Industry-Specific Search Services That Turn Long Videos Into Next Actions

Start with the part you need, not the whole video

On September 1, 2026, Google announced that it would add Agentic Video Understanding to Gemini. The name is complex, but the approach is simple. Rather than reading a long video from beginning to end at equal intervals, it finds time ranges related to a question and examines the visual and audio signals first.

For example, when asked, “Where does the instructor explain the refund policy?”, it selects the needed signals and time range from a transcript, video frames, and audio. If it needs to find a very short action, it examines the visuals in that range more closely. It can be used with public YouTube videos or videos uploaded directly by the user.

This announcement does not mean that an entire separate subtitle file can be searched freely. Google explicitly names visuals, audio, and speech transcripts as the supported material. Whether a service can upload a subtitle file separately or connect a private video URL directly needs to be checked during product development.

In its own tests, Google said it reduced token use for long videos by up to 88%, cut analysis costs by up to 66%, and improved accuracy by up to 7%. It is the difference between reading every chunk of a text and selecting only one or two chunks. These are separate maximum figures, however, and results will vary by video and question.

Existing approaches may be better when short videos need to be processed quickly or every moment must be checked without omissions. Google also says that, for videos under five minutes, the exploration process can delay the answer. This makes long-running records that need to be searched again for a specific scene a better starting point than real-time monitoring.

One recurring question matters more than every scene

When everyone says that artificial intelligence can understand an entire long video, look in the other direction. If accurately interpreting every scene remains expensive and error-prone, a service that handles one industry’s recurring questions well may find its place before a general-purpose video assistant does. It should not stop at showing search results. It should hand the result into the next piece of work.

Consider the owner of a window-installation company with 12 employees. Workers record wall conditions before installation, the work process, and finishing results on their phones, then post the videos in a group chat. Each job may produce short videos, but footage from many sites becomes difficult to retrieve.

If a customer claims a leak three months later, the owner searches the chat for the address and plays videos one by one. A person checks with their eyes and ears whether there was a crack before installation, whether the silicone finish was completed, and whether the customer was given instructions. After finding the scene, the owner clips it, sends it to an employee, and asks again what happened.

This company does not need a huge surveillance system that evaluates every video. It needs three or four questions that recur in disputes: “a scene showing a crack before installation,” “the part where the customer confirmed completion,” or “a drainage test.” When the question types are narrow, it is also easier to find and correct wrong answers.

In a new service, selecting a site address and date gathers the relevant videos in one place. When the owner selects a question, the service shows three candidate scenes and their time ranges, along with the visuals and spoken words used as evidence. The owner approves only the right scene, then sends it as a customer explanation or an employee confirmation request.

The value appears after search. The flow should continue from attaching an approved scene to a defect claim, assigning a person and response deadline, and adding recurring issues to a training list. Companies pay not for “technology that reads video,” but for “a process that closes one dispute.”

People still have a role. A service cannot discover facts that were never filmed, and shaky footage should not be treated as conclusive evidence. Videos may contain employees’ and customers’ faces and voices, as well as the inside of homes. The purpose of filming, retention period, viewing permissions, and whether outside processing is involved should be set first.

Elsewhere, video search was tied to action rather than search alone

Mantis Solutions in the United Kingdom uses long videos from news outlets operated by Reach for advertising review. It determines whether a video is unsuitable for advertisers and sends the reviewer not only a pass-or-fail result and score, but also the problematic time range and supporting frames. It charges for advertising-review services, but does not disclose specific pricing.

The service was tested with more than 70 to 80 videos in the fourth quarter of 2025, then moved into live operation in March 2026. The claim that review time fell from hours to minutes was published by supplier TwelveLabs and needs separate verification. The important point is that it began not with open-ended video conversation, but with one decision: whether an advertisement can be placed.

CLC Utility Services, a UK water-pipe and excavation company, recorded underground cables and site conditions in video before excavation. The FYLD service checks these records for underground cables and site conditions, then connects problems to stopping work, reporting to a manager, and remote guidance. Pricing depends on crew and organisation size, while list prices are not public.

The company said damage to underground infrastructure such as power lines and water pipes fell by 85%, but the figure appears in a supplier case study and needs verification. Here, video is not only a record to search later. A retrieved risk scene moves directly into field action and supervisor review.

Global company Accenture built Video IQ to organise an internal video archive of about 1 million gigabytes. Using Microsoft video-analysis capabilities, it turns speech into text and adds speakers, topics, and time ranges so employees can find the sections they need. It is not an external product. It is an internal operating system used by an organisation of about 779,000 people.

It currently processes 200 to 300 video clips each week. Accenture says the same work would have required five or six dedicated people if done manually. This figure is also the company’s estimate in a Microsoft customer case study. The starting point was not creating more new videos, but making accumulated records usable again.

Four small products to try from here

1. Finding before-and-after installation evidence

• A service that finds before-and-after scenes related to a defect claim in site videos organised by address, then sends them into a confirmation request and customer response.
• It is for window-installation, waterproofing, and interior-work companies where multiple crews upload phone videos to group chats.
• It is a practical place to start because it can search first for sections with recurring questions, such as cracks, finishing, and operation tests, rather than analysing every long video.
• The first screen should contain an address search field and scene cards for “before installation,” “operation test,” and “completion confirmation.”

2. A meeting-commitment inbox

• A service that finds who agreed to do what, and by when, in recorded customer meetings, then turns approved items into tasks.
• It is for small architectural design offices that hold weekly video meetings with building owners but have no dedicated note-taker.
• It is now more feasible to show the time range where a commitment was made together with the original statement, rather than focusing on the sentence quality of a full meeting transcript.
• The first screen should contain a list of unconfirmed commitments, a button to play the original statement, and fields to assign an owner and deadline.

3. An answer library for skilled-work scenes

• A service that finds a relevant action scene from past work videos for a specific piece of equipment and symptom, then shows it to field employees.
• It is for small factories where skilled workers are approaching retirement and exception cases involving older equipment are hard to document.
• It can create short answer materials by selecting only work scenes related to the question, without editing every video into a training course.
• The first screen should contain fields for equipment name and symptom, three scenes approved by an experienced worker, and cautions.

4. Real-case training bundles

• A service that selects recurring mistakes and correct work scenes from field records, then distributes them as short training bundles.
• It is for training managers at food-service franchise headquarters whose store employees do not watch long headquarters training videos to the end.
• Rather than showing the same video to everyone again, it can find only the necessary scenes and send them as confirmation tasks for each store.
• The first screen should contain “this week’s recurring mistakes,” related scenes, target stores, and confirmation status.

One call to make today

Call one person in the target industry who receives the most long videos and ask: “What scene did you try to find again in an old video during the past month?” If a similar question comes up at least twice, and they either could not find the scene or had to pass it to someone else after finding it, a small service that combines search with follow-up action is worth testing.

Why this matters where you are

You can check whether people in your own market repeatedly search old videos for the same evidence, commitments, work methods, or mistakes. The relevant privacy rules, video sources, and work handoffs may differ, but the practical test is the same: identify one repeated question and the action that follows an approved scene. Start with records that already exist rather than trying to interpret every new video.