Software that's
intelligent.

A focused integration — a support chatbot, document Q&A or a recommendation feature — typically takes 4–8 weeks to build. Ongoing cost is mostly model usage, which we optimise deliberately: caching, smaller models for simple steps and prompt efficiency often cut running costs by an order of magnitude.

We work with the current frontier models from Anthropic, OpenAI and Google, plus open-source options like Llama and Mistral where they fit. We're model-agnostic by design and build so that swapping providers later is straightforward rather than a rewrite.

Not always. For LLM-based features like chatbots or document Q&A, you can use pre-trained models with your own documents — no training required. Custom prediction models (churn, demand forecasting) do need historical data, typically at least six months of it.

RAG (retrieval-augmented generation) gives an existing model access to your knowledge base at question time — no training, and easy to update whenever your content changes. Fine-tuning adjusts the model itself for a specific task or style. We use RAG for most knowledge use cases and fine-tune when accuracy or latency demands it — see our RAG chatbot for AVYANTA for a live example.

Every AI feature we build includes evaluation suites, output validation, guardrails and fallback logic for when the model isn't confident. We test adversarially before launch and monitor outputs in production, so regressions get caught before your users find them.

Yes — that's the most common request. We integrate into existing websites and mobile apps through your current stack, so you don't need to rebuild anything to add an AI feature.

It depends on the setup, and we're specific about it: enterprise API tiers generally don't train on your data, and for sensitive workloads we can run open-source models in your own infrastructure so nothing leaves your environment. We document exactly where your data goes before anything ships.

Yes — and we often do. Open-source models can run dramatically cheaper at scale and keep data in-house. We evaluate both against your accuracy, latency and cost requirements rather than defaulting to the most expensive option.

A typical integration takes 4–8 weeks with us. Building the same capability in-house usually means hiring ML engineers and takes 3–6 months before anything ships. For most companies, bringing us in first and transferring knowledge afterwards is the cheaper path.

We monitor model performance, cost per request and accuracy drift, since model behaviour changes as providers update them. You get documentation and training so your team can maintain it — or we handle ongoing improvement on a retainer.