1Solutions AI Practice

Generative AI Services - Build with Generative AI

From AI content pipelines to image generation workflows, RAG applications, and multi-modal AI products — we design, build, and deploy generative AI solutions that create real business value.

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GenAI Projects Delivered

120+

AI Models Worked With

20+

Years Tech Experience

15+

Client Retention

97%

Trusted by Leading Brands

What We Do

Generative AI Services We Offer

Expert generative ai services for businesses in the US, Canada, and Australia — from strategy through implementation and ongoing optimization.

AI Content Generation Systems

Design and build scalable AI content pipelines — blog posts, product descriptions, email campaigns, social content, and SEO articles generated at scale with human editorial oversight and brand voice consistency.

AI Image & Creative Generation

Midjourney, DALL-E, Stable Diffusion, and Flux integration for automated product imagery, creative assets, social graphics, and design variations — reducing creative production costs while increasing output volume.

RAG Application Development

Retrieval-augmented generation systems that connect LLMs to your proprietary data — customer databases, product knowledge bases, documentation, and internal systems — for accurate, grounded AI responses.

Custom LLM Fine-Tuning

Fine-tune open-source language models (Llama, Mistral, Falcon) on your specific domain data — creating specialised AI models that understand your industry terminology, brand voice, and use-case nuances.

AI-Powered Customer Experiences

Generative AI for customer-facing applications — personalised product recommendations, dynamic content generation, AI-generated email personalisation, and intelligent FAQ systems that adapt to individual users.

Multimodal AI Applications

Build applications that process and generate text, images, audio, and video together — product analysis from uploaded images, video transcription and summarisation, and voice interface AI that goes beyond text-only interactions.

Got Questions?

Generative AI Services — Frequently Asked Questions

Generative AI Services refer to the design, development, and deployment of applications and workflows powered by generative artificial intelligence models — AI that creates new content (text, images, audio, video, code) rather than only classifying or analysing existing content. Examples include: AI writing tools that generate blog posts and product descriptions; image generation systems that create product photography or design variations; chatbots that generate personalised responses rather than selecting from pre-written answers; and AI applications that summarise, translate, or transform existing content. Generative AI Services cover both the strategic design of what to build and the technical implementation of building it.

We build with all major generative AI models: OpenAI GPT-4o and GPT-4.1 for text generation; DALL-E 3 and GPT-4V for image generation and vision; Anthropic Claude Sonnet and Opus for long-context text generation; Google Gemini Pro and Ultra for multimodal applications; Meta Llama 3 and 3.1 for on-premise or custom-deployment text generation; Stability AI Stable Diffusion and SDXL for image generation; Midjourney via API for high-quality creative imagery; ElevenLabs and Udio for audio and music generation; and RunwayML, Kling, and Sora (when available) for video generation.

RAG (Retrieval-Augmented Generation) is an AI architecture where an LLM is connected to an external knowledge base — your documents, database, product catalogue, or internal wiki — that it retrieves relevant information from before generating a response. This solves the key limitation of base LLMs: they only know what they were trained on. With RAG, an AI assistant can answer questions about your specific products, policies, or internal data accurately, rather than hallucinating or giving generic answers. You need RAG if you want AI that knows your business specifically — customer-facing chatbots, internal knowledge assistants, support agents, and product advisors all benefit from RAG architecture.

We build quality controls into every AI content system: human editorial review workflows for high-stakes content (medical, legal, financial, brand-critical); fact-checking layers that verify generated content against source material; brand voice guidelines embedded in system prompts; toxicity and bias filtering; output validation against required format and length specifications; and A/B testing frameworks that measure generated content performance against human-written benchmarks. AI content without quality controls produces volume without value — our systems are designed to produce both.

eCommerce (product description generation, AI product photography, personalised recommendations), media and publishing (AI-assisted content creation at scale, content repurposing), marketing agencies (campaign asset generation, copy variation, creative production), SaaS companies (in-product AI features, customer onboarding automation), legal and financial services (document drafting, contract analysis, report generation), healthcare (medical content generation, patient communication), and education (personalised learning content, AI tutoring tools). Virtually every industry has high-value generative AI use cases — the opportunity varies by content volume and content value, not by sector.

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