Generative AI Development

It depends on 4 factors: (1) Quality: Flux.1 (2025) = best quality (beats Midjourney v6), DALL-E 3 = premium (expensive), Stable Diffusion XL = very good (customizable). (2) Cost: DALL-E $0.04-$0.12/image (expensive at scale). Midjourney $60/month (900 images = $0.07/image). Flux self-hosted $0.003/image (20x cheaper). Stable Diffusion self-hosted $0.001/image (70x cheaper). (3) Customization: Stable Diffusion/Flux = LoRA fine-tuning on YOUR brand (best for consistency). DALL-E/Midjourney = generic outputs (everyone uses same models). (4) Volume: <1K images/month → DALL-E/Midjourney OK. >10K images/month → self-hosted Flux/Stable Diffusion (70-90% savings). We analyze YOUR requirements and recommend the optimal model (often hybrid: Flux for quality, Stable Diffusion for volume).

SaaS APIs (pay-per-use): DALL-E 3: $0.04-$0.12/image. Midjourney: $60/month (900 images) or $120/month (unlimited slow). GPT-4: $0.01-$0.03/1K tokens. At 10K images + 100M tokens/month: $700 (images) + $2,000 (text) = $2,700/month = $32K/year. Self-Hosted (fixed cost): Setup: $15K-$45K (our packages). GPU: RTX 4090 ($2K buy) or A100 cloud ($1-3/hour = $720-$2,160/month). Hosting: ~$1K-$3K/month. At same usage (10K images + 100M tokens): Setup $45K + hosting $2K/month = $69K first year, $24K/year after. Break-even: 18-24 months. 3-year savings: $96K (SaaS) vs $93K (self-hosted) = similar cost BUT unlimited usage, IP ownership, fine-tuning. At scale (100K images/month): SaaS $300K/year vs self-hosted $30K/year = 90% savings. Recommendation: <10K images/month → SaaS APIs. >10K images/month → self-hosted (massive savings).

YES via LoRA fine-tuning! How it works: (1) Provide 50-200 brand images (logos, product photos, marketing materials, color palettes). (2) We fine-tune Stable Diffusion or Flux on YOUR images (~4-8 hours training on A100 GPU). (3) AI learns YOUR visual style (colors, composition, lighting, brand elements). (4) Generate 1,000s of images in YOUR brand style (95% consistent vs 50% with generic AI). Example: E-commerce brand fine-tunes on product photography → AI generates new products on same backgrounds, lighting, model poses (brand consistency). Marketing agency fine-tunes on client campaigns → AI generates ads matching client's visual identity. Text: Fine-tune Llama 4 on YOUR copywriting (website, emails, ads) → AI writes in YOUR voice/tone. Cost: LoRA training included in all packages. Retraining: $2K-$5K per update (quarterly recommended as brand evolves).

Image Generation (Stable Diffusion XL, Flux): Consumer GPU: NVIDIA RTX 4090 (24GB VRAM) = $2,000, generates 1 image in 5-10 seconds (good for <1K images/day). Professional GPU: NVIDIA A100 (40GB/80GB) = $10K-$15K or cloud $1-3/hour, generates 1 image in 2-3 seconds (production scale 10K+ images/day). Text Generation (Llama 4, DeepSeek-R1): 7B-13B models: RTX 4090 (24GB) = $2K, handles 10M-50M tokens/month. 70B models: 4× A100 (40GB) = $40K or cloud $3/hour, handles 100M-1B tokens/month. Code Generation (Qwen3-Coder): 7B-32B models: RTX 4090 = $2K (sufficient for most teams). Cloud Options: AWS/Azure/GCP: $1-3/hour for A100 (flexible, pay-as-you-go). Reserved instances: 50% discount (1-year commit). On-Premise vs Cloud: On-premise: $10K-$120K upfront (GPUs), break-even at 12-24 months, then unlimited free usage. Cloud: $1K-$10K/month ongoing (flexible, no upfront cost). We recommend: Start cloud (fast deployment), migrate to on-premise at scale (cost optimization). We handle all infrastructure setup (cloud or on-premise).

Self-hosted models = uncensored (YOU control content policies). Content blocked by OpenAI/Midjourney but allowed with self-hosted AI: Fashion/swimwear (flagged "inappropriate" by Midjourney). Medical/anatomy (educational content rejected by DALL-E). Alcohol/tobacco marketing (banned by OpenAI policies). Fantasy/gaming (weapons, violence censored by Midjourney). Horror/thriller content (blocked by content filters). Mature/edgy creative (arbitrary censorship by Big Tech). With self-hosted Stable Diffusion/Flux: YOU decide what's appropriate (based on YOUR brand, legal requirements, not Big Tech's arbitrary rules). We implement YOUR content policies (compliance filters for illegal content, brand guidelines, but no arbitrary censorship). Use case: Alcohol brand → generate whiskey ads (blocked by OpenAI, works with self-hosted). Medical startup → anatomy illustrations (rejected by DALL-E, works with self-hosted). Fashion brand → swimwear photography (flagged by Midjourney, works with self-hosted). Legal: We ensure compliance with laws (no illegal content), but YOU control creative decisions (not external platforms). Ethical use: We're model-agnostic (provide uncensored tools), YOU responsible for ethical usage (brand guidelines, legal, compliance).

Timeline varies by package (6-26 weeks). Typical process (Production tier, 10-12 weeks): Week 1-2: Requirements gathering (use cases, volume, quality benchmarks). Model selection (Flux vs Stable Diffusion vs hybrid). Infrastructure design (cloud vs on-premise, GPU sizing). Week 3-4: Data collection (50-200 brand images for fine-tuning, 1K-5K copywriting samples). Fine-tuning training (LoRA, 8-16 hours GPU time). Model evaluation (quality benchmarks, human review). Week 5-6: Infrastructure setup (AWS/Azure GPU nodes, auto-scaling, load balancing). API development (FastAPI, batching, streaming, webhooks). Workflow automation (ComfyUI pipelines for images, vLLM for text). Week 7-8: Integration with tools (Shopify, Figma, CMS, custom APIs). Quality assurance (A/B testing, edge cases, load testing). Security hardening (authentication, rate limiting, encryption). Week 9-10: Team training (2-day hands-on workshop: model usage, fine-tuning, troubleshooting). Documentation (API reference, runbooks, best practices). Gradual rollout (10% traffic → 50% → 100% over 2 weeks). Week 11-12: Monitoring & optimization (dashboards, alerts, cost tracking). Post-deployment support (90 days: answer questions, fix issues, monthly check-ins). Continuous improvement (monthly model retraining on new data). Fast-track option: 6-8 weeks (Starter package, single model, basic fine-tuning, no custom integrations). Enterprise: 14-26 weeks (multi-modal, proprietary training, compliance, white-label). Process highlights: (1) Incremental delivery (working model by Week 4, not big-bang at end). (2) Weekly syncs (Fridays: demo progress, get feedback, adjust). (3) Hands-on training (YOUR team can maintain/improve models after handoff).

We offer multiple support models (included + optional add-ons): Included Support (all packages): Starter ($15K): 30 days post-deployment (email/Slack, business hours, 24-hour response SLA). Production ($45K): 90 days support + 2-day training + monthly model retraining (first 3 months). Enterprise ($95K): 120 days support + weekly check-ins + quarterly optimization reviews. Transformation ($185K): 180 days support + dedicated Slack channel + SLA guarantees. Optional Extended Support (after included period): Retainer Support: $3K-$10K/month (10-40 hours/month, rollover unused). Use cases: Monthly model retraining (LoRA fine-tuning on new data), infrastructure scaling (new GPU nodes, auto-scaling tuning), new use cases (integrate new tools, workflows). On-Call Support: $5K-$15K/month (24/7 coverage, 1-hour response SLA for critical issues). Managed AI Services: $15K-$50K/month (we run your AI infrastructure, you focus on product). Includes: Model retraining, infrastructure monitoring, scaling, incident response, quarterly R&D (new models, optimization). Ad-Hoc Support: $250/hour (no commitment, pay-as-you-go for one-off help). Most Common Path: We build AI platform ($45K-$95K, 10-18 weeks) → 90-120 days included support (handoff training) → you maintain in-house with junior AI engineer ($80K-$120K/year) → we provide retainer ($3K-$10K/month, 10-40 hours) for model retraining, optimization, advanced issues. This hybrid model: Expert infrastructure build + affordable maintenance + available for complex R&D. Real Example: E-commerce client hired us for $45K Production package → 90 days support (trained their team on LoRA fine-tuning) → $5K/month retainer (monthly model retraining on new products, quarterly optimization) → cost-effective vs hiring senior AI engineer full-time ($180K/year).

YES, multilingual support depends on model choice: Image Generation: Stable Diffusion/Flux = language-agnostic (prompts in English, but generates ANY visual content regardless of language/culture). Fine-tuning: Train on non-English brand assets (Arabic logos, Japanese product photos) = AI learns visual style (language irrelevant). Text Generation - English-focused: GPT-4, Claude = excellent English, OK other languages (50-80% quality vs English). DeepSeek-R1 = strong English + Chinese, basic others. Text Generation - Multilingual: Llama 4 = trained on 20+ languages (Spanish, French, German, Portuguese, Italian, Dutch, Polish, Arabic, Hindi, Bengali decent quality 70-90% vs English). Qwen3 = trained on 29 languages (Chinese, Japanese, Korean, Thai, Vietnamese, Arabic excellent 90-95%, all 22 Indian languages 80-90%). Best multilingual for India: Qwen3 fine-tuned on Hindi, Bengali, Odia, Tamil, Telugu, Marathi = 95% native-speaker quality. Voice/Audio: ElevenLabs = 29 languages (multilingual TTS, voice cloning works cross-language). Whisper = 99 languages (speech-to-text, translation). Fine-tuning for languages: Text: Collect 1K-10K examples in target language → fine-tune Qwen3/Llama 4 → native-quality outputs. Voice: Provide 30-60 minutes audio in target language → clone voice (ElevenLabs or XTTS) → AI speaks that language. Use case: Indian e-commerce → Qwen3 fine-tuned on Hindi product descriptions → AI generates Hindi copy (saves ₹10L/year on translators). Global SaaS → Llama 4 fine-tuned on 5 languages (English, Spanish, French, German, Portuguese) → AI responds in user's language. Multilingual included in all packages (no extra cost for language support). We recommend models based on YOUR language requirements (not one-size-fits-all).