Searching for properties on online real estate platforms often begins with promise but ends in frustration.
Customers are presented with faceted search filters—price, location, number of bedrooms, property type—yet these rigid parameters rarely capture the nuance of human intent.
A buyer looking for “a house in a particular neighborhood with good schools, particular size and structure while fitting their budget” is forced to translate their vision into dropdowns and sliders.
The result? Missed opportunities, cognitive overload, and a fragmented journey.
Faceted search assumes the customer knows what they want and how to express it in structured terms. But real estate decisions are emotional, contextual, and exploratory. Customers toggle filters endlessly, skim listings, and abandon sessions when the experience feels impersonal or overwhelming.
This is where Flashfill.ai
steps in to reimagine the journey through agentic AI – an ensemble of intelligent agents that adapt, converse, and focus on what’s important to the user.
Flashfill’s product suite of Filterly, Chatly, Notely, and Askly transform static search into a dynamic, personalized experience:
🧠 Filterly replaces rigid filters with adaptive intent modeling. Instead of asking users to select parameters, it infers preferences from natural language surfacing homes that captures buyer’s thought, while still giving them flexibility.
In the demo, a user enters a free-flowing query. Filterly converts it into structured search criteria, selecting filters on their behalf. The possibilities are endless, as a customer can type a simple query with 2-3 parameters or a very complex query which may cover wider range of filter values. Filterly adapts to match the user’s needs. Customers retain full control and can adjust filters at any time, combining intuitive search with flexible refinement.
💬 Chatly introduces a conversational layer that acts as a real estate co-pilot with multi-turn conversations to support how customers work towards finding their dream home. Users can ask, “Show me homes in Austin between $500K and $800K” and then follow it up with “3 Bedroom and 2 Bathroom with a car garage” and Chatly understands, refines, and guides the search with contextual awareness.
In the demo, a user search journey experience is transformed as Chatly understands and infers its intent every turn. Chatly also provides smart context switches, and memory capabilites to re-start search where it was left at, providing continuity to customers.
📝 Notely enables users to annotate and favorite listings, compare features, and capture impressions—turning passive browsing into active decision-making while customers can focus on what matters to them. Notely provides customer focused sessions, memory to store favorties and history and multi-turn chat capabilites. Think of it as a smart way to shortlist before making a decision.
In the demo, customer is analyzing their favorite homes based on various factors, comparing them to each other while also looking at market trends from internet and getting intelligent inputs and suggestions from AI.
📝 Askly empowers users to ask deeper questions on particular items like “Why is this home priced higher than similar ones nearby?” or “What’s the walkability score?” surfacing insights that faceted search may not. allowing you to focus attention on single item research and anlysis.
In the demo, customer is asking exploratory questions which allows them to dig deeper on a single property asking complex questions that matter to them and uncovering insights which are sometimes difficult to find while adding context and relevant information to it.
