AI Image Generation Software
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
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?
Plain answer
RaftLabs builds AI image generation software, pipelines for product visualisation, marketing creative, and content at scale, for clients across the US, UK, Europe, Canada, and the UAE. We select DALL-E 3, Stable Diffusion, or Flux based on brand-consistency needs. A first integration covers model selection on your real brand assets and a working pipeline. Every project starts at $10,000; scope and price are fixed before the phase begins.
What to remember
An e-commerce team ships hundreds of products a quarter. Every one needs lifestyle imagery, colour variants, and marketing creative. The photo studio books weeks out, the design queue is backed up, and stock libraries never quite match the brand.
So launches slip, or the images ship generic. Neither is the outcome anyone wanted.
A generation system changes the shape of the problem. One approved hero image multiplies into variants and scenes on demand, on brand, the moment a product lands in the catalogue.
The model is the easy part. The pipeline around it is the product.
A demo that generates a few striking images is not a pipeline. Production means brand-consistent, policy-compliant output at volume. The generation call is one small part. The real work is prompt engineering, fine-tuning, moderation, durable storage, and the integration back into your systems.
Synthetic imagery has already moved into the marketing mainstream, and the shift is measured, not anecdotal. For product and marketing teams, generated imagery is turning from an experiment into infrastructure.
RaftLabs has shipped production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE. One team scopes the pipeline, builds it, wires it into your stack, and hands it over. You get the whole system, not a wrapper around an API call.
Everything on the left should already be true for your operation. Even one thing on the right, and a managed tool or a design contractor is the smarter first step.
You produce product or marketing imagery at a volume physical shoots and design headcount can't keep up with.
You need brand-consistent output, not one-off generations, and have approved brand assets to fine-tune on.
You want generation wired into your product, PIM, CMS, or DAM, with moderation and storage handled, and budget for a build from $10,000.
You need a handful of one-off images a managed tool like Midjourney or DALL-E already covers.
Hero shots that require perfect accuracy, or a flagship brand campaign where creative quality is critical.
No existing product or content system to integrate against, and no volume to justify a pipeline.
What we build
Automated product imagery for e-commerce, fashion, and consumer goods brands that need contextual photography faster than physical shoot cycles allow. One approved hero image multiplies into colour variants and lifestyle scenes per batch job, triggered when a new product lands in your PIM. ControlNet conditioning holds the product's real shape and placement steady across every scene, so a generated bottle or handbag keeps its true silhouette instead of drifting. Built on DALL-E 3, Stable Diffusion XL, Flux, and Replicate, wired into Shopify.
Batch generation of ad creative, social visuals, email headers, and blog illustrations at the volume content teams need without scaling design headcount. A LoRA checkpoint fine-tuned on your approved assets keeps output on-brand, and generated images auto-tag into your DAM and publish to your CMS via API.
AI image generation embedded as a feature inside your product: design tools, personalisation flows, content assistants, and avatar generation. The surrounding infrastructure makes it production-safe. Job queues (BullMQ or Celery on Redis) keep slow generations from blocking sessions. Pre-signed S3 URLs hold private storage. Per-user attribution drives history and metered billing, and moderation runs before anything reaches users.
Training Stable Diffusion XL or Flux models on your approved brand assets with LoRA or DreamBooth so the model generates in your visual language by default, without lengthy style descriptors on every prompt. Your brand team rates validation output before deployment, and you receive the adapter plus a prompt reference guide.
Any product with end-user access needs moderation at two independent layers, input and output, or it becomes a liability. Input prompts pass the OpenAI Moderation API before you spend GPU time. Generated output clears an NSFW classifier before delivery. Medium-confidence flags route to a human review queue. Every decision lands in an audit trail, the record regulators and platform policies expect.
End-to-end image pipeline connecting generation to your storage, delivery, and content systems, because a raw image URL is not a production asset until it is stored durably and discoverable. Images upload to S3 or Cloudinary with structured metadata and serve via CDN, and completion webhooks notify your CMS or DAM so approved images appear in the content team's media browser without manual uploads.
Walk us through the volume and the brand standards. We'll tell you which models fit and what a pipeline costs to build.
How it works
Every AI image project follows the same four phases. Scope is locked and price is fixed before development starts.
We map your use case: volume requirements, brand consistency standards, moderation needs, and integration targets. You leave the first phase with a written scope document, model recommendation, and fixed-price quote. No development starts without your sign-off.
We run test generations across candidate models using your actual brand assets. You see real output before any pipeline is built. Fine-tuning decisions are made here, not after the build is in progress.
Pipeline built end-to-end: generation, moderation, storage, CDN delivery, and CMS or DAM integration. You see a working pipeline at a staging URL early in the build. QA runs in parallel throughout.
Production deployment with monitoring activated on launch day. Post-launch support is included in every project. Generation costs are modelled at your volume and reviewed monthly.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
"I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support."
Amer Abu Khajil
Founder, Peak Studios & Perceptional
"All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!"
Charles E.
Entrepreneur at Aggie Technologies
Proof
AI integration in the remote patient monitoring app lifts efficiency by automating data analysis and providing personalized insights through wearable health monitoring devices such as CGM and BPM. This advancement has cut clinical decision-making time by 20%, enabling virtual care management, particularly in chronic care scenarios.
What it costs
Model selection and fine-tuning decisions made on your real brand assets first, then a production system with moderation, storage, and integration built end to end.
Prove the pipeline on one product line before rolling it out further.
Starting investment
Starts at $10,000
A batch pipeline launches a working v1 once scope is agreed. We model generation costs at your volume during scoping, so the first campaign can start small and the rest of the catalog follows once it works.
No hourly billing
Once we scope your first phase, that number is locked in writing. No hourly billing, no surprise invoices, no unapproved change fees.
See it first
Test generations run across candidate models on your actual brand assets, so you see real output before any pipeline is built and before you commit to the full scope.
Work with us
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
It depends on your constraints. DALL-E 3 (OpenAI) offers strong prompt adherence and built-in moderation via API, best for general-purpose generation. Stable Diffusion is open-source and self-hostable with LoRA and DreamBooth fine-tuning, best when you need full control and brand-specific style training. Flux (Black Forest Labs) offers high quality with open weights; Midjourney leads on aesthetic quality for creative imagery; Ideogram is strong at text-in-image. We recommend based on your style requirements, fine-tuning needs, volume, and whether self-hosting or a managed API 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, so treat this as a risk area to manage rather than a settled answer. Practical steps: read each provider's current terms of service before deploying commercial work, prefer commercially-licensed API services for business-critical applications, and disclose AI generation where platform policy requires it. Have your legal advisers review training-data provenance for self-trained models, and design the workflow with human creative control from the start.
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.
Generate in batches and lay all outputs on one canvas. Drift is invisible one image at a time and obvious in a grid, so audit the series and regenerate outliers. For brand work, ask how consistency is enforced: it should come from locked master reference images and fine-tuned style models, not prose prompts. Keep the model away from your logo; the vector logo gets composited over a generated plate.
Cost follows scope: the use case, volume, fine-tuning needs, and moderation requirements. Every engagement is scoped and quoted in writing before development starts, so you see the price before you commit. We model expected generation API costs at your volume as part of scoping.