Generative AI Development Services
Production grade generative AI. LLM applications, RAG pipelines, fine tuned models, diffusion systems, and multimodal content engines. Projects start at $5,000.
- PHOTOFOXAI: DIFFUSION BASED VISUAL AI
- SKETCHTOIMAGE: CONTROLNET IMAGE GENERATION
- QUIZMAKERAI: KNOWLEDGE GENERATION
Generative AI development is the engineering of systems that create new content (text, images, audio, video, and code) using large language models, diffusion architectures, and retrieval augmented generation pipelines. Riverborn is a generative AI development company that builds these systems using GPT-5.6, Claude Sonnet 5, Claude Fable 5, Claude Opus, Llama 4, SDXL and Flux and runs the same stack across 5+ live generative AI products. Those include PhotoFoxAI (AI photography via diffusion), SketchToImage (ControlNet image generation), AiStoryGen (multimodal content), Vocalo.ai (voice generation), and QuizMakerAI (knowledge generation). 100K+ users, in production today.
Riverborn delivers generative AI development services and generative AI services to CTOs, VPs of Engineering, and Heads of AI/ML at Series A-C teams and global enterprises.
What Your Team Gets
on GPT-5.6, Claude Sonnet 5, Claude Fable 5, Claude Opus, and Llama 4 with function calling and structured output. So your AI features run on your data, not generic model knowledge.
grounded in your knowledge base via Qdrant, Pinecone, Weaviate, or pgvector with hybrid search and reranking. So your team queries documents instead of searching folders.
via LoRA, PEFT, and 4-bit quantization on your domain datasets. So your model behaves the way your business requires, not the way the base model defaults.
on SDXL, ControlNet, and Flux with custom LoRA training. So your visual content follows your brand guidelines, not generic aesthetic defaults.
hallucination detection, PII masking, cost optimization, and compliance monitoring on every production release.
text + image + audio + video from unified pipelines. Projects start at $5,000.
What We Build: Generative AI System Types
Six generative AI architectures, matched to the shape of your generation requirement. Riverborn delivers custom generative AI solutions across each type.
Generative AI development sits within applied artificial intelligence engineering focused on content and data creation. Generative AI system types include LLM application platforms, RAG powered knowledge systems, fine tuned domain models, diffusion image generators, and multimodal content engines. A generative AI system consists of a model inference engine, a prompt management layer, a retrieval pipeline, a guardrail module, and an output delivery API.
LLM Application Platforms
Custom applications on GPT-5.6, Claude Sonnet 5, Claude Fable 5, Claude Opus, or Llama 4. Prompt engineering, function calling, structured output parsing, and streaming responses. Authentication, rate limiting, and per request cost monitoring built in as product requirements. Your AI features run on your data and access controls, not generic model knowledge.
RAG-Powered Knowledge Systems
Retrieval-augmented generation over your enterprise knowledge base. Embedding pipeline plus vector store (Qdrant, Pinecone, Weaviate, pgvector) plus hybrid search plus reranking plus LLM generation with citation grounding. So your knowledge base answers queries instead of staying locked in PDFs.
→ See also: NLP & RAG Development for Enterprise Knowledge SystemsFine-Tuned Domain Models
LoRA and PEFT fine tuning on your domain specific datasets. 4-bit quantization for deployment efficiency. RLHF for behavioral alignment to your business requirements. Evaluation on task specific benchmarks, not generic leaderboards.
Diffusion Model Systems
Image and video generation using SDXL, ControlNet conditioning (depth, edge, pose, canny), and Flux. Custom LoRA training on brand specific datasets produces consistent visual output. PhotoFoxAI and SketchToImage are production proof.
→ See also: Computer Vision & Vision AI DevelopmentAudio and Voice Generation
Text to speech via ElevenLabs and OpenAI TTS with SSML control for pacing and emphasis. Voice cloning and multi language synthesis cover 35+ languages. Vocalo.ai's production architecture validates this at 100K+ live voice interactions.
Multimodal Content Engines
Unified pipelines producing coordinated text, image, audio, and video from a single content brief. AiStoryGen is production proof.
→ See also: AI Content & Video Production ServiceOur Technical Approach
According to McKinsey's State of AI 2024 report, 65% of organizations use generative AI in at least one business function, up from 33% the prior year. Gartner projects that by 2026, more than 80% of enterprises will have deployed generative AI applications in production environments.
The production failure mode is not adoption. It is AI deployed without retrieval grounding, guardrails, or evaluation. Every Riverborn generative AI engagement runs a documented technical methodology. Each stage produces system components, documentation, and handover assets. Your engineering team owns the system after implementation.
Generative AI Technical Methodology
1. Model benchmarking.
GPT-5.6 (128K context, highest reasoning, OpenAI dependency), Claude Sonnet 5 (1M context, production and document analysis, lower API cost), Claude Opus 5 (1M context, complex reasoning and agents, high API cost), Claude Fable 5 (1M context, frontier-tier capability, highest API cost), Llama 4 (128K context, self hostable, fine-tunable, lowest cost), SDXL (1024×1024, ControlNet and LoRA support), Flux (higher fidelity than SDXL for photorealistic output, Black Forest Labs). Every model is benchmarked against your latency, cost, accuracy, and context window requirements.
2. RAG pipeline.
Embedding via OpenAI text embedding 3 large, Cohere embed-v3, or BGE. Vector storage in Qdrant, Pinecone (managed, sub-10ms p99), Weaviate (self hosted, multi tenant), or pgvector (PostgreSQL native). Hybrid search runs BM25 and semantic retrieval in parallel via Reciprocal Rank Fusion. Reranking via Cohere Rerank or BGE reranker. Grounded generation with citation attribution. RAGAS scores faithfulness, answer relevance, and context precision on every release.
3. Fine tuning pipeline.
LoRA and PEFT keep trainable parameters under 1% of the base model, cutting GPU cost 60 to 80% vs full fine tuning. QLoRA adds 4-bit quantization for smaller inference footprint. RLHF aligns outputs to your behavioral constraints.
4. Diffusion engineering.
ControlNet conditioning injects layout constraints (depth maps, edge maps, pose keypoints) that text prompting cannot achieve. Custom LoRA on your brand dataset adapts the base model to your visual style without full retraining.
5. Guardrails.
Input validation blocks prompt injection. Output validation runs hallucination detection (faithfulness scoring), LlamaGuard content filtering, and PII redaction before anything reaches your users.
Model Comparison
We select the optimal foundation model based on your accuracy, context window, latency, and deployment cost targets.
| Model | Best For | Trade-offs |
|---|---|---|
GPT-5.6 | Complex reasoning, function calling, structured output. 128K context. | Highest cost. Closed source. OpenAI dependency. |
Claude Sonnet 5 | Production default: long-document RAG, analytical writing, tool use. 1M context. | Lower API cost. Anthropic API dependency. |
Claude Opus 5 | Hardest reasoning, multi-step agents, and heavy tool orchestration. 1M context. | High API cost. Anthropic API dependency. |
Claude Fable 5 | Frontier-tier generation and reasoning when quality dominates cost. 1M context. | Highest API cost. Anthropic API dependency. |
Llama 4 (Meta) | Cost efficient batch inference. Self hostable. Fine tunable. 128K context. | Requires GPU infra. Community support only. |
SDXL | 1024×1024 image generation with ControlNet and LoRA fine tuning. | Slower inference than Flux. Requires GPU infra. |
Flux | Higher fidelity than SDXL for photorealistic output. Faster inference. | Newer ecosystem. Fewer ControlNet adapters. |
GPT-5.6
Best For
Complex reasoning, function calling, structured output. 128K context.
Trade-offs
Highest cost. Closed source. OpenAI dependency.
Claude Sonnet 5
Best For
Production default: long-document RAG, analytical writing, tool use. 1M context.
Trade-offs
Lower API cost. Anthropic API dependency.
Claude Opus 5
Best For
Hardest reasoning, multi-step agents, and heavy tool orchestration. 1M context.
Trade-offs
High API cost. Anthropic API dependency.
Claude Fable 5
Best For
Frontier-tier generation and reasoning when quality dominates cost. 1M context.
Trade-offs
Highest API cost. Anthropic API dependency.
Llama 4 (Meta)
Best For
Cost efficient batch inference. Self hostable. Fine tunable. 128K context.
Trade-offs
Requires GPU infra. Community support only.
SDXL
Best For
1024×1024 image generation with ControlNet and LoRA fine tuning.
Trade-offs
Slower inference than Flux. Requires GPU infra.
Flux
Best For
Higher fidelity than SDXL for photorealistic output. Faster inference.
Trade-offs
Newer ecosystem. Fewer ControlNet adapters.
Industries & Use Cases
Riverborn ships enterprise generative AI across four active deployment patterns.
Enterprise Knowledge
50K+ internal documents with no searchable knowledge base. RAG pipeline ingests, retrieves, and answers with citation grounding. So your team queries a system that cites sources, not a shared drive.
→ Generative AI for SaaS & TechnologyE-commerce Visual Content
10K+ on brand product images per season without photoshoots. Diffusion pipeline with brand LoRA generates catalogue ready images from product specs. PhotoFoxAI is production proof.
→ Generative AI for E-commerce & RetailHealthcare Documentation
5K+ clinical summaries monthly consuming clinician time. Fine tuned LLM generates structured summaries with HIPAA aligned guardrails. So your clinicians dictate and review, not draft.
→ Generative AI for HealthcareFinancial Reports
Quarterly analyst reports built from structured data. RAG pipeline retrieves, generates narrative, and compliance checks the output. Analysts review and finalize the output instead of building it from scratch.
→ Generative AI for Financial ServicesHIPAA/PCI-DSS/SOX framing describes architectural alignment, not corporate certification. Riverborn is not corporately certified for these standards. Your legal counsel remains the system of record for regulatory interpretation.
Technology Stack
Every engagement ships on the production validated stack below. Riverborn also delivers dedicated generative AI integration for embedding a GenAI system into your existing infrastructure without rebuilding the surrounding stack.
| Category | Technologies & Frameworks |
|---|---|
LLM Providers | GPT-5.6Claude Sonnet 5Claude Fable 5Claude OpusLlama 4 (Meta)Mistral LargeGemini Pro (Google) |
Image / Video Generation | Stable Diffusion XLControlNetFluxRunwayMLCustom LoRA training |
Audio / Voice | ElevenLabsOpenAI TTSBarkWhisper (transcription) |
RAG Infrastructure | QdrantPineconeWeaviatepgvectorChromaDB |
Fine-Tuning | Hugging Face PEFTLoRAQLoRA4-bit quantizationRLHF |
Frameworks | Google ADKOpenAI APILangChainCrewAIAutoGenLlamaIndexHugging FaceCustom retrieval pipelines |
Cloud & Infra | AWSGCP Vertex AIAzure MLKubernetes GPU nodesGCP Compute Engine |
Guardrails | Hallucination detectionPII maskingCost monitoringLlamaGuard |
Evaluation | RAGAS (faithfulness, relevance, context precision)FID scoreCLIP score |
LLM Providers
Image / Video Generation
Audio / Voice
RAG Infrastructure
Fine-Tuning
Frameworks
Cloud & Infra
Guardrails
Evaluation
How It Works: Our Development Process
Riverborn delivers enterprise generative AI development through a five step process. Standard engagements complete in 7 to 12 weeks. Projects start at $5,000 for a focused single modality build. Riverborn also runs generative AI consulting engagements for teams evaluating fit before committing to a build.
Use Case Discovery & Model Selection
We map your business objectives, identify your generation types (text, image, audio, video, or multimodal), and benchmark GPT-5.6, Claude, Llama, SDXL, and Flux against your latency, cost, and accuracy requirements.
Deliverable
Data & Pipeline Architecture
We map your data sources, design the RAG pipeline or fine tuning dataset, and specify guardrail requirements. Full technical architecture is documented before engineering begins.
Deliverable
Model Engineering & Build
LLM application development, RAG pipeline build, fine tuning, and diffusion model configuration. Prompt engineering validated against your domain before staging. Weekly build demos keep your team aligned.
Deliverable
Guardrails, Testing & Evaluation
RAGAS scoring for RAG systems. FID and CLIP scoring for image generation. PII and compliance validation. Adversarial edge case testing on every release.
Deliverable
Deployment & Monitoring
Production deployment on AWS SageMaker, GCP Vertex AI, or Kubernetes GPU nodes. Per request cost monitoring. Latency SLA tracking. Model drift detection.
Deliverable
Why Riverborn
Grand View Research projects the global generative AI market at $1.81 trillion by 2030. The build versus resell distinction matters at that scale. Riverborn builds and runs the same generative AI architecture on its own products that it ships to clients.
BUILDER, NOT RESELLER
Production proof across five GenAI products.
PhotoFoxAI (diffusion), SketchToImage (ControlNet), AiStoryGen (multimodal), Vocalo.ai (voice, 100K+ users), and QuizMakerAI (knowledge generation) are all live. No generative AI development company in the direct competitor set ships own GenAI products. Riverborn has five.
MULTIMODAL DEPTH
Multimodal generation in one service.
Text, image, audio, and video generation across a single engineering team. Most generative AI services cover one or two modalities. So your multimodal pipeline ships without stitching specialist vendors together.
DIFFUSION MODEL DEPTH
Diffusion model depth with named architectures.
Riverborn names SDXL, ControlNet, Flux, and custom LoRA training because these are architectures Riverborn's engineers have debugged in production, not reviewed in papers. PhotoFoxAI and SketchToImage validate this in production, with real users.
COST STRUCTURE
A cost structure that sustains the rigor.
Riverborn's Bangladesh delivery model gives you a 40 to 60% cost reduction vs US and EU agencies at identical production benchmarks. That includes RAGAS evaluation, FID/CLIP scoring, RLHF alignment, and enterprise guardrails. Projects start at $5,000.
4.8+ avg.
product rating
5+ live
GenAI products shipped
100K+
global users served
PhotoFoxAI + SketchToImage
diffusion & ControlNet in production
Vocalo.ai
voice generation across 35+ languages
RAGAS + FID/CLIP
eval on every release
Related Services
NLP & RAG Development for Enterprise Knowledge Systems
For deep RAG engineering (hybrid search, reranking, evaluation frameworks) as a standalone build independent of a generation application.
Learn more →AI Content and Video Production Service
For automated content pipelines combining text, image, audio, and video.
Learn more →AI Consulting and Strategy Service
If you are confirming whether generative AI fits your use case before committing to a build.
Learn more →Frequently Asked Questions
Generative AI development is the engineering of systems that create new content (text, images, audio, video, and code) using large language models, diffusion architectures, and retrieval-augmented generation pipelines. Riverborn builds production-grade generative AI with enterprise guardrails, RAGAS evaluation, and full handover documentation on every engagement.
GPT-5.6 (OpenAI), Claude Sonnet 5 (Anthropic), Claude Fable 5 (Anthropic), Claude Opus (Anthropic), Llama 4 (Meta), Mistral Large, and Gemini Pro (Google). Model selection is task-specific, benchmarked against your latency, cost, accuracy, and context window requirements rather than vendor preference.
Retrieval-Augmented Generation grounds LLM outputs in your verified enterprise data rather than pretrained model knowledge. It reduces hallucination and enables domain-specific accuracy without full model retraining. Riverborn builds RAG pipelines with hybrid search (BM25 + semantic), Cohere reranking, and RAGAS evaluation on Qdrant, Pinecone, Weaviate, or pgvector.
Yes. Riverborn uses LoRA, PEFT, and QLoRA for parameter-efficient fine-tuning on domain-specific datasets. 4-bit quantization reduces inference cost at deployment. RLHF aligns the fine-tuned model's behavior to your business constraints and output format requirements.
Standard engagements run 7 to 12 weeks through a five-step process: Discovery and Model Selection, Data and Pipeline Architecture, Model Engineering and Build, Guardrails and Evaluation, and Deployment and Monitoring. Accelerated timelines are available for single-modality builds.
Healthcare (clinical documentation, medical knowledge retrieval), financial services (report generation, compliance documentation), e-commerce (product content generation, visual asset production), education (course content, assessment generation), and SaaS (in-product AI features, knowledge assistants).
Yes. Riverborn builds diffusion model systems using SDXL, ControlNet, and Flux. PhotoFoxAI (diffusion-based AI photography) and SketchToImage (ControlNet image generation) are production proof. Custom LoRA training adapts the base model to your brand's visual style without full retraining.
Projects start at $5,000 for a focused single-modality build. Final cost depends on generation type, fine-tuning requirements, integration count, and guardrail scope. Riverborn's Bangladesh delivery model provides 40 to 60% cost advantage vs US and EU agencies at identical production-grade benchmarks.