Search quality
NLP-enhanced search adopted immediately by end users, reducing zero-result searches and increasing time-on-catalog metrics.
Technologies used
Django
PostgreSQL
FastAPI
Celery
Redis
Docker
Motivation
The client, a property developer, needed a catalog platform to present and manage their full inventory: apartments, houses, garages, and storage units across multiple developments. Timelines were tight, requirements were well-defined, and quality was non-negotiable: the platform would be used by administrators, content managers, and end buyers. Azati applied AI coding agents throughout the development lifecycle, not to replace engineering judgment, but to compress it. Every decision, every module, and every line of production code was owned by a senior engineer from day one.
Main challenges
Challenge 01
Tight Delivery Timeline Without Quality Compromise
The client needed a working platform, not a prototype, on a schedule that a traditional development approach couldn't meet. Using AI tools to accelerate scaffolding, boilerplate, and routine implementation freed engineers to focus time on architecture, optimization, and the business logic that actually mattered.
#1
Challenge 02
Complex Search and Filtering Requirements
Real estate search is deceptively complex: users filter by property type, area, floor, price range, developer, and availability, all against a dataset that changes frequently. NLP-enhanced search needed to handle natural language queries gracefully alongside structured filters.
#2
Challenge 03
Performance at Scale with Heavy Admin Usage
Content managers update listings frequently. Buyers query the catalog in parallel. Without careful caching strategy and query optimization, the system would degrade under normal usage, a common failure mode in platforms where AI-generated ORM code is never reviewed.
#3
Our approach
Architecture-First, AI-Assisted Implementation
Before any code was generated, our engineer defined the full data model, API contract, module structure, and caching strategy. AI coding agents were then used to accelerate implementation within this structure, not around it. Every generated output was reviewed, tested, and owned before it touched the repository.
Role-Based Admin and Content Manager Functionality
We built distinct permission layers for administrators and content managers, with custom Django admin interfaces tailored to their workflows. AI accelerated the CRUD scaffolding; engineers designed the permission model and UX flows.
NLP-Enhanced Property Search
An NLP-powered search engine was integrated to support natural language queries alongside structured filtering. The system understands queries like 'two-bedroom apartment near the park under 400k' and maps them to structured database lookups with ranked results.
Query Optimization and Redis Caching
Every ORM query was reviewed and optimized: indexes added, N+1 patterns eliminated, and a Redis caching layer implemented for high-frequency listing views. Celery handled background tasks including cache invalidation, notification dispatch, and async data updates.
CI/CD and Automated Tooling
We wrote CI/CD pipelines for automated testing, deployment, and, unusually, automatic JIRA ticket updates triggered by pipeline events, reducing manual project management overhead for the client's team.
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01
Full Property Catalog with Admin Layer
A complete content management system for administrators and content managers, supporting all property types, media management, pricing, availability status, and developer attribution, with role-based access and audit logging throughout.
Key capabilities:
- Multi-type property management (apartments, houses, garages, storage)
- Role-based admin and content manager interfaces
- Bulk update and status management tooling
02
NLP-Powered Property Search
A search engine that handles both structured filters and natural language queries, ranking results by relevance and availability. Built to stay fast as the catalog grows, with indexed queries and cached result sets for common search patterns.
Key capabilities:
- Natural language query parsing and mapping
- Multi-parameter structured filtering
- Relevance ranking with real-time availability
03
Performance Infrastructure
Redis caching for listing views, Celery for async processing, and fully optimized PostgreSQL queries, ensuring consistent performance for buyers and content managers regardless of catalog size or concurrent load.
Key capabilities:
- Redis cache layer for high-frequency endpoints
- Celery async task queue for background operations
- Optimized ORM queries with proper indexing strategy
04
CI/CD Pipeline with Automated Project Tooling
A complete delivery pipeline covering automated testing, containerized deployment via Docker and Nginx, and custom CI/CD scripts that automatically update JIRA tickets based on pipeline state, reducing overhead for the client's project team.
Key capabilities:
- Automated test suite on every push
- Docker and Nginx production deployment
- CI/CD-triggered JIRA ticket automation
Results & business impact
System performance
Query optimization and Redis caching reduced average page load times by over 60% compared to the pre-optimization baseline.
Admin efficiency
Content managers reduced listing update time by 40% through purpose-built interfaces replacing generic admin tooling.
Full code ownership
Every module documented, tested, and maintainable, the client's internal team can extend the platform without requiring Azati's continued involvement.
