Demonstration Prototypes

Working Prototypes & Architectural Blueprints.

We believe engineering capability is demonstrated through working systems, transparent architecture, and verifiable data flows rather than marketing claims. Explore our functional prototypes below.

DEMONSTRATION // 01

AI Real Estate Lead Assistant

Industry: International Real Estate & Developments

Business Challenge

In high-velocity real estate markets worldwide, high-value buyer inquiries frequently arrive across international timezones outside standard business hours. Delays exceeding 15 minutes result in prospective buyers moving to competing agencies.

Solution Overview

An autonomous WhatsApp conversational agent powered by FastAPI and Google Gemini. The agent handles inbound inquiries instantly, conducts structured budget and property type qualification, checks live inventory availability, and coordinates viewing appointments with human brokers.

Workflow Breakdown

  1. 1. Ingestion: Buyer initiates chat via WhatsApp QR or portal click-to-chat link.
  2. 2. Natural Language Extraction: Agent parses budget ($ / EUR / local), preferred areas, and cash vs. mortgage intent.
  3. 3. Inventory Check: Internal FastAPI service queries active listing database for matches.
  4. 4. CRM Record Generation: Qualified deal record and contact entry created in CRM with high-intent flag.
  5. 5. Calendar Dispatch: Viewing invitation sent to buyer and assigned broker with automated confirmation.

Demonstration Status

Prototype Status: Architecture and data flows validated in staging. Simulated property listings and test numbers used for evaluation.

WhatsApp Business Platform Python 3.11 FastAPI Google Gemini 1.5 HubSpot API PostgreSQL
AI Real Estate Lead Assistant Workflow Blueprint
DEMONSTRATION // 02

Intelligent Customer Support System

Industry: Multi-Channel E-Commerce & Freight Logistics

Business Challenge

Customer support queues are frequently clogged with repetitive queries regarding order tracking, customs clearance, and return policies, inflating resolution times for high-priority operational exceptions.

Solution Overview

A multi-tier customer inquiry engine incorporating Retrieval-Augmented Generation (RAG). The system classifies intent and urgency, retrieves verified standard operating procedures, resolves routine inquiries autonomously, and compiles rich context dossiers for complex human escalation.

Workflow Breakdown

  1. 1. Ingestion: Customer ticket received via email or webchat portal.
  2. 2. Intent & Urgency Classification: FastAPI service tags ticket category and SLA urgency score.
  3. 3. Vector Knowledge Retrieval: Semantic search in pgvector retrieves exact corporate policy clauses.
  4. 4. Grounded Resolution: Agent crafts response citing verified policy; automatically closes routine ticket.
  5. 5. Exception Escalation: High-urgency or sensitive issues route directly to human tier-2 with pre-fetched order data.

Demonstration Status

Prototype Status: End-to-end vector retrieval pipeline operational in sandbox with mock logistics SOPs and ticket data.

n8n Orchestration PostgreSQL (pgvector) OpenAI Text-Embedding-3 Zendesk / Freshdesk Webhooks Docker
Intelligent Multi-Tier Customer Support System Blueprint
DEMONSTRATION // 03

Automated Business Reporting Assistant

Industry: Multi-Branch Retail & Corporate Holding Groups

Business Challenge

Leadership teams often wait until weekly meetings to review performance metrics because stitching together POS transactions, advertising spend, inventory balances, and bank accounts requires extensive manual analyst hours.

Solution Overview

An automated cron ETL pipeline executing daily at 07:00 AM (Local Time). The service pulls raw operational telemetry from payment processors, ERPs, and ad networks, runs statistical anomaly detection, and compiles an executive digest delivered directly to executive WhatsApp and encrypted email.

Workflow Breakdown

  1. 1. Scheduled Trigger: Linux cron initiates Python ETL pipeline at 07:00 AM local time.
  2. 2. Multi-Source Ingestion: API workers pull previous-day figures from Stripe, POS, Meta Ads, and warehouse DB.
  3. 3. Mathematical Normalization: Raw figures aggregated to calculate gross margin, blended ROAS, and inventory velocity.
  4. 4. Anomaly Detection: Script flags metrics that deviate by more than 2.0 standard deviations from 30-day moving average.
  5. 5. Multi-Channel Dispatch: Executive summary sent to CEO WhatsApp channel; comprehensive PDF dossier emailed to finance team.

Demonstration Status

Prototype Status: Functional Python ETL harness running with synthetic multi-branch commercial datasets.

Python ETL PostgreSQL / Supabase FastAPI ReportLab PDF Engine WhatsApp Cloud API
Automated Business Reporting Assistant Blueprint
Integrity & Transparency Standard

Our Demonstration Policy

ZUNEX Technologies maintains a strict code of integrity. The prototypes displayed above are engineered working models operating on simulated datasets to demonstrate system architecture, latency, and integration flows. We never invent fictitious client case studies, falsified metrics, or unverified testimonials.

Have a Specific Operational Workflow in Mind?

We can architect and deploy an isolated proof-of-concept prototype tailored to your exact software ecosystem before full-scale deployment.

Discuss a Custom Prototype