Philippines Freight Forwarding Services
Air & Sea Freight Between the Philippines and the UK

Airbus A350F first flight set for 29 September

Airbus has confirmed the maiden flight of its A350 freighter prototype is due to take place on 29 September. The aircraft manufacturer announced the date of the flight today in a post on social media platform X. Airbus said the A350F take off on Tuesday would be live-streamed, although the flight would be dependent on the aircraft fulfilling all operational requirements, plus suitable weather conditions. The company had recently confirmed it was in the final stages of preparing its A350F freighter prototype for flight tests Joël Rocker, chief engineer for the A350F, said in a video that the first aircraft will undergo checks in the flight line before moving to flight testing, and the second aircraft will follow the same process. Rocker said that "the aircraft will be prepared for the flight line before being handed over to the flight test community for the flight test period". He added that the second aircraft is also now in the final assembly line cycle. Last month, Airbus unveiled the livery of its first A350 freighter on a social media post, with the aircraft decked out to look like a parcel. The airframer is working towards its goal of starting to deliver the new generation model to customers by the end of next year. The company is targeting simultaneous certification from EASA and the FAA under the latest Amendment 27 safety regulations. The airframer said earlier this year that certification is targeted by the middle of next year.

Awery adds new AI functionality for data interrogation

Awery Aviation Software has expanded its AI functionality to allow users to connect their operational data with platforms including ChatGPT, Claude, Gemini and Cursor. The new functionality is called Ask Awery and allows users of Awery's Enterprise Resource Planning (ERP) platform to ask questions about their data and receive answers directly through their chosen AI tool. "Achieving widespread digital adoption in air cargo depends on making technology straightforward and practical for the people using it every day, and this tool allows our customers that benefit," said Vitaly Smilianets, founder and chief executive of Awery. "Instead of manually extracting and processing data, our users can now prompt their chosen AI tool to compile information, such as 'tonnage by customer for August, broken down by lane' or 'charter quote conversion by customer, covering the last 90 days', using data directly from their Awery account. "Reporting tasks that previously required bringing together disparate data and could take hours can now be completed in seconds, giving our customers more time to analyse the results and use them to make better-informed decisions, save time and resources, and ultimately deliver a better service to their own customers." The air cargo software firm said the integration uses Model Context Protocol (MCP), which it described as "a standard that enables AI applications to connect with external systems and data sources". Access is read-only and governed by each user's existing Awery permissions, meaning users can only access data they are already authorised to view. "Looking ahead, we see significant potential to take this further through AI agents that can automate recurring reporting and routine data checks," added Smilianets. "Customers will be able to set agents to compile reports on a daily, weekly, or monthly basis, removing repetitive work and giving teams more time to focus on the decisions that data supports."

How a team learns to trust a quote it did not build

Four in five of the AI-built quotes on cargo.one go out without a person touching them. Every team running at that number started at zero, with a person reading every quote, trusting none of them. What happened in between was not a leap of faith, and it was not simply time. Trust is a process, and like any process in a forwarding operation, it can be designed, run, and improved systematically. We have run enough of these deployments to see the same six patterns emerge. Where all six are in place, teams move fast. Where one or more are missing, adoption stalls, however well-designed the AI workers may be. 1 - Every decision is inspectable. A quote is a chain of decisions: which rate, which charges, which margin. The AI worker shows all of it: every quote traces back to the rate it chose and the rule that chose it. The first thing a skeptic does is rebuild a few quotes by hand and compare. Let them. A skeptic who can verify becomes an advocate and someone who ends up defending it 2 - High quality & trusted inputs. The AI worker builds from the same rate sources the team already searches on cargo.one, in the system it already works in. Look behind any AI-built quote and you recognize everything in it. People extend trust to a tool that works from the same facts they do. 3 - Authority is yours. Any quote can be adjusted or overwritten on the platform, by anyone on the team (with the right permissions), before it leaves. That safety valve is what makes every next step possible: nobody hands over a workflow they cannot take back. 4 - Autonomy arrives in steps. First a person reviews everything the worker builds. Then it sends on its own inside a narrow scope - general cargo under a set weight, on named lanes - while everything unusual routes to a person. Then the scope widens as evidence accumulates. Three levels, and the team decides when to climb: * Assisted: a person reviews every quote before it leaves * Supervised: the worker sends on its own inside a scope the team has drawn; everything unusual routes to a person. * Autonomous: the worker handles the flow end-to-end; the team manages by exception. 5 - Feedback is visible. When the team corrects a quote, the correction becomes a rule, and after review, gets added to the AI worker, so the next thousand quotes follow it. This is the most underestimated of the six: people keep investing in a system they can watch getting smarter. 6 - Someone watches the full system. What really counts is the picture across thousands of quotes, including margins by lane, win rates by customer, and the outliers worth a closer look. Monitoring is set up with the team during deployment, with a path from any number down to the single quote behind it. A supervisor steers AI workers much the way they steer people: watch the numbers, sample the work, look into the outliers. Buy-in by design Every one of these six aspects leaves a decision with the team. The rates are their own, the rules are the ones they wrote, and they are involved in the pace-setting. That changes something no metric captures. Six months in, the AI worker doesn't feel like software that was rolled out to the team. It feels like something they co-built, and teams treat it that way. They argue about its rules, show it off to other branches, and notice when it gets smarter, because they made it smarter. Put simply, trust converts. Where the AI worker runs well, the mechanic is the same. Quotes go out in minutes, not hours. Volume jumps, because every request gets an answer. Faster answers win more business. The freed-up hours go where they earn most: business development and the complex shipments that need a human. More requests come in, and the AI worker quotes those too. Here is the part that matters most: every gain in that cycle depends on trust. A team that still checks every quote caps its own volume at human speed. That is why the six patterns are not the soft part of the rollout: they are what releases the return. Getting there takes two to four weeks, from kickoff to an AI worker quoting your first lanes, with a defined scope, a named team on both sides, and your number attached: the hours back, the quotes that stop going unanswered, the win rate on your lanes. It all starts with a working session on how your team prices today. Reach out to me or the team at cargo.one.