Semantic search (RAG) for real estate agents.
Built for the real estate agency who's already tried the off-the-shelf option and bounced off it.
The friction real estate agencies hit that other industries don't.
Agents live on their phones between open homes, listing enquiries, and vendor updates that can't wait until Monday.
Lead response speed decides who gets the listing, and most agencies lose leads simply because nobody replied fast enough.
Listing copy, buyer follow-ups and appraisal requests drafted fast, with personal information handled under the Privacy Act 2020.
Knows the New Zealand rules.
Ask questions across your manuals, contracts and records and get answers with the source attached, not a guess.
- Serious breaches get reported
- An annual return every year
- ACC levies set by industry
On the tools or running at scale, the admin still eats the week :)
The questions a public sector buyer asks, answered up front: privacy impact, the NZISM, where the data lives and how an algorithm is held to account.
*Illustrative example based on nine hours a week of admin. Actual results vary by workflow and business.
How the work runs
We find the one workflow costing real estate agencies the most hours, ship a working slice into production in weeks, prove it pays for itself, then harden it and hand it over.
An AI agency, minus the theatre.
*Every engagement is scoped and quoted up front. Results vary by workflow and business.
Less admin.
More weekend.
No theatre.
What you get
If we build the right slice first, real estate agencies feel the difference inside the first month.
- Indexes Drive, SharePoint, Notion, Slack, your CRM
- Returns answers with source links - no hallucinations
- Permissioned so staff only see what they should
- Re-indexes nightly so results stay fresh
- No annual contracts
- No shelfware or licences you do not use
Straight answers
Is semantic search (RAG) worth it for a smaller real estate agency?
Often, yes - and counterintuitively the ROI is sometimes faster than for the big end of town because there's less integration overhead. We'll tell you honestly on the scoping call if it isn't.
What happens if we want to swap a vendor out later?
Semantic search (RAG) is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Pinecone, Claude, Postgres pgvector, Vercel AI SDK are our defaults, but the build is intentionally portable.
Do you have proof this works for real estate agencies?
Direct case study: Average search time drops from 6 minutes to 12 seconds. Happy to walk you through full numbers on a call.
Can it remind us about the annual return?
Yes. Every company must file an annual return with the Companies Office each year, separate from its tax return.
Can AI help with payday filing?
It can prepare it. Employers file employment information every payday, within 2 working days when filing electronically, and automation can have it ready for a person to check.
Let's chat
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Tell us what you're trying to do and we'll reply with how we'd build it, no obligation.
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