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AI Consulting · Atasehir

AI Consulting for Atasehir businesses

Put two jobs behind one button, let one run longer, and whoever clicks it always waits on the slow half. If you work on the tower floors around Barbaros the price of that wait is easy to read: a screen stays open and the next task stops. That is where AI consulting in Atasehir looks first. In Secretum, the product we are building now, text and image do not travel the same path: text is written inside the request while the image job goes into a queue.

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AI Consulting — Atasehir

Atasehir reads as a finance district and a place of multi-branch corporate management. If the question on your desk is when an output will arrive, the answer comes from how the production path is shaped. Secretum is the closest work we have to that, though the frame comes first: it is our own product, under construction, and we do not describe it as a finished system. Where it does not reach is written down too. It is a social feed, so head-office reporting, branch performance boards and financial institutions are all outside what we can show, and there is no finance client in the portfolio. The Anthropic API writes text, Flux API and Replicate make images, Redis carries the queue and files land on S3.

  • The Anthropic API produces the text inside the request, while image work on the Flux API and Replicate side is placed on a Redis queue.
  • A generated file is written to S3 once, so opening the feed again never regenerates the image.
  • The queue exists to keep somebody moving rather than waiting; this is not an approval workflow or a reporting tool.

Why Algoriel

  • The image does the reading

    Vipkurum reads optical form marks with OpenCV from the image of the sheet, not through the optics of a reader appliance.

  • Cropping apart from judgement

    Image processing decides where a question begins and ends; the language model only binds the answer to it. The two jobs never mix.

  • Model output lands in a record

    Vipkurum writes the answer key into the cropped question's own record instead of keeping it as a separate document.

  • A person has the last word

    At Tahlil.com the model drafts the reading and a doctor approves what reaches the patient; an unapproved reading never appears on screen.

What we build

AI Consulting

  • Where no model is needed

    Marks on an answer sheet are read from the form image itself; the threshold that decides whether a bubble counts as filled sits in the code, and no language model runs in that step.

  • Splitting the job in two

    Computer vision sets the question boundaries; a language model then ties each answer in the key to its question. Cropping accuracy never rests on a model's interpretation.

  • Removing the hardware step

    Marking stops depending on a reader device; when the form layout changes, we adjust the reading service in software instead of ordering new hardware or device-specific forms.

  • Turning existing material into a pool

    A booklet page is split into individual questions, the answer key is written onto each question's own record, and the questions already on hand become the pattern for new ones.

  • Tying the output to a person

    At Tahlil.com AI prepares the first reading, a specialist physician writes and approves the interpretation, and responsibility for the text that reaches the patient stays with the physician who signed it.

  • Queueing the generation

    In Secretum text and images come out of two separate generation paths; image generation is not instant, so that work goes onto a queue, and the finished file is stored once instead of being regenerated in the feed. Secretum is the product we are building now.

Work like this

AI Consulting · Case study

Secretum — a product under construction: short work now, long work queued

Our own product, under construction right now, with no delivered installation to show. Someone sets up an AI character and that character posts into a feed. The decision the build turns on was splitting the production paths: the Anthropic API writes the text inside the request, while Flux API and Replicate make the image and that job is placed on a Redis queue. The file is written to S3 once and never regenerated when the feed reopens. The aim is to keep nobody sitting in front of a screen while long work runs behind it. Laravel takes the records. The boundary: a consumer feed experiment, not a corporate reporting tool.

Read the case study →

Working with Atasehir teams, remotely

We have no office, so work for this city runs remotely and we do not offer on-site support. What was done, and why it was done that way, stays in writing. The project pages mark which builds came out of a partnership.

  • The first release ships as a working piece; extras follow later.
  • Every client we know of is in Turkey.
  • Decisions stay in writing; progress shows in working screens.

Frequently asked

  • Does the user have to wait for the image to be ready?

    No, the design was written around that. The text comes back inside the request, the image job joins a queue, and it lands in the feed once it is ready. The waiting moves from in front of the screen to behind it.

  • We want a panel that generates visuals for our head office. Is this that job?

    It is not. Secretum is a consumer feed and we have nothing to show for a corporate content board. What travels is narrower: do not keep long-running production inside the request.

  • Do you have a bank or financial institution as a reference?

    No. The district may read as a finance centre, but there is no finance client in our portfolio and we do not suggest otherwise. We show what we actually have.

  • Does a queue need infrastructure of its own?

    Redis carries the queue, generated files are written to S3, and Laravel takes the records. It is a work order beside the record rather than a separate surface to administer.

AI Consulting in other cities

The other cities where we publish this service are below, grouped by province.

Other services for this location

Let us look at which step needs a model and which one needs computer vision.

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