Generative AI, built into your product
and your operations. Not bolted on as a demo.

Traditional AI classifies, predicts, or detects: it tells you what category something belongs to, what’s likely to happen next, or whether something matches a pattern. Generative AI creates new content: text, code, images, structured data, summaries. The practical difference for your business is that traditional AI helps you understand and predict, while generative AI helps you produce and create. Most of what’s discussed as “AI” in 2026 is generative AI, because the recent breakthroughs (GPT, Claude, Gemini, image models) are generative.

This is the right question to ask, because the term covers a lot. If you want a conversational interface users talk to, that’s chatbot development. If you want AI that takes actions autonomously, that’s agent development. If you want AI that creates content, code, documents, or images, that’s the generation work this page covers. If you want to add AI broadly to an existing product, that’s integration. The simplest path: describe what you want the AI to produce or do, and we’ll tell you which approach fits. Often it’s a combination, and part of our job is helping you see the whole picture.

We work with OpenAI (GPT-4 and newer), Anthropic Claude, Google Gemini, and open-source models like Llama and Mistral. For image generation, we use models like Stable Diffusion and DALL-E. We choose based on three things: which model produces the best output for your specific use case, your data privacy requirements, and your cost profile at the scale you expect. We don’t default to one model. Different use cases genuinely perform better on different models, and we test rather than assume.

Through several layers. Grounding the model in your actual data and content via RAG, so it generates from your real information rather than general knowledge. Careful prompt engineering that encodes your requirements, voice, and constraints. Output validation logic that catches problems before they reach users. And human review workflows for output where approval matters before use. For brand voice specifically, we tune the prompting against examples of your actual content. No generative AI produces perfect output every time, but these techniques make the output reliable enough for production use.

RAG (Retrieval-Augmented Generation) is the technique of retrieving relevant information from your own data and including it in the model’s context before it generates output. It matters because it’s the difference between generic AI output and output grounded in your specific reality. Without RAG, generative AI produces plausible content based on general training. With RAG, it produces content based on your actual documentation, data, and context. For most business generative AI use cases, RAG is what makes the output useful.

We don’t train foundation models from scratch, and we fine-tune only when there’s a clear reason it outperforms the alternatives. For most use cases, grounding a strong base model in your data through RAG and engineering the prompting carefully produces better results than fine-tuning, at significantly lower cost and complexity. Fine-tuning makes sense in specific situations, such as when you need a very particular output format or style that’s hard to achieve through prompting. We assess your use case honestly and recommend the approach that actually fits, not the most complex one.

Yes, depending on the model choice. Open-source models (Llama, Mistral) can run in your own cloud environment or on-premise, giving you full control over where your data goes. The major API-based models (GPT, Claude, Gemini) run through their providers’ infrastructure, though enterprise versions offer data handling agreements that prevent your data being used for training. We’ll design the architecture around your data security requirements, including using open-source models in your own environment where that’s a hard requirement.

A focused generative AI feature with a clear use case and clean data typically takes four to eight weeks from scoping to production. More complex projects, multiple generation capabilities, larger data grounding requirements, or custom product builds take longer. The most common timeline factor is data readiness: if the content the AI needs to ground its output in requires cleanup or restructuring, that adds time. We assess this in scoping.

We define success metrics before building. Depending on the use case, those might be: time saved per content piece produced, document processing volume increase, reduction in time spent searching for information, feature adoption rate, or support ticket reduction. Generative AI ROI is usually clearest in use cases that automate or accelerate repetitive, high-volume work. We help you identify whether your use case has a clear ROI path before you invest, and we build in the tracking to measure it.

You do. Full source code, all prompt configurations, all pipeline logic, and all integration code belong to you from day one. The system runs on your infrastructure or your chosen cloud and model providers. There’s no proprietary platform lock-in. The prompts and the RAG pipeline are often where significant value lives, and that value is yours to keep and build on.