AI smile-makeover image generation for dental practices
- 30-40s
- AI smile transformation, photo to result
AI Image Generation Services
AI image generation, built as a production pipeline, not a demo.
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
Recent outcomes
AI smile makeover · Dental (US)
30-40s per transformation
Built an AI image pipeline that turns a patient photo into a smile-transformation preview, with four photo-capture channels.
AI before/after · Aesthetics
60 sec per preview
Shipped a photorealistic before-and-after preview generator spanning dozens of service categories.
Video generation · Marketing (US)
8,000+ video renders completed
Built a marketing video generation platform with a large template library for a US digital marketing agency.
4.9
on ClutchThe problem
Sound familiar?
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?
Short answer
RaftLabs builds AI image generation 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 starts around $15,000; a full batch pipeline launches a v1 in 10 to 14 weeks.
Key takeaways
- A first generation API integrated into an existing product starts around $15,000-$35,000.
- A batch pipeline for internal creative automation launches a v1 in 10 to 14 weeks, from $20,000-$45,000.
- Systems that need fine-tuning on brand assets or custom moderation infrastructure grow to $40,000-$80,000.
- We select DALL-E 3, Stable Diffusion, or Flux based on style requirements and infrastructure needs.
- Brand-consistent output comes from prompt engineering plus LoRA or DreamBooth fine-tuning on your approved imagery.
- Every pipeline screens input prompts, moderates output, and keeps an audit trail for policy compliance.
Trusted by
500 new SKUs, and the photography calendar was already full.
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.
AI image generation in production
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.
- 30%
- of outbound marketing messages from large organisations to be synthetically generated
- Gartner prediction (by 2025), up from under 2% in 2022
- $2.6-4.4T
- annual value generative AI could add across business functions, marketing among the largest
- McKinsey, The economic potential of generative AI, 2023
RaftLabs has shipped production software since 2015 for clients including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. 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.
A generation pipeline pays off at volume, not for one-off images.
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.
A fit
01
You produce product or marketing imagery at a volume physical shoots and design headcount can't keep up with.
02
You need brand-consistent output, not one-off generations, and have approved brand assets to fine-tune on.
03
You want generation wired into your product, PIM, CMS, or DAM, with moderation and storage handled, and budget for a build from $15,000.
Not a fit
- 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
What our AI image generation service covers
- 01Product visualisation pipelinesAutomated 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.
- 02Marketing creative automationBatch 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.
- 03User-facing generation featuresAI 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.
- 04Brand style fine-tuningTraining 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.
- 05Content moderation and safetyAny 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.
- 06Image pipeline integrationEnd-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.
Which part of your image workflow is the bottleneck?
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
From scope to shipped
Every AI image project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 101
Discover and scope
We map your use case: volume requirements, brand consistency standards, moderation needs, and integration targets. You leave week 1 with a written scope document, model recommendation, and fixed-price quote. No development starts without your sign-off.
- Weeks 2-302
Model selection and prototype
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.
- Weeks 4-1203
Build, integrate, and QA
Pipeline built end-to-end: generation, moderation, storage, CDN delivery, and CMS or DAM integration. Working pipeline at a staging URL by the end of sprint one. QA runs in parallel with every sprint.
- Weeks 12+04
Launch and post-launch support
Production deployment with monitoring activated on launch day. 8 weeks of post-launch support included in every project. Generation costs modelled at your volume and reviewed monthly.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
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.
01 / 02
What AI image generation costs
Where you land depends on scope, not negotiation:
- API integration, $15,000-$35,000
- A user-facing generation feature integrated into an existing product, with job queues, private storage, and moderation.
- Batch pipeline, $20,000-$45,000
- A batch production pipeline for internal creative automation. Launches a working v1 in 10 to 14 weeks, then expands.
- Fine-tuned system, $40,000-$80,000
- Fine-tuning on brand assets, custom moderation infrastructure, or complex style control.
What it costs
Starting at $15,000, scoped before we start.
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.
Starts at $15,000
A batch pipeline launches a working v1 in 10 to 14 weeks. 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.
Generation API costs at volume, DALL-E 3 at $0.04 to $0.12 per image or self-hosted Stable Diffusion at infrastructure cost, are modelled during scoping. Prove the pipeline on one product line before rolling it out further.
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.
Stay on topic
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Read moreFrequently 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.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope AI Image Generation Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.