Spending budget on stock photography or custom shoots for content that could be generated?
Need product visualisations or marketing creative at a volume that design resources can't support?
AI Image Generation
Generative image AI has moved from novelty to production infrastructure. Product photography, marketing creative, design asset generation, and content illustration can now be produced at scale with the right model and integration.
We integrate AI image generation into your products and workflows -- selecting the right model, building the generation pipeline, implementing safety controls, and connecting output to your existing design and content systems.
DALL-E 3, Stable Diffusion, Flux, Midjourney API, and Ideogram depending on your use case
Prompt engineering and fine-tuning for brand-consistent output
Batch generation pipelines for high-volume creative production
Safety filtering, moderation, and content policy compliance
RaftLabs integrates AI image generation (DALL-E 3, Stable Diffusion, Flux, Midjourney API) into products and workflows for product visualisation, marketing creative, design asset generation, and content illustration. We handle model selection, prompt engineering, fine-tuning on brand assets for consistent output, batch generation pipelines, safety filtering and content moderation, and integration with design tools and CMS platforms. We build both user-facing generation features and internal production automation pipelines.
AI image generation in production
A demo that generates interesting images is not the same as a production pipeline that generates brand-consistent, policy-compliant output at volume. The generation step is one part of the system -- prompt engineering, fine-tuning, moderation, storage, and integration are the other parts.
We build the full pipeline, not just the API call.
What we build
Product visualisation pipelines
Automated product imagery at scale: generate lifestyle contexts for product photos, create colour and variant visualisations without physical samples, produce scene variations from a single product image. Useful for e-commerce catalogues with many SKUs, fashion and furniture retailers needing contextual imagery, and brands launching product variations faster than photography cycles allow. Integration with your product information management system to automate generation when new products are added.
Marketing creative automation
Batch generation of marketing creative: ad creatives in multiple formats and ratios, social media visuals for campaigns, email header imagery, and blog post illustrations. Brand-consistent output from a trained style system rather than generic generation. Reduces creative production time and cost for high-volume content teams. Integration with your CMS, DAM (digital asset management), or creative workflow tools.
User-facing generation features
AI image generation embedded in your product: design tools that generate background elements or assets, personalisation features that create custom imagery for users, content creation assistants with image generation, and avatar or profile image generation. Generation integrated into your UI with appropriate safety controls, usage limits, and rate limiting. Works with your authentication system to attribute generated images to users.
Brand style fine-tuning
Training Stable Diffusion or Flux models on your brand's visual assets to produce output that matches your established visual language -- illustration style, photography aesthetic, colour palette, and composition preferences. LoRA fine-tuning for efficient style training without full model retraining. The result: a generation model that produces your brand's style without detailed style prompting on every call. Best for teams with an established visual identity that needs to be maintained at scale.
Content moderation and safety
Content moderation infrastructure for production image generation: input prompt classification to block prohibited content before generation, output screening before image delivery, confidence-scored moderation with configurable thresholds, human review queues for edge cases, and audit logging for compliance. Built for your specific content policy requirements and risk tolerance. Includes moderation model selection and threshold calibration based on your use case.
Image pipeline integration
End-to-end image pipeline connecting generation to your existing systems: generation API integration, storage in S3 or Cloudinary with optimised delivery, CDN configuration for fast image loading, metadata tagging for asset management, integration with your CMS or DAM, and webhook notifications when generation completes for async workflows. The infrastructure that makes AI-generated images a production asset, not an experiment.
Image production at scale?
Tell us the use case, volume, and brand consistency requirements. We'll recommend the right generation model and build the pipeline.
Related services
AI Video Generation -- video generation for marketing and product content
Custom AI Development -- end-to-end AI product development
Generative AI Consulting -- model selection and AI architecture strategy
Generative AI Integration -- broader generative AI integration beyond images
AI Orchestration -- orchestration for multi-step AI production pipelines
Frequently asked questions
DALL-E 3 (OpenAI): strong prompt adherence, text rendering in images, API with usage-based pricing, content moderation built in. Best for general-purpose generation via API. Stable Diffusion (open-source): self-hostable, highly customisable, supports fine-tuning (LoRA, DreamBooth) for brand-specific styles. Best when you need full control and custom style training. Flux (Black Forest Labs): high quality, strong prompt following, open weights. Midjourney API: highest aesthetic quality for creative and editorial imagery, limited API access. Ideogram: strong text-in-image capability. We recommend based on your style requirements, fine-tuning needs, volume, and whether self-hosting or managed API better fits your infrastructure.
Style consistency requires either: (1) prompt engineering with detailed style modifiers that encode your brand's visual language -- colours, lighting, composition, reference aesthetics -- applied to every generation call; (2) fine-tuning on your existing brand imagery using LoRA or DreamBooth (for Stable Diffusion / Flux) to train the model on your specific visual style; or (3) both together for maximum consistency. We build a style system for your use case -- not generic prompts that produce inconsistent output.
The legal landscape for AI-generated images is still developing. Current practical considerations: images generated by commercial APIs (DALL-E 3, Midjourney) are generally usable for commercial purposes under each provider's terms of service -- read the current terms before deployment. Training data provenance is the primary legal risk for self-trained models (Adobe Firefly uses licensed training data as a risk-mitigated alternative). We recommend using commercially-licensed API services for business-critical applications, disclosing AI generation where required by platform policy, and monitoring evolving regulations in your jurisdiction.
Production image generation requires content moderation at multiple layers: input prompt screening to block attempts to generate prohibited content, output screening to catch policy violations before images are delivered, human review queues for edge cases flagged by automated moderation, and audit logging for moderation decisions. Most commercial APIs (DALL-E 3) include built-in moderation. Self-hosted models require building moderation infrastructure. We design the content moderation architecture for your specific use case and risk tolerance.
For some use cases, yes. AI generation is cost-effective for: product mockups showing items in lifestyle contexts, colour and variant visualisation without physical samples, marketing creative for social and ad creative, background replacement for existing product photos, and scale photography for categories with many SKUs. AI generation is not yet reliable for: hero product shots requiring perfect accuracy, brand campaigns where high creative quality is critical, complex scenes with many elements, and any content requiring legally defensible authenticity. We scope which parts of your photography workflow AI generation can replace now.
Integrating a generation API into an existing product (user-facing generation feature) typically runs $15,000--$35,000. A batch production pipeline for internal creative automation runs $20,000--$45,000. Systems requiring fine-tuning on brand assets, custom moderation infrastructure, or complex style control run $40,000--$80,000. Generation API costs at volume: DALL-E 3 at $0.04--$0.12 per image, Stable Diffusion self-hosted at infrastructure cost. We model expected generation costs at your volume as part of scoping.