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HeyFriday

A photo databank with AI features. From the first wireframe to the running application.

HeyFriday screens stacked on top of each other: the gallery with a file list, the dashboard with a photo overview and a photo detail
Industry
Photo management and AI
Scope
Design, application, website, operations
Timeline
2025 to present
Stack
Node.js, Nuxt, LLM, MCP

The brief

Company photos sit on hard drives, in email and on shared storage. Who took them, who is in them and whether they can be used at all is usually known by one person. And that person happens to be away from their desk.

HeyFriday is the answer to that problem. A databank that describes a photo on its own, tags it and finds it when you ask in an ordinary sentence. It keeps a licence with every shot: the medium, the territory and the expiry date, so it is clear what can be published and until when.

We built the web application for gen live production s.r.o. We refined the brief together with the client from the very first sketches and handled the entire delivery: application design, development and ongoing operation.

The whole application in two minutes

A quick walkthrough of the whole application: the dashboard, the gallery, import with a duplicate check, the map and sharing. Narrated in Czech.
A short look at the live application: the gallery, AI descriptions, face recognition and a licence on every photo.

Everything gets drawn first

We draw the whole application in Figma as a prototype. The client walks through it screen by screen, writes comments straight into the design, and we work them in before the first line of code is written.

Wireframe of the photo gallery with folders, filters and context menus
1 Gallery wireframe: folders, filters, bulk selection and the AI button. Penpot, sprint 3.
The gallery in the running HeyFriday application as a grid: folders, photo thumbnails each with a date and size, one of them tagged Licence expired
2 The same gallery, live. Grid, list and mosaic views, with the size and date on every item.
The prototype canvas in Figma with dozens of application screens, connections between them and the client’s comments
3 The whole prototype runs to dozens of screens. But we don’t go into excessive detail.
Diagram of the AI features in Figma: a photo’s path from reading its metadata through description and embedding to tags and face recognition
4 The AI features themselves need careful planning too.

The design in numbers

246
wireframes
6
sprints
48k
lines of code
1k
OpenAI requests

The programming itself took just under three months. Along the way there were several meetings, and we set up development, testing and production environments as well as an automated CI/CD build pipeline.

Artificial intelligence in the lead role

OpenAI models work on the photos. They analyse what is in the shot, write a description for it, suggest tags and convert the content into a vector database, which can then be searched by meaning, not just by matching words.

The AI recognises what is in a photo, identifies people and lets you ask naturally, for example “a smoking chimney with the sky in the background”.

If face recognition is what interests you, you can try it right here on our website: the AI page runs a live demo directly in your browser, and neither the camera feed nor an uploaded photo is sent anywhere.

Our AI assistant Marie

Marie optimises an uploaded photo, describes it, suggests tags and finds the people in it, with their emotions and estimated age. She rewrites the description on request and finds photos in a conversation too. The commentary is in Czech.

How much glass is too much glass?

The application’s panels are made of glass. The translucent layer blurs whatever is behind it and takes its colour from there, so the interface holds together even over a photo that would normally cause visual chaos. But the glass can be switched off, and the application has to hold up without it too: the panel gets a solid fill, and instead of a dead grey it takes a tint of the primary colour, so it keeps the tone it had through the glass.

The same photo detail with glass switched off: the panels are solid and tinted in the warm tone of the primary colour
A photo detail in HeyFriday with glass switched on: the panels are translucent and the gallery content behind them shows through
Glass on the left, off on the right. Drag the boundary between them to compare both versions in the same spot.

Redesign Q4 2026

At the end of 2026 the application went through a major redesign. A year in production brought a lot of new features, and this big update reworked how things look, from the navigation to the individual screens, so they read as one whole again.

The HeyFriday gallery in map view: photos grouped by where they were taken around the Temelín power plant, each group showing its number of shots
A better map view came with it. Photos are grouped by where they were taken, each group shows how many shots it holds, and it is clear at a glance how many photos have no coordinates.

In the client’s words

Jan Šebek

What I value most is how independent and flexible the guys at 200solutions are. Not everything worked the first time, but being able to meet at 2 a.m. and show the client the fixes at 9 a.m. is what counts. Thanks a lot for that.

Jan Šebek
founder, producer @ gen live production, s. r. o.

What else the system does

Going through every feature and clever detail of the system would fill a whole book. Here is at least a quick list of the most interesting ones.

  • Search in your own words, not keywords

    Type “photos from last summer where the cooling tower is visible” and the system sets all the filters itself: time period, content, orientation. You can see those settings and fine-tune them by hand, so you are never left guessing why you got these particular photos.

  • The system knows what is in a photo

    It looks at every uploaded photo on its own, describes it and tags it. So you can find “a toast” even in a shot called DSC_4471.jpg that nobody ever described by hand. This is exactly where archives usually break down: the photos are there, but nobody can find them.

  • Find photos of a specific person

    The system recognises faces and can answer a query like “all photos with our CEO in them”. Handy when you need a portrait for a press release and don’t want to click through three years of events.

  • Licences are attached to the photo, not kept in a spreadsheet next to it

    Every photo states where it may be used (print, web, or both), in which territory and until when. Once the licence expires, the preview is degraded and the photo cannot be downloaded, which protects against someone accidentally publishing a shot you no longer have the rights to. Licences can be renewed in bulk.

  • Sharing outside and inside, always with an end date

    You share a folder or an album either with a link for anyone (the recipient does not need an account) or with specific people in your organisation. For both, you set the date when access ends. The system also warns you when someone downloads a photo whose licence is limited to a different territory.

  • Everything runs on the company’s own servers

    Neither the photos nor their analysis ever leave infrastructure the company controls. Plus one less visible but important thing: the system is not tied to a single AI provider. That is insurance against one provider raising prices or changing its terms.

Company photos finally have a single home. And they find themselves in it.