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      <video:description>The license is the smallest number on the invoice.

Cost has four parts: software, build, integration, and the upkeep that never ends. Most quotes cover the first one, which is why an agent that worked for six months can stop in the seventh and nobody writes it down. More than four in ten agent projects will be scrapped by 2027, and the reasons given are cost, unproven value and weak controls, not model capability. So ask the question that reprices the whole contract: when an integration breaks, who rebuilds the agent, and who pays for it.

From working with AI, to AI that works for you.</video:description>
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      <video:title>Why AI agents fail quietly (and the 95/5 split that stops it)</video:title>
      <video:description>Most AI agents do not fail loudly. They keep running, quietly do the wrong thing, and nobody goes looking.

Every agent Uplift builds is about 95% code and 5% AI. Code takes every step that has one right answer; AI gets only the steps that need judgment. This short explainer shows why that split matters, with one everyday job: a supplier emails to say a delivery will be late. Two teams automate it, one with rules and one with an agent. The difference is not rules versus AI. It is when the thinking happens.</video:description>
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      <video:description>An autonomous AI agent is not a switch you flip on. It is a dial, and where you set it decides whether a mistake gets caught or paid.

At one end of the dial an agent asks before every move; at the other it acts and only tells you afterwards. Same software, two different products. This short explainer runs one everyday job, matching supplier invoices to purchase orders, at the wrong setting: one invoice comes in at the wrong amount, the agent logs it and moves on, and three weeks later it has been paid. Then the same job the way Uplift sets it: by how reversible the mistake is, so the agent proposes the match and a person signs off before anything posts.</video:description>
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      <video:description>Most business processes are held together by a person copying between two systems. Here is what to build instead.

The average company runs 957 apps and only 27% of them talk to each other, so the gap is filled by a person. Take one customer email: one person reads and routes it, a second reads it again to answer, and the customer is still waiting. Uplift builds the same job as code steps that match the sender, pull the history and route the email at the same speed on email 400. AI handles only the one step with no single right answer, working out what an unclear message means. That is the 95% code, 5% AI split behind every agent Uplift builds.</video:description>
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      <video:title>AI for Accounting That Proves Who Approved It</video:title>
      <video:description>AI for accounting already works. The real test is whether it can prove who approved it six months later.

Finance teams have moved fast on AI for accounting: it reads invoices, drafts journal entries, and matches records well. But adoption has stalled, and the reason isn't the model. It's that a bought AI tool can draft and get a click approved, with nothing recording who approved it or what the AI actually saw. Six months later, when an auditor asks about that entry, someone is rebuilding the trail from screenshots and memory. This film walks through the same audit request handled two ways, and why the agent that keeps running is the one that records every approval as the work happens, not after.</video:description>
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      <video:family_friendly>yes</video:family_friendly>
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