How you work with Innguma, told on video.

Monday, 8:15. The committee meets at 12 and a market report on energy storage for data centres is needed.

A professional gets the request by phone on her way to the office. That used to mean days searching a thousand websites, never sure if something important had been missed. But her team has been following that market for months with Innguma: every news item, every tender and every competitor move, saved in the team's library. From her corporate AI, connected to those sources, she asks what they have on the topic and gets 23 signals, each with its date and source. Then she asks for the committee report, with every conclusion linked to the news item that backs it. By 10:40 it is ready, with time to review it calmly.

What used to take two days now takes a morning, and every figure can be backed up. A good market report doesn't start from scratch: it starts with constant monitoring that turns sources into market intelligence. Illustrative story, no company names.

The committee is clear: AI-driven cyberattacks have to be seen coming. A security lead sets up that monitoring in ten minutes, just by talking to his corporate AI.

From his phone he asks Innguma for sources and searches to monitor AI cyberattacks. The AI adds 64 sources, including official advisories, vulnerabilities, patents and technical publications, organises the filters by folder and sets up the alerts: a daily summary by email and critical ones, instantly, in the Security channel on Teams. Three days later a critical alert arrives. AI agents are exploiting a vulnerability in corporate VPN gateways, the very one his company uses. He asks for a response protocol and the AI prepares it with 34 news items from his library and 5 new ones from that week.

The alert arrived before the problem because they weren't starting from scratch, but from weeks of threat monitoring. AI has changed the way attacks are made, and the way to get ahead of them. Illustrative story, no company names.

Tomorrow there is a management committee and they want to know how the competition is moving in electric vehicles. This afternoon, the strategy director is flying out.

She leads strategy at an automotive components supplier that manufactures for several European carmakers. Preparing something like this used to take her several days. Before boarding, she asks her corporate AI by voice: competitor moves, key technologies and patents from the last six months, using what they already have in Innguma. She receives a PDF report built from 52 signals in their library, each with its source. She reads it on the flight and marks what doesn't add up. The next morning she asks for a dashboard with three panels and presents to the committee: competitor C has announced a new plant, B has hired a sales director from the industry and A has filed six patents in AI-based predictive maintenance. Her proposal is to accelerate their own pilot.

Competitive intelligence isn't about arriving with every answer, but with verified data, its sources and a concrete proposal. Illustrative story, no company names.

An industrial machinery manufacturer protects its know-how as a trade secret, with no patents. One day, its own customer posts a video of the machine at work.

The video showed the parameter screen, the mechanism and a detail of the process. It was online for 1 hour 26 minutes and no one saw it. The manufacturer used Innguma to monitor what was published about the company and its machines, including on video platforms. The alert arrived 47 minutes later, they spoke to the customer and the video was taken down before any competitor could see it.

Technology watch is not only for looking outwards. It also tells you what is being published about you, so you can protect your intellectual property in time.

Years of design, prototypes and testing. An international trade fair. And another company's patent that nobody had checked.

In 2024, a machinery manufacturer unveils its new machine at the industry's trade fair. On the spot, the holder of a patent recognises its own invention. A patent infringement lawsuit follows, the stand is shut down and the manufacturer faces a claim worth millions. The patent had been published long before.

With patent monitoring from the start of the project, the alert would have arrived in time to redesign that part or negotiate a licence, before investing in prototypes and the trade fair. Monitoring patents is not only about protecting your own: it is also about not building on other people's.

An official gazette announces that your critical supplier has filed for insolvency proceedings at 7:30 a.m. When do you find out?

This capital goods manufacturer relied on a single supplier for the gearbox in its machines. As well as its own industry, it monitored its key suppliers in the press and in official gazettes. The alert reached Purchasing at 8:12 a.m. That same morning they secured three months of stock and started qualifying an alternative supplier. Six weeks later the supplier stopped delivering, and their production carried on. Other customers had lines down for weeks.

Supplier monitoring is also supply chain risk management. An illustrative story, with no company names.

At a trade fair, a sales rep hears that a competitor is already making parts 5% lighter. He thinks nothing of it. Six months later, the same news reaches Operations.

The operations director does a different calculation: 5 million parts a year, 500 tonnes less material and more than €2 million in annual savings. They launch a lightweighting project, but six months late: over €1 million that can no longer be recovered.

In a shared monitoring environment, that signal would have been captured and classified the same day and would have reached Operations, R&D and Sales that week. In competitive intelligence, the value of a piece of news depends on who reads it. An illustrative story, with no company names.

An EU restriction on a chemical substance does not come as a surprise. It is announced in a public consultation, sometimes years before it becomes mandatory.

A metal components manufacturer uses a coating containing a substance the EU proposes to restrict. The day the consultation opens, the alert reaches Quality and R&D. With two years ahead of them, they reformulate the coating, validate it with their customers and get it certified. When the rule enters into force, they already comply. A competitor has to stop a line.

Regulatory monitoring turns a regulatory change into time to adapt. An illustrative story, with no company names.

In April 2025, workshops that make goods for luxury brands posted on social media what their products cost to make. Within days, those brands' reputation was at stake.

One video showed a bag that sells for more than $30,000 and a production cost of around $1,400, according to the video itself. It was shared millions of times, the comments were divided and hundreds of people asked how to buy directly from the manufacturer. The brands responded with statements about their workshops in Europe, by which time the conversation had already moved on.

With social media monitoring, a brand would have seen mentions and negative sentiment grow from the very first video, and could have responded that same day with facts: origin, workshops, craftsmanship and traceability. Based on a public event, with no brand names.

An EU food safety alert on sesame is published at 7:45 a.m. By 9:00 a.m., this bread manufacturer had already put its batch on hold.

It used sesame in half of its products and had just taken delivery of 20 tonnes from its usual supplier. It monitored EU food safety alerts, filtered by its ingredients, origins and suppliers. The alert, for residues of an unauthorised pesticide, matched the origin of its batch: it reached Quality at 8:20 a.m. and the batch never went on the line. On Friday, the lab confirmed it. Other manufacturers had to recall products from supermarkets.

Food safety is also monitored outside the factory: keeping watch on food alerts in time prevents product recalls and protects the brand. An illustrative story, with no company names, inspired by the EU sesame alerts of 2020.

A €1.8 million public tender with a fourteen-day bid deadline. This medical supplies distributor saw it thirty minutes after publication and had time to win it.

It sells medical supplies to hospitals in several regions and monitors public procurement platforms, filtered by its sector, products and regions. On a Thursday at 9:10 a.m., a regional health service publishes a two-year tender for wound care supplies. At 9:40 a.m. the alert reaches Sales. That week they prepare the documentation and samples, on day thirteen they submit the bid, and in July they are awarded the contract.

Hearing about it in the trade press a week later would have left seven days for a bid that needed fourteen. In public procurement, monitoring tenders from the day they are published is what gives you time to compete. An illustrative story, with no company names.

A few scientific papers, some job openings and a public grant. On their own they were not news; together, these weak signals gave a dairy company an 18-month head start on a competitor.

A dairy company monitors its technology landscape across scientific publications, company websites and official gazettes. In March 2023, papers on precision fermentation, dairy proteins made without cows, are on the rise; in September, a startup is hiring to scale up its production; in February 2024, a public grant funds its pilot plant. The monitoring team files all three under the same topic, and in April management sets up its own pilot line. When the first rival product reaches supermarket shelves in October 2025, its line has been running for a year and a half.

Weak signal detection is not about finding one big story, but about connecting small pieces of data in time: that is how technology watch becomes competitive intelligence, and time to decide. An illustrative story, with no company names.