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AI Consulting · Umraniye

AI Consulting for Umraniye businesses

Photographing a product waits for the bench to clear. If the line stops on shooting day in a small workshop around Madenler, the part is cleaned and the lights go up, which is why a catalogue image costs far more than the paragraph beside it. When AI comes up the two get put in one box, though their price is nothing alike. In Secretum, the product we are building now, text and image travel separate production paths and the image job is queued.

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AI Consulting — Umraniye

Umraniye leans on the mechanical contracting, air conditioning service and small manufacturing around Dudullu. If you have a product worth describing but no separate budget for photography, the AI question is not which model to choose but which job belongs in a queue. The work closest to this district's sector profile is Secretum, our own product, still in development. We are building it for a social feed. We have no work that plans production from factory data or interprets a technical drawing. What carries across is the split in the production path. Anthropic API writes the text, Flux API and Replicate make the image, the image job enters a Redis queue, and the file is written to S3.

  • Text generation and image generation run on separate paths, so the heavy job never holds up the light one behind a single queue.
  • A finished image is written as a file and read from there; the feed does not ask the model again on every view of the same picture.
  • What we hold is not a finished install: we are building Secretum now, we quote no delivery date, and we promise nothing about reading manufacturing imagery.

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 — text and image take separate paths, the image job waits in a queue

Secretum is our own product and we are building it now. There is no finished install to show and no delivery date to quote. The construction is this: a person defines an AI character, and the character posts into a feed. Anthropic API writes the text while Flux API and Replicate produce the image, and the two paths run apart. The image job goes into a Redis queue, the finished file is written to S3, and the feed never regenerates it. The aim is to let a person keep moving through the feed while an image is still being prepared. The context is a social feed, not a manufacturing catalogue or a technical drawing.

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Working with Umraniye 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

  • Could AI produce our catalogue imagery?

    We have not done that on client work, so we do not promise it. Image generation is something we are building inside our own product, and what we can explain is the mechanism: the image job runs on its own path and is queued.

  • Why keep image generation apart from text?

    They do not cost the same time or money. Share a queue and the short job waits behind the long one. On separate paths the text returns at once and the image attaches to its record when it is ready.

  • Is a generated image recomputed every time the page opens?

    No. It is produced once, written as a file, and read from there. Asking the model again on every view would add waiting and a call that buys nothing.

  • Would you come out to the Dudullu side and look at the process in person?

    We keep no office and work fully remotely. Instead of a site visit we decide together which job has to return immediately and which belongs in a queue. Skip that decision and the waiting spreads everywhere.

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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