field notes · ai and agents
AI integration
A model that reads your CRM and help articles, and a person approves.
In short
AI integration is the work of connecting a language model to the software a business already runs, such as a CRM, a database or a helpdesk, so it can answer questions or do part of a job. Zenoware, a small studio in Auckland, does this for teams in New Zealand and overseas. It covers assistants that answer from your own documents, making the agents you already have cheaper to run, and training the people who'll look after it. The first call is free.
What you get
Customer questions answered from your help centre, with a link
A morning inbox sorted, each email with a two-line summary
The same agents as before, on a smaller monthly bill
How it runs
- 01
Watch the job
First we watch someone do the job and write down every lookup they make.
- 02
Index the sources
Their sources (help articles, CRM records, PDFs) get indexed for search.
- 03
Draft and approve
The model drafts; a human hits send on anything that matters.
- 04
Trim and hand over
Easy jobs move to a cheaper model, and your developers learn to run it.
Who you'll work with

JP Caparas
Founder and lead engineer
Thirteen years of principal engineering, including development lead at Public Trust. OpenAI Codex and Devin ambassador in New Zealand. You talk to the person who writes the code.
About the teamQuestions
What is AI integration?
Connecting a language model, from OpenAI, Anthropic or Google, to the systems you already have. The model on its own knows nothing about your customers. Once it can read your CRM or your help articles, it can draft replies, summarise records or sort incoming email. The integration part is the plumbing, the permissions and the checks around it.
Can an AI chatbot answer from our own documents?
Yes. The trick (it's called RAG) is to search your documents first and hand the model only the relevant paragraphs with the question. So it answers from your help centre, not from half-remembered internet, and links the page it used. When it isn't sure, it says so and passes the chat to a person.
How do you reduce LLM API costs?
Stop sending everything to the biggest model. Sorting an email into five folders is a job for a small, cheap one. Prompts that repeat can be cached. Context the model never looks at can be cut. We time and price each task before and after every change, so you can see which one helped.
Is it safe to send company data to an AI model?
With the right setup, yes. Business plans from OpenAI, Anthropic and Google don't use your data for training, so check that first. We also strip out anything personal the model doesn't need (a phone number rarely helps it sort an email), log what was sent where, and check data residency if it matters.
Do you train our team?
We do, because otherwise you'd be ringing us every time a prompt needs changing. Developers, product people and testers get hands-on sessions on writing prompts, testing output and picking models. You keep the diagrams, runbooks and dashboards we used.
You might also need
Tell us what isn't working.
The first call is free, and we'll tell you straight whether we're the right people for it.