Most AI generated imagery looks AI generated, and the reasons are consistent enough to be listed. Once you know what the tells are, the gap between a synthetic image and a photograph becomes a solvable production problem rather than a matter of luck with prompts.
This is how photorealistic AI image production actually works at campaign standard.
1. Think like a photographer, not a prompter
The single largest quality jump comes from describing images in the language of photography rather than the language of description.
A prompt that says a woman standing in a kitchen produces a generic result because it specifies subject and nothing else. A photographic brief specifies:
- Light source, direction and quality. Window light from camera left, overcast, soft. This one variable controls more of the realism than any other.
- Lens and distance. An 85mm portrait at close range compresses features very differently from a 24mm wide shot. Real cameras have consistent optical behaviour and images that ignore it read as wrong even when viewers cannot say why.
- Depth of field. Real lenses produce a specific falloff. Uniform sharpness across an entire frame is one of the most common synthetic tells.
- Time and colour temperature. Late afternoon light is warm and directional. Midday is hard and overhead. Fluorescent interiors are green.
2. Add controlled imperfection
Generative models drift toward the idealised average of their training data. Everything is symmetrical, unblemished and perfectly composed, which is exactly what real photography is not.
Realism comes from deliberately reintroducing:
- Skin texture, pores, fine lines and subtle asymmetry
- Slightly imperfect composition, the framing a person would actually get
- Environmental clutter, since real rooms contain objects nobody arranged
- Natural fabric behaviour, creases, drape and movement
- Minor motion blur where motion exists
The uncanny valley is usually a perfection problem, not a rendering problem. Faces read as synthetic because they are too even, too symmetrical and too clean, not because the resolution is insufficient.
3. Solve consistency, because it is the actual hard problem
Generating one striking image is easy. Generating four hundred images of the same person across different settings, outfits, lighting conditions and camera angles, all recognisably the same individual, is the engineering challenge that separates a campaign from a mood board.
This requires a locked identity: a reference set, a controlled generation approach and quality control that rejects drift rather than accepting near misses. Audiences recognise faces with extraordinary precision. A character whose bone structure shifts between posts never becomes familiar, and familiarity is the entire mechanism by which influence works.
Photorealistic production at campaign volume
Proklisi produces photorealistic images, animated video, talking head clips and Reels at scale through a proprietary AI pipeline, with identity held stable across every asset.
4. Get the product exactly right
For brand work this is non negotiable and it is where a lot of AI creative fails commercially. A generative model will happily invent a logo that is nearly your logo, a bottle shape that is nearly your bottle and a label that is nearly your label. Nearly is worthless.
The production approach that works treats the product as fixed input rather than generated output: real product photography composited into the generated scene with matched lighting, perspective and shadow, or controlled generation constrained by accurate reference. Either way, the product is never left to the model's imagination.
5. Run quality control against a checklist
Most synthetic tells appear in the same places. Check every asset for:
- Hands and fingers. Still the most reliable giveaway. Count them.
- Text. Signage, labels and packaging often render as plausible nonsense.
- Reflections. Mirrors, windows and glossy surfaces should agree with the scene.
- Shadow direction. Every shadow in the frame should come from the same light source.
- Jewellery, straps and glasses. Thin objects frequently pass through skin or terminate mid air.
- Repeating patterns. Tiles, fabric prints and brickwork tend to warp or fail to repeat correctly.
- Teeth and ears. Two areas models handle inconsistently.
- Background people. Frequently deformed and frequently missed because attention sits on the subject.
6. Finish in a real editing workflow
Campaign standard imagery is generated, then graded, retouched and finished exactly as photography would be. Colour grading unifies a set so twenty images feel like one shoot. Selective retouching fixes small artefacts. Grain and subtle chromatic aberration reintroduce the optical characteristics of an actual camera.
The most photorealistic AI images are rarely single generations. They are composites, refined through several passes, treated with the same care a retoucher would give a commercial shoot. The model is a tool in the pipeline, not the pipeline itself.
The standard to hold
An image is finished when a viewer scrolling past has no reason to look twice. Not because it is spectacular, but because nothing in it signals that it was made rather than taken. That is a production discipline, and it is entirely achievable at volume once the pipeline is right.