All work

Case study · Real estate and land zoning

Lander.

An AI-powered chatbot for the real estate industry that simplifies land and zoning regulation queries.

What we built
AI chatbot web app
Stack
Next.js 15.3.1, React 19.0.0, TypeScript 5, Material-UI (MUI) 5.15.11
Services
AI chatbotWeb appData processing
Monitor showing the plot analysis start screen with precinct and plot number fields

About the project

What is Lander?

We built an AI-powered chatbot solution for the real estate industry to simplify land and zoning regulation queries. The platform enables users to enter a district and plot number and instantly receive details about applicable development plans, building permissions, and zoning regulations. Designed for a Polish client, the system integrates local zoning data with legal frameworks, turning static spreadsheets into an intelligent, interactive assistant.

The problem

What was slowing them down.

  1. 01

    Data stored in Excel sheets

    The client had land and zoning details stored in Excel sheets, including zoning plans, district data, and area designations.

  2. 02

    Manual effort and cross-checking

    Accessing and interpreting this data required manual effort, deep familiarity with Polish building law, and time-consuming cross-checking against government regulations.

  3. 03

    Hard to know what could be built

    This made it difficult for property owners, investors, and legal professionals to quickly determine what could be built on a given plot of land.

The solution

What we built.

For property owners, investors and legal professionals

We developed a chatbot powered by GPT-4.1, combined with a structured data backend, to provide real-time answers about plots and zoning.

  • Input a district and plot number to view relevant zoning regulations.
  • Understand which development plan applies and what construction is permitted.
  • Ask follow-up questions about regulations, laws, or building rules in natural language.
  • The platform merges a Next.js frontend, AI embeddings, a vector database (Upstash Vector), and document parsing tools.

Results

What changed.

Users and client

  • Faster decision-making

    Reduced time from hours of manual checking to instant chatbot responses.

  • Improved accessibility

    Non-technical users can now understand complex zoning laws with ease.

  • Scalability

    Capable of handling large datasets of plots and zoning plans.

  • Trust & accuracy

    Provides context-driven, regulation-backed answers that align with Polish law.

  • Competitive advantage

    Positioned the client as a digital innovator in the real estate industry.

Key challenges

The hard parts, and how we solved them.

  • Unstructured Data: original plot and zoning data existed in raw Excel format.

    Processed and normalized Excel data into structured JSON for reliable AI querying.

  • Complexity of Legal Regulations: Polish building laws and zoning rules are highly detailed.

    Used embeddings and vector search to ensure the chatbot retrieves precise, context-specific regulations.

  • Accuracy & Trust: misinterpretation of building laws could cause serious risks.

    Integrated AI with regulation-backed document search to provide verifiable answers instead of relying solely on generative output.

  • User Experience: needed to balance technical accuracy with an intuitive interface.

    Designed a clean, modern UI with real-time streaming responses, markdown rendering, and smooth interaction flow.

Desktop monitor in an office showing the plot selection panel and chat windowLaptop in dramatic light showing the plot selector and AI assistant chat

Tech stack

What it runs on.

Frontend
Next.js 15.3.1React 19.0.0TypeScript 5
UI & Styling
Material-UI (MUI) 5.15.11Tailwind CSS 4React Markdown 10.1.0React Spinners 0.16.1
Backend & API
Next.js API RoutesStreaming responses
AI & Document Processing
OpenAI GPT-4.1OpenAI EmbeddingsUpstash Vector
Data Management
JSON databaseExcel processing (xlsx 0.18.5)PDF processingRedis 4.7.0

Process

How we got there.

  1. 01Data Ingestion

    Extracted zoning and plot details from Excel sheets into structured JSON.

  2. 02AI Integration

    Embedded zoning and legal documents using OpenAI embeddings and stored them in Upstash Vector.

  3. 03Frontend Development

    Built a modern UI with Next.js, React 19, MUI, and Tailwind CSS for smooth interactions.

  4. 04Backend & APIs

    Created serverless APIs via Next.js for chatbot queries and data retrieval.

  5. 05AI Chatbot

    Integrated GPT-4.1 with real-time streaming responses for conversational interaction.

  6. 06Testing & Optimization

    Ensured accuracy, scalability, and compliance with regulations.

Building something like Lander?