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I do not build generic AI chatbots.I translate expert knowledge into operating systems.I am Dahlia Adam, founder of Dahlia in Bloom. I hold a BBA in Business Analytics and a Lean Six Sigma Green Belt.My completed system work includes:• A lead-qualification system with readiness scoring, summaries, notifications, a database, and an admin dashboard• An AI support assistant trained on a business owner’s policies, language, and boundaries• Subscription infrastructure using Stripe, webhooks, database logic, and automated accessThe infrastructure dictates the quality of the customer experience.A useful AI system must understand the founder’s framework, language, decision rules, boundaries, offers, and customer pathways.That is what Dahlia in Bloom engineers.
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AI Voice Agent for Real Estate Lead Booking
An AI-powered phone receptionist built for real estate agencies that answers inbound calls, qualifies leads in real time, and books showings directly to Google Calendar — all within the call itself, with no manual follow-up required.
The problem: Real estate agencies routinely lose leads to missed calls — after-hours inquiries, calls during showings, or overflow during busy periods. A slow callback often means the lead has already moved on to a competitor.
What it does:
Answers every call instantly, 24/7
Naturally qualifies the caller (buy/sell intent, area of interest, showing vs. agent call)
Collects contact details and preferred timing conversationally
Checks real-time calendar availability before booking
Prevents double-bookings and duplicate entries automatically
Confirms the appointment out loud before ending the call
Built with: Retell AI (conversational voice layer), n8n (workflow automation and business logic), Google Calendar API (scheduling)
Available for: Custom builds for real estate agencies, brokerages, or property management companies looking to stop losing leads to missed calls
Beautiful workflow by the way. My nicheeee
At CRITER.AI, we’re building AI that helps migrants avoid expensive legal mistakes and visa bans by auditing Australian visa applications against immigration law. We’ve been getting a healthy amount of investor interest, but there was one recurring piece of feedback: our pitch deck was lackluster.
Fair enough. As a lawyer, my creativity usually goes into finding the legal issue everyone else missed, not deciding which shade of blue says “invest now.”
So we set out to change that. We chose Contra over its competitors because the caliber of people looked considerably stronger.
When I posted our request for a pitch-deck redesign, I expected a few people to reach out with quotes. As of today, we have had 303 applicants.
For a bootstrapped business, working through that volume was simply impossible. So we created a challenge:
“Here is my pitch deck. Pick the slide you think needs the most work, and show me what you can do.”
What followed was fascinating.
101 applicants (around 1/3) never replied. Of those who did, around 10% simply reiterated what they had already said. One person replied that, with 250 applicants, they were not going to bother trying. lol.
Several asked whether the test would be paid. 12 sent a quote for the work to be paid first, after which I could decide whether to go further.
Less than 25% of the applications were obviously AI-generated. We are by no means AI-phobic, but the Claude watermark was perhaps a little too much. You know who you are dude!
2 people had trouble opening the Google file we sent. I’m still not entirely sure why.
7 people were so impressed by the work they did that they watermarked it, labelled it “draft,” or otherwise made it impossible to download for further review.
12 people reached out through LinkedIn rather than the app; a further 18 emailed; and 1 person even called me.
18 people not only sent an example slide; they also signed up for our free immigration-advice beta test.
From 303 applicants, we narrowed the field to 15 people who were simply too good to ignore.
Every one of those candidates took the time to understand what we were trying to do, what CRITER.AI is about, and spoke to us as people rather than as another job listing. Two spotted mistakes in the pitch deck that we would not have noticed without them. Another created a 3-minute Loom presentation explaining the changes they made and why they mattered. Most went beyond the requested single slide. One applicant redesigned the entire pitch deck and sent it watermark-free.
It was a difficult decision, but the person we chose had just that little bit more talent than an exceptionally strong group. They not only showed that they understood what we wanted and impressed us with the slide they redesigned; they also suggested another slide we had never considered relevant, then created it using information we had not even provided.
They took our AI immigration-law business and made our pitch deck speak to investors on a human level.
Can't wait to come back to resign our website, social media and other design work.
Lovely and impressive!
AI Lead Qualification & Appointment Automation System
AI Lead Conversion Platform — Telegram, CRM & Automated Scheduling
Designed and developed an AI-powered lead conversion system that handles incoming Telegram conversations from first contact to qualification, CRM synchronization, and appointment booking.
The AI works with conversation history, lead profile data, unanswered questions, current funnel stage, available actions, and verified CRM data to determine the next appropriate step and generate structured responses.
The system automatically creates and updates leads in the CRM, manages qualification stages, books meetings, generates Yandex Telemost links, sends reminders, handles rescheduling, and escalates exceptional cases to a human manager.
The architecture includes dialogue state management, message deduplication and batching, PostgreSQL persistence, concurrency protection, validation before actions are executed, and safeguards against sending unverified or prohibited information.
This is not just an AI chatbot — it is an automated sales workflow that connects Telegram, AI, CRM, scheduling, and human escalation into one controlled process.
AI makes decisions within controlled business rules instead of operating as an unrestricted chatbot.
The concurrency safeguards and validation-before-actions detail is a strong distinction here—booking systems usually fail at the edges, not in the happy path. I’d expose a compact lead state to the operator (stage, next action, last unanswered question) so a human can resume...