AI Interior & Room Visualiser

Which use cases actually work

Not everything in a room is equally amenable to this treatment. Being clear about it early prevents disappointment at the demo.

Getting your products into the generated image

Making a model place a specific SKU accurately, at the right scale, in a photographed room, is the technical core of this build.

• Product asset preparation. Each item needs clean cut-outs from multiple angles, correct dimensions and material data. This is usually the largest single line of work and it sits with your team, not ours.
• Scale from the room. Detecting a doorway or a standard tile gives a reference dimension, so a three-seater does not appear the size of an armchair.
• Lighting match. The product has to be relit to match the room's light direction and colour temperature or it looks pasted on.
• Perspective and placement. Floor plane detection so items sit on the ground at a plausible angle.
• A traceable link back. Every item in the render maps to a SKU, which is what makes the image a sales tool rather than a picture.

Capture is the part users get wrong

A dark photo taken from a doorway at an angle produces a poor result, and the user will blame the app rather than the photograph.

The capture flow should guide actively: stand with your back to the wall, hold the phone level, get the floor and the ceiling line in frame, turn the lights on. On iOS we can use the LiDAR sensor on Pro devices to capture room geometry directly, which improves scale accuracy considerably where it is available.

For a retail deployment, staff-assisted capture in the showroom or at a home visit sidesteps the problem entirely and produces markedly better output than customer-taken photos.

Where this fits in the sales process

The visualiser is most valuable at the point of hesitation. A customer likes a sofa but cannot picture it at home; a family cannot agree on a wall colour. Both are stalls that a render resolves in a minute.

That means the app should exist where those conversations happen — in the showroom on a staff tablet, in the sales rep's hand at a site visit, and on the customer's own phone afterwards so they can show a spouse.

The renders should be shareable, because the actual decision often gets made in a family WhatsApp group rather than in your showroom. Every shared image should carry your branding and a route back.

Running costs and how to control them

Image generation is the expensive operation in this app, at roughly ₹2 to ₹8 per render depending on resolution and complexity. A curious user generating forty variations is a real cost.

The controls are practical: cache aggressively, since the same room with the same product is the same image; generate previews at lower resolution and only produce a full-quality render when the user saves or shares; and meter free generations while giving customers who have started an enquiry a higher allowance.

For a retail app where every render supports a possible sale, the economics are comfortable. For a free consumer app with no catalogue attached, they are not — which is the commercial argument for the catalogue version stated a second way.

What we hand across

The iOS app, generation backend on your cloud, catalogue integration with your product data, the asset pipeline for adding new SKUs, an enquiry or cart handoff, analytics on what customers visualise most, and full source.

The asset pipeline matters most in the long run. New products arrive every season, and if adding them requires us, the app decays. We build it so your merchandising team can prepare and publish a new SKU themselves.