belongs.
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Computer vision on the cameras you already have: what two proofs of concept taught us
Counting people and vehicles, tracking occupancy, checking for a hard hat — the models are ready and the cameras are already installed. The work is placement, thresholds, drift, and deciding what never gets recorded.
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Support ticket triage with AI: classify, enrich, route — and measure the reassignments
The first minute of every ticket is spent deciding what it is and who should see it. That minute is automatable — and the number that tells you if it worked isn't speed, it's how many tickets bounce.
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An internal knowledge assistant is only as good as its last wrong answer
People ask in Slack because searching is slower and less trusted. A knowledge assistant earns its place the same way a colleague does: by being right, saying "I don't know," and knowing who's allowed to see what.
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Contract intelligence: the model reads, your playbook decides what's risky
A contract review system is not a lawyer. It is a very fast reader with your playbook in hand — and the playbook, not the model, is where the project succeeds or fails.
Leer el artículoAutomating invoice processing: the hard part isn't reading the invoice
Extraction is the easy half. The system that pays off is the one that knows which invoices a person should look at — and gets that list shorter every month.
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