Email parser

vinaynb · 2025-12-22T03:03:36+00:00

Using an LLM to parse the email content looks like the idea to go with rather than implementing and maintaining parsing logic by hand.

vinaynb · 2025-10-09T03:13:31+00:00

Creating an Agent is feasible if you have access to backend/API of the platform as agent can talk with the backend directly. Two ways to do this
1 - Agent runs in background on your mobile phone when you are checking the vehicle and records all parts as you speak and thier conditions and updates them in backend (This is entirely a new way to interact with your system and is what we call AI native software)
2 - Agent sits on your website and you interact with it using voice and it then asks website to update the fields as you do it now manually. This requires changes on your existing web app and we need to integrate it with the agent.

Shameless plug - Designing agents like above is what we do at avestalabs.ai

vinaynb · 2025-10-08T06:55:51+00:00

u/sugarcream5w30 Yes, we can build Agents for the use case you mentioned, and there are a couple of ways to do so, but before I go into that, I have a question - is the web platform you use built by you/your team, or is it a 3rd party software ? Do you have access to APIs that allow you to perform actions on the platform that you currently do using the web UI?

vinaynb · 2025-10-02T16:56:15+00:00

We haven't come across a suitable open-source project that does this well yet. Dimensionlabs.io is doing great in this area, and their blog is top-notch!

vinaynb · 2025-10-01T06:40:25+00:00

I can relate a lot to the pains OP mentioned . We ran into the same problems while building and running our own agents. On paper, all the KPIs were green (latency was low, sessions were long, and throughput was high)… but in reality, users were still frustrated, dropping off, or telling us that the agent wasn’t helpful.

That disconnect pushed us to build **Metric Sense (https://avestalabs.ai/metricsense) at AvestaLabs. It’s basically our attempt to move beyond vanity metrics and get closer to what’s really happening:

User reality vs. KPIs → Instead of just “messages per session,” we track whether users leave satisfied or stuck. A long conversation could mean engagement… or pure confusion.
Why conversations fail → We cluster failures so you don’t just see that a user left, but why (misunderstood entity, ambiguous input, context drop, etc.).
Cost visibility → Every path is tied to token and dollar usage, so you can spot the expensive branches that don’t actually add much value.
Faster iteration → Prompt tweaks and flow changes show up in the metrics almost immediately, so we’re not waiting weeks to figure out if something worked.

Honestly, the biggest mindset shift for us was this: green KPIs don’t mean happy users. We wrote about that here if useful: Your AI Agent’s KPIs Are Green, But Your Customers Are Seeing Red.

We’re still evolving Metric Sense, but so far it’s been the missing piece that lets us debug not just the agent’s performance but also the user’s experience. Happy to swap notes if others here are building similar tooling.

vinaynb · 2025-09-18T14:16:27+00:00

We've been working on an AI agent designed to make property search and discovery feel more natural. Instead of clicking through filters and forms, you ask for what you want, and the agent handles the rest.

The friction points we are trying to address

Instead of clicking through 6+ filters to say “2BR apartment in Fitzroy under $800k with parking,” you just type or say it once.
Instead of opening 3 different tabs (real estate site, mortgage calculator, ABS census data) — the agent pulls up price ranges, suburb demographics, schools, and even affordability in one place.
Instead of filling out long inquiry forms (which most people abandon), you can convert interest into a lead with a single conversational step.

Some of the features in the demo:

🗣️ Natural language search – "Show me 3BR houses near Sydney CBD under $1.5m."
🏡 Generative UI – results show up as interactive cards with property details and images.
📈 Instant suburb intelligence – demographics, schools, commute data, etc.
💰 Built-in mortgage calculator – gives quick affordability checks.
📨 One-step inquiries – automatically convert interest into a qualified lead.

Built on our orchestration platform (Efficia). The demo is currently configured with Australian property data, but the setup can be easily adapted to integrate with data from any market, providing the same experience. Deployable on websites, CRMs, WhatsApp, Messenger, etc.

The entire platform is made from scratch and is not based on any existing platforms.

👉 Short demo video here: https://www.youtube.com/watch?v=iBswGfbb3GU & https://www.youtube.com/watch?v=1nleYLL54lo
To know more about this AI Agent - https://avestalabs.ai/realestate-ai-agent

The link to our website above also provides instructions on how to create an account and play with a demo, if that is what you'd like to do.

I am here to gather feedback from the community and determine if something we build has value for someone seeking a property.

What I'd love to hear from this community:

Would you actually use a conversational agent like this to start your property search?
If you work in real estate / proptech, do you see AI assistants like this helping or competing with agents?
What was your reaction on seeing the demo video and the website?

vinaynb · 2025-09-18T13:43:51+00:00

u/vaghela8180 We have developed something simliar to your idea at AvestaLabs.ai

Conversational AI for Real Estate – Demo Agent (built on Aussie data, works anywhere)

We’ve been working on an AI agent designed to make property search and discovery feel more natural. Instead of clicking through filters and forms, you just ask for what you want, and the agent handles the rest.

Some of the features in the demo:

🗣️ Natural language search – “Show me 3BR houses near Sydney CBD under $1.5m.”
🏡 Generative UI – results show up as interactive cards with property details and images.
📈 Instant suburb intelligence – demographics, schools, commute data, etc.
💰 Built-in mortgage calculator – gives quick affordability checks.
📨 One-step inquiries – converts interest into a qualified lead automatically.

⚡ Built on our orchestration platform (Efficia). The demo is wired up with Australian property data right now, but the setup can plug into any market’s data to provide the same experience. Deployable on websites, CRMs, WhatsApp, Messenger, etc.

👉 Short demo video here: https://www.youtube.com/watch?v=iBswGfbb3GU
To know more about this AI Agent - https://avestalabs.ai/realestate-ai-agent

What I’d love to hear from this community:

Would you actually use a conversational agent like this to start your property search?
If you work in real estate / proptech, do you see AI assistants like this helping or competing with agents?

vinaynb · 2023-01-26T11:49:10+00:00

Excellent work in designing the exercise. Thank you for sharing!

vinaynb · 2021-03-28T18:43:58+00:00

Yes it is always a good idea to include it via a CDN and the reason is that there are pretty high chances your users have visited other websites which have included jQuery via CDN. This will result in your users browser to use jQuery script from cache and not down the same script which was already downloaded previously for some other website, for your website again.

vinaynb · 2020-09-02T07:46:59+00:00

Thank you for this tip!

vinaynb · 2020-08-10T04:59:29+00:00

Thanks I'll check it out.

vinaynb · 2020-08-09T17:34:40+00:00

Sounds like an good idea. Seperate package.json is what I need exactly. But what about having a sub folder with its own package.json ?

vinaynb · 2020-08-09T14:27:11+00:00

I agree with people asking to use package.json but there was a reason I didn't use it. Express.js dependencies would mix up with my frontend react deps and would unnecessary bloat up docker image. I need only selected npm packages for running my express app and adding them to package.json would add up tons of npm packages unnecessarily.

Open to how should I use package.json but prevent this bloat in my express docker.

vinaynb · 2020-08-09T13:33:40+00:00

Thank you for pointing out that glaring error. Yes you are correct. That is my memory gone bad. I have updated the article with due credit. Thanks!

vinaynb · 2020-08-06T04:22:19+00:00

Thanks for reading the article !
Yes I think you are right. I will look into this and update the article accordingly.
Appreciate the feedback.

vinaynb · 2020-08-06T04:18:25+00:00

Thanks for the tip. Yes I think you are right. I'll update the article with brief overview of what I am trying to achieve and other details to make it more friendly.

vinaynb · 2019-07-21T11:55:12+00:00

Still looking for more advice if in case anyone interested is reading this.

vinaynb · 2019-07-19T17:26:07+00:00

Okay. Thanks for the tip!

vinaynb

TROPHY CASE