Generate content,
not compromise.

Generative AI can produce a wide range of content including marketing copy, product descriptions, technical documentation, blog articles, social media posts, email campaigns, reports, and more. For visual content, it can generate images, graphics, and design assets. It can also generate code, create synthetic data for testing, and automate document assembly. The specific capabilities depend on the models and training we implement for your use case.

Yes. Unlike generic AI tools, we fine-tune models on your specific brand guidelines, terminology, tone of voice, and content standards. This training process uses your existing approved content as examples, teaching the AI to generate outputs that align with your established style. The result is content that's consistent with your brand identity without requiring extensive manual editing.

We work with leading AI platforms including OpenAI (GPT-4), Anthropic (Claude), Meta (LLaMA), Mistral, and others. For image generation, we use Stable Diffusion, SDXL, and Flux. We select the most appropriate model for your specific use case, considering factors like output quality, cost, latency, and deployment requirements. We can also combine multiple models for different tasks within your workflow.

Quality assurance is built into our process. We implement validation layers, content filters, and review workflows appropriate to your use case. For critical content, we can add human-in-the-loop review stages. We also establish feedback mechanisms so the system improves over time based on real-world usage and your team's input.

With on-premise deployment, the AI models run entirely on your own infrastructure—whether that's your data centre, private cloud, or secure environment. Your data never leaves your systems, providing complete sovereignty over your content and outputs. We handle the installation, configuration, and optimisation of models on your hardware, and provide documentation for your team to manage ongoing operations.

Implementation timelines vary based on project complexity, integration requirements, and your team's availability. Simple integrations with existing AI APIs can be deployed quickly, while custom-trained models with on-premise deployment require more time for development and testing. During our discovery phase, we'll provide a realistic timeline based on your specific requirements. Contact us to discuss your project scope.

We design solutions with data privacy as a priority. On-premise deployments ensure your data never leaves your infrastructure. For cloud deployments, we work with providers that offer enterprise security and compliance certifications. We can implement solutions that meet GDPR, industry-specific regulations, and your internal data governance policies. We also ensure AI models don't train on your proprietary data without explicit consent.

We offer ongoing support including model performance monitoring, output quality reviews, and continuous refinement based on feedback. As your needs evolve, we can retrain models, adjust parameters, and expand capabilities. Support arrangements are tailored to your requirements—from self-managed solutions with documentation to fully managed services with dedicated account management.