How to build a Real Estate Agent AI Chatbot

11 Sep. 2019 - - Total Reads 824

We’ve just gone through the learning curve of building (and rebuilding a number of times) a hopeful “Turing Test” passable Real Estate Agent AI Chatbot. Was it easy? Hell no. The Turing test is a huge benchmark for a reason– the natural language we bumble through even as a toddler, is incredibly sophisticated. To get a computer to do that like a real Real Estate agent in a SciFi show (there may not be too many real estate agents in a sci-fi show, but you get the idea), is really light years ahead.

It’s live and working. Check it out on MotionProperty.com.au and their Facebook page (yes, it answers all live chats on Facebook as well). We have at best 6 months of programming to improve it.

How to build and AI Chatbot

The AI Part of any chatbot engine is really just a comparison tool. It looks for like-minded words or phrases, and comprehends if you replace one for another. For example, “Rent” could be “Lease” or “Renting”, and as soon as you give that example, is Rent as in “lease my place” or “I want a place to rent”, interchangeable again is “I want to live there”, which also means “live” as in performance. This shows how fast things can escalate outside the scope of AI or at best, form part of the training inside the AI platform.

We came up with a couple of processes to support these challenges– you can approach this from Top-down or Bottom-up.

Bottom-up is where you sort of go for it, and start with an open-ended “ask-me-anything” chat and try building in intended cornerstones to get to the bottom- the info you want to capture, the person’s name, location of interest, budget, etc.

Top-down is where you build out an elaborate data flow diagram (DFD pictured here), showing all the “pathways” you think how most conversations will go down. We did this from our second rebuild onwards for two reasons:

First, there were too many options to cover off in the Bottom-up approach. So while we did get something working, the potential conversation pathways were enormous, and conversations that got to the end were under a few percent.

Second, we could launch what was basically a multi-choice questionnaire from the DFD, so that 100% of the conversations reach a conclusion. It wasn’t anything better than a dumb form at this point, but it gave us a lightbulb moment. We could tackle the programming in a piecewise process, removing layer by layer of the multi-choice into smaller AI conversations. This approach worked superbly. As we pulled out the first multi-choice from the top of the DFD tree, it changed the entire feel of the conversation– far more human and realistic.

While Bottom-up chatbot programming can work, it seems only applicable when programming something simple, such as handling a single Question pathway (example below). This liner pathway is essentially a zoomed-in version of every box in the larger DFD.

For our “Be the Agent” AI chatbot with 5-6 key services and a range of sub-trees for every conversation point, the Top-Down approach has been a great solution so far, and is only getting better with every iteration.

For more info on our Chatbot strategy, design and development services, please see our section on Chatbots under Our Services.

Michael Simonetti
Posted by:

Post Reads: 824

Share this

Go on, see if you can challenge us on "How to build a Real Estate Agent AI Chatbot" - Part of our 171 services at AndMine. We are quick to respond but if you want to go direct, test us during office hours.

Add Your Comment

Trusted by

tribe
Appstore
Tony and Guy
BlackMores
One Shift
WTFN
DepSkin.com
Fortune Institute
MSAC
AC/DC
Palace Cinemas
Liveoneday
Acquia Drupal Certification
Metricon
ISO Certified
Telstra
News
Vendor Advocacy Australia
Scrum
Loan Market
Instant RockStar
Dusa
Bintani
MyAccount
131 Pizza
Amino Active
Swin
Movember
Uber
DeeWhy Market
Engine Swim
Melbourne Central
Madman Entertainment
Engineers Without Borders
CBRE
Kay Burton
Focus On Furniture
Bondi Sands
National Relay Services
Max
vissf
Australia Goverment
CAN
Drupal
CSquared Executive
Jalna
Rock Pool Group
SunSense Digital Agency
Melrose MCT
Oracle
Watches of Switzerland
Fed Square
High Street Armadale
Rydges
Dinosaur Designs
Maxine
Paypal
BCG
Hanover
Tek Ocean
Sports Power
Fresh Cheese Company
MAP
Atlantic Group
University of Melbourne
Green St Juice
Unsw Australia
Castran Gilbert
Grow Your Business
Aqium Gel
Schiavello
Eway
Magento
OMS
Arc One
Etihad Stadium
Marshall White
Parker Lane
The Burger Cheese
Melbourne Heart
Brisbane Times
Melrose Health
TPP
White Suede
RMIT University
OJAY
Natralus Australia
Rackspace
Switzer
Smart Company
Crumpler
QV Skincare
Australian Physiotherapy Association
Shell
Van Egmond Group
Bank of Cyprus
Tassal
Ego Pharmaceuticals
Jetstar
Smh
Mamma Lucia
Microsoft-Certified-Azure-Fundamentals
Viktoria + Woods
Dial Before You Dig
Chia
Arthur Galan
Matchbox
Positive Poster
21st Century Education Agency
Bigcommerce
Ebay
GooglePlay
Novvi
ADP Payroll
TSB
Magento Solution Specialist
Forbes
Associated Press
Digital Agency Panitsync
Cronos Australia
James Buyer Advocates
GPT Group
Google
The Age
Bulk Nutrients
Nmiinsurance
Fairfax Media
Coles
POSTER Magazine
Moov Head Lice
Hairhouse Warehouse
LBG Australia and New Zealand
National Museum of Australia
Canberra
Ello
McArthur Skincare
Ubertas Group
Victorian Government
Professional
Grow Rich
Royal Freemasons
abc
Anthill
Elucent
iPrimus
Toy World
Grays Ecommerce
Macmillan

Testimonials

The &Mine team is great to work with and went beyond the brief to deliver a family violence website which was both engaging and easy to use. The team is collaborative, understand the constraints and sensitivities of a government environment and work alongside you to develop creative and practical solutions and ideas. Stakeholders have only had positive feedback about the website including with comments such as the best government website I have seen. Christine Panayotou, Director Communications, Family Safety Victoria

More Testimonials
AndMine-Google-Partner-Signature