AI and LLM systems
AI that does a job, measured against your real cases.
Large language models are genuinely useful for a specific kind of work: reading, sorting, summarising, drafting and answering questions over text. Most businesses have people doing exactly that for hours a day. Infinitech is an AI consultant in Auckland that builds rather than just advises: AI systems that take that work on, grounded in your own documents and data, with guardrails and measurement built in, so you know what they get right and what they hand back to a person.
Where it pays off
The strongest cases are repetitive reading. Sorting incoming email into the right queue. Pulling the key fields out of invoices, applications or forms. Summarising long documents so a decision-maker reads one page instead of forty. Answering staff questions from a policy manual, or customer questions from your product documentation, with the source quoted.
The weak cases are the ones in the headlines: open-ended chatbots with no grounding, and anything where a confident wrong answer costs more than the time saved. We will tell you which side of that line your idea sits on before any money is spent.
Grounded in your data, not the internet
A model on its own knows nothing about your business. The useful systems retrieve the relevant pieces of your own documents, records or knowledge base and give the model only that to work from, with instructions to say when it does not know. Answers cite their source, so a person can check in seconds.
Your data stays yours. Systems are built on providers whose terms exclude training on your content, run inside your own cloud account where that matters, and keep a log of every request so you can audit what was asked and answered.
Measured before it is trusted
Before an AI system touches live work, it is run against a set of real past cases with known right answers. That gives you a plain number for accuracy, shows the kinds of case it struggles with, and sets the threshold below which it should hand off to a person. The same test set is re-run whenever the model or the prompt changes, so a quiet regression cannot creep in.
Rollout is staged: the system suggests, a person approves, and only once the numbers hold does it act on its own for the clear-cut cases.
Priced to pay for itself
Model usage is metered, and a careless design can run up a surprising bill. Systems are built to use the smallest model that meets the accuracy bar, cache what repeats, and cap spend per day. You see the running cost per document or per question alongside the hours it replaces, so the business case stays visible after launch.
Infinitech can also review an AI tool you are already paying for, or a proposal from a vendor, and tell you plainly whether it is doing what was promised.
Questions people ask
Before you get in touch
- Is AI worth it for my business?
- If people in your business spend hours a week reading, sorting, summarising or re-typing, usually yes, and we can put a number on it in the first conversation: hours saved against the running cost. If the work is judgement-heavy or the volume is small, we will say it is not worth it yet.
- Is this just ChatGPT for business?
- ChatGPT for business is a chat window; it still relies on a person to paste things in and check the answer. What Infinitech builds is connected to your documents and systems, runs on your real workload without a person driving it, and is measured against your own past cases. Where a plain chat tool is enough, we will tell you.
- Will our data be used to train AI models?
- No. Systems are built on commercial model APIs whose terms exclude training on customer data, and where sensitivity demands it, on models running inside your own cloud account.
- How accurate is it?
- It depends on the task, and we measure it rather than guess. Every system is tested against real past cases before launch and the accuracy is reported as a number, with the hard cases routed to a person.
- Do we need a lot of data to start?
- No. Most useful systems work from the documents and records you already have. A few hundred past examples are enough to measure accuracy properly.
- Can it plug into our existing tools?
- Yes. Common patterns are reading from a shared mailbox, a document store or a CRM and writing results back, so staff keep working in the tools they know.
Next step
Tell us what is slowing the business down.
A short description is enough. You will hear back from the engineer who would do the work, within one business day.