AI Chatbots

The reliable wins are unglamorous: answering repeated questions, extracting information from documents, drafting first versions, routing work to the right person, and searching years of internal material. Those tasks are high-volume, tolerant of a review step, and easy to measure. AI is a poor fit where a wrong answer is expensive and unverifiable, or where the process is not written down anywhere yet — in that case documenting it comes first.

No. We use enterprise API tiers where the provider contractually excludes your inputs from training, and we can run open models entirely inside your own cloud tenancy when data cannot leave it at all. Your documents are indexed in infrastructure you own. We will also tell you plainly which parts of a proposed system involve sending data to a third party, so that decision is yours to make with the facts.

Accuracy depends far more on grounding than on the model. An assistant answering from your own approved documents and citing the source is dependable in a way that a general chatbot is not. We design for being wrong anyway: answers carry citations so a person can verify them, the assistant is instructed to say it does not know rather than guess, low-confidence cases hand over to a human, and every exchange is logged so you can audit what was said.

A single well-chosen use case — a support assistant, a document extraction step — usually reaches production in four to eight weeks, and the saving is measurable within the first month of real use. Broad programmes take longer and fail more often, so we deliberately start with one task, prove the number, then expand. If the first use case does not pay back, you have spent a small amount finding that out.

Less than most people expect. Retrieval-based systems read your existing documents, policies and records as they are, so no training dataset is required — a few hundred pages of good documentation is often plenty. What matters is that the material is current and accurate; an assistant grounded in out-of-date procedures will confidently repeat them. Tidying the source content is frequently the most valuable part of the project.

Whichever fits the task, and we avoid designing you into a single vendor. In practice that means OpenAI and Claude for reasoning and drafting, Gemini where Google Workspace is already in use, and open models from Hugging Face when data has to stay in your own tenancy. The orchestration layer is built so a model can be swapped without rewriting the application — pricing and capability in this field move too quickly to bet everything on one supplier.

That is not what we build, and it is not usually where the value is. The systems that work take the repetitive middle of a job — copying, checking, looking things up — and leave the judgement with a person. Teams typically end up handling more volume with the same headcount, and spending their time on the cases that actually need a human. We are happy to say when a task should not be automated at all.

A fixed written quote for the build, and running costs stated separately, because model usage is a real ongoing expense that some proposals quietly omit. We estimate the monthly API and hosting cost at your expected volume before you commit, and design to keep it predictable — caching, smaller models for simple steps, and limits so a spike cannot produce a surprise invoice.