AI knowledge base is overhyped at the macro level and underused at the workflow level. For a NZ optometry practice, that gap is where the actual ROI lives – and where most of our work happens.
Optometry practices · Applied AI
Internal AI knowledge base, built around how a optometry practice actually works.
What AI knowledge base actually does
Your team's tribal knowledge, finally searchable. Upload your SOPs, training videos, past emails, and Slack threads – your team asks questions and gets answers with citations.
- 01 Ingests PDFs, Word docs, videos, Slack, Notion, Drive
- 02 Answers with citations back to source documents
- 03 Permission-aware – staff only see what they should
- 04 Detects stale docs and prompts owners to update
Built on: Claude Pinecone Vercel AI SDK Postgres MCP
Without Kiwi Dynamics
With Kiwi Dynamics
Why optometry practices are different
Optometry practices run their businesses unlike anyone else.
- Optometrists balance eye tests, recalls, and frame sales, with recall reminders often the first thing to slip.
- Recall reminders and insurance or funding queries are the recurring admin load behind every optometry front desk.
How we build AI knowledge base for optometry practices.
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for a optometry practice business, so value lands before the build is finished. Internal AI knowledge base.
Talk to usThe outcome for optometry practices
The shape of the result for optometry practices: New staff get to productive 3x faster – less senior-team interruption. Built on Claude, hardened with the rest of the stack as it scales.
Not your typical AI agency.
Honest about what AI can and cannot do
Ships the one workflow that pays for itself
Hours given back, never the size of the invoice
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
Questions
FAQ
How long does AI knowledge base take to ship for optometry practices?
We aim for a working pilot inside 4-6 weeks – narrow scope, real optometry practices data, measurable outcome. From there it's another 6-8 weeks of hardening before you'd consider it production. Full rollouts (multiple sites, multiple teams) typically land in 3-4 months.
Are there hidden costs we should plan for?
Three to know about: model/API spend (which we set up under your own account, not ours, so you see and control it), any new SaaS subscriptions we recommend, and your team's time during rollout. We surface all three in the quote so there are no surprises.
Do you have proof this works for optometry practices?
Direct case study: New staff get to productive 3x faster – less senior-team interruption. Happy to walk you through full numbers on a call.
Who owns the code and the model setup?
You do, on delivery. We deploy AI knowledge base into your own cloud account where possible, with the model setup, prompts, evals and integration code all checked into a repo you own. Claude sits in your account too – we don't operate it from ours.
Skip the pitch.
Tell us the workflow and we'll come back with what we'd build first.
Get in touch
Talk to us about this
Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.