CASE STUDIES

Shipped Systems.
Verifiable Outcomes.

Every system built by AZK AI is backed by verifiable metrics and auditable architectures. Below is the technical breakdown of our systems currently running in production.

Advisory · Multi-Agent PlatformOwn ProductLive: MVP Deployed

Advisify

3x
Faster Advisory Response
Validated Business Outcome

1. The Challenge

Immigration firms and applicants waste thousands of hours manually reviewing constantly changing visa laws and policy updates. General LLMs hallucinate rules, lack cutoff knowledge, and cannot provide cited eligibility matching, creating high liability risks for professional advisors.

2. The Solution

We engineered a multi-agent policy matching platform. The system ingests a user's actual qualifications, nationality, and work records, runs deterministic checks against real-time visa codes, and outputs an auditable eligibility path report.

3. System Architecture

Data Flow Pipeline
01
User Profile
Nationality, work, degrees
02
RAG Retrieval
Searches live visa databases
03
Rule Filter
Deterministic eligibility pass
04
Agent Consensus
GPT-4 & Claude output weight
05
Audit Trail
Source cited report output

4. Business Outcome

3x Faster Advisory Response: Turnaround times for comprehensive visa eligibility reviews dropped from 1–5 business days of manual search to less than 5 minutes, with 45% streamlined workflows and 2x scalable AI assistance.

Technology Stack

Next.jsPython FastAPIGPT-4.1Claude SonnetMongoDBPinecone Vector SearchAWS VPC
AI Search · Real Estate EngineClient WorkProduction Deployment

Direct Home

5x
Property Discovery Speed
Validated Business Outcome

1. The Challenge

Property investors spend weeks scrolling standard real estate portals using basic keyword filters. These portals cannot evaluate complex conditions (e.g. proximity metrics, investment yields, zoning codes, and construction ages) simultaneously to score optimal acquisitions.

2. The Solution

A hybrid semantic search and scoring engine. We built ingestion pipelines that vectorize municipal zoning records, property descriptions, and coordinate maps, allowing investors to submit conceptual natural language searches (e.g. 'high-yield duplex near transit built after 2010').

3. System Architecture

Data Flow Pipeline
01
Query Parse
Intent, parameters extraction
02
pgvector Search
Semantic neighborhood match
03
Scoring Engine
Zoning & investment metrics
04
Re-ranking
Sort by proximity & yield

4. Business Outcome

5x Property Discovery Speed: Investors locate qualifying assets 5 times faster than manual filtering, leading to 50% quicker decisions and a 35% improvement in lead quality.

Technology Stack

Next.jsPython FastAPIOpenAI EmbeddingsMongoDBPostgreSQL pgvectorGoogle Maps API
Personalization · Matching PlatformClient WorkProduction Deployment

SmartGuy

5x
Matching Discovery Speed
Validated Business Outcome

1. The Challenge

B2B business networks struggle with matching members based on service requirements, location, and historical transaction behavior. Manual search directory grids convert poorly and fail to connect appropriate partners efficiently.

2. The Solution

We designed an automated matching and recommendation core. The system monitors user session activities and service listings, vectorizes profiles, and utilizes multi-model ranking to suggest high-conversion B2B connections.

3. System Architecture

Data Flow Pipeline
01
Activity Stream
Ingests user click behaviors
02
Profile Vector
Constructs business embeddings
03
Similarity Match
Pinecone cosine comparison
04
Action Hook
Auto-alerts matching partners

4. Business Outcome

5x Matching Discovery Speed: Provides 5x faster network matching discovery for business assets, enabling faster decision-making and better lead quality across transactions.

Technology Stack

Next.jsNestJS BackendOpenAIMongoDBRedis CacheAWS ECS
Own Product · Education AIOwn ProductActive MVP

AcePrep AI

2x
Learning Velocity
Validated Business Outcome

1. The Challenge

Students preparing for high-stakes exams struggle to get instant, reliable answers to complex questions, practice in a structured, adaptive way, and identify concept gaps before it is too late.

2. The Solution

An intelligent exam preparation platform combining a vision-based step-by-step Q&A engine, a configurable dynamic quiz generator, and a personal revision library grounded in real exam mark schemes.

3. System Architecture

Data Flow Pipeline
01
Image/Text OCR
Ingests questions via snaps or typing
02
Syllabus Grounding
Links answer steps to mark schemes
03
Reasoning Solver
Generates logic steps and subject labels
04
Dynamic Quiz Engine
Compiles configurable practice sessions

4. Business Outcome

2x Learning Velocity: Students achieve target exam readiness scores twice as fast compared to static practice materials, reducing preparation schedules by 40% with a 92% pass success rate.

Technology Stack

Next.jsFlutter (Admin)Python FastAPIOpenAI GPT-4 VisionMongoDBPinecone Vector DBRedis Queue
AI Ingestion · Professional ServicesClient WorkProduction Deployment

The Chemistry Room

99%
Extraction Accuracy
Validated Business Outcome

1. The Challenge

Professional services intake is highly manual, relying on unorganized client briefs, scanned files, and emails. Sorting client requirements, mapping unstructured text formats, and manually entering data into CRMs takes hours, delaying response times and leading to transcription errors.

2. The Solution

We engineered an AI ingestion and document processing pipeline. The system automatically extracts key client details, service queries, and requirements from incoming briefs, structuring them into unified JSON schemas and routing them straight to CRM databases.

3. System Architecture

Data Flow Pipeline
01
User Brief Ingestion
Uploads client documents
02
OCR & Ingestion
Parses unstructured document text
03
Structured Extraction
Extracts parameters into JSON
04
CRM Integration
Routes records to CRM database

4. Business Outcome

99% Extraction Accuracy: Automated brief extraction achieved 99% accuracy, cutting administrative intake times by 80% and routing leads to sales teams instantly.

Technology Stack

Next.jsPython FastAPIOpenAI GPT-4MongoDBHubSpot APIAWS ECS
AI Assistant · Hospitality & MembershipClient WorkLive: MVP Deployed

Naturals Club

50%
Reservation Lift
Validated Business Outcome

1. The Challenge

Hospitality membership portals receive hundreds of customer inquiries daily about package rates, membership amenities, and room availability. Guest services staff spend most of their time answering simple, repetitive questions instead of focusing on high-touch guest relations.

2. The Solution

We deployed a secure, database-grounded RAG chat assistant integrated directly into the guest booking portal. The assistant provides instant, accurate answers about amenities and rates, matching guest queries with available reservation slots in the booking logs.

3. System Architecture

Data Flow Pipeline
01
Guest Query
Chat assistant input
02
Vector Retrieval
Searches semantic amenity database
03
RAG Response
Generates grounded answers
04
Booking Sync
Schedules calendar reservation slots

4. Business Outcome

50% Reservation Lift: Direct guest bookings increased by 50% via automated reservation assistance, while freeing up support staff from handling repetitive FAQs.

Technology Stack

Next.jsNode.js BackendPinecone Vector SearchMongoDBGoogle Calendar APIVercel
ARCHITECTURE FIRST

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