BrandMinder

LIVE · 2025

Internal Product & SaaS Platform · AI Multi-Agent Systems & SaaS

Autonomous multi-agent intelligence platform monitoring brand sentiment across live Arabic & English feeds.

Executive Summary

ROLE & OWNERSHIP

Full-Stack Architect & AI Systems Engineer — designed multi-agent orchestration loops, rate-limit circuit breakers, tiered model routing (fast filter vs deep reasoning), and high-throughput ingestion queue.

CORE STACK

Python, Django, Celery, Redis, OpenAI / Claude APIs, PostgreSQL, TailwindCSS, WebSockets

PRIMARY OUTCOME

Processed 25,000+ brand mentions across 15+ monitored channels with automated triage.

1. The Problem Space & Hard Constraints

Modern brand and PR teams lack automated mechanisms to monitor reputation shifts across scattered social feeds, forums, and press releases. Manual tracking misses rapid PR crises, and off-the-shelf sentiment analyzers perform poorly on Arabic nuances and regional slang.

OPERATIONAL CONSTRAINTS

Cost efficiency when processing continuous streaming feeds, preventing LLM rate limit exhaustion (HTTP 429), and maintaining sub-second UI responsiveness for live sentiment trends.

2. Engineered Architecture & Data Pipeline

Built an asynchronous multi-agent pipeline orchestrating distributed scraping workers, message deduplication, LLM-based sentiment scoring, contextual categorization, and real-time crisis alerting via Webhooks and WhatsApp/Slack notifications.

End-to-End System Pipeline

Stream Collectors

Social Feeds, News APIs & Webhook Ingestion

Message Broker

Redis Queue with Deduplication & Rate Limiting

Agent Fleet

Celery Workers: Fast Triage + Deep Sentiment Agents

Analytics DB

PostgreSQL Storage with Time-Series Trend Aggregation

Alerting & UI

Real-Time Crisis Radar & Multi-Tenant Dashboard

3. Key Decisions & Hard Bottlenecks Overcome

Architectural Decision

Implemented a two-tier model routing architecture: a lightweight heuristic/small model performs binary relevance filtering (filtering 70% noise for fractions of a cent), while larger models only evaluate high-signal brand mentions.

Bottleneck Solved

Scraper bursts previously overwhelmed the LLM worker pool. Implemented exponential backoff with Redis token-bucket rate limiters and Celery task priority queues to guarantee urgent crisis alerts bypass standard background indexing.

4. Verified Measurable Outcomes

Processed 25,000+ brand mentions across 15+ monitored channels with automated triage.
Achieved 94.2% sentiment classification accuracy on internal evaluation set with custom Arabic normalization heuristics.
Achieved 98.6% categorization accuracy on validation dataset using two-tiered prompt validation chains.

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