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RivalScope

Inspiration and Problem

RivalScope was built to address a real and recurring pain point: competitive intelligence is broken. Businesses that want to track their competitors have to juggle a dozen different tools, manually scan news sites, scroll through Reddit threads, monitor review platforms, and hope they don't miss anything important. Most available solutions are too narrow — they cover SEO, or social listening, or analytics, but rarely all of it together.

RivalScope brings these capabilities under one roof. It is an AI-powered competitive intelligence platform that automatically discovers competitors, tracks market activity in real time, analyzes public sentiment, and turns all of that raw information into actionable business recommendations. The goal was simple: give any business the kind of market awareness that previously required a dedicated research team.

Tech Stack

Frontend

Next.js
React.js
Tailwind CSS
Recharts

Backend

Node.js
Server-side APIs
Background job schedulers

AI and Intelligence Layer

Large Language Models (LLMs)
AI-powered competitor discovery
Sentiment analysis systems
AI-generated strategic recommendations

Data and Integrations

Web search integrations
Website content extraction
Automated competitor monitoring
Real-time analytics pipelines

How We Used MeDo

MeDo was not just a code generator throughout this project — it was a collaborative engineering partner from the very beginning. We used it across every major phase of development, including:

Product ideation and scoping
System architecture planning
Backend and database structure design
Prompt engineering
Dashboard and UX planning
Debugging and feature refinement
Structuring scalable AI workflows

Rather than firing off isolated one-off prompts, we treated MeDo as a running technical collaborator — someone to think through problems with, challenge assumptions, and iterate alongside us at every stage.

How We Structured Conversations with MeDo

We deliberately broke the project into focused modules and worked through them one at a time. This kept each conversation meaningful and contextually grounded, rather than trying to juggle the entire system at once.

The main modules we worked through were:

Onboarding and business analysis
AI competitor discovery
Real-time competitor insights
Portfolio intelligence dashboards
Public sentiment analysis
AI recommendation systems

This modular approach made it significantly easier to iterate quickly on complex features without losing direction or project context.

Best Feature MeDo Helped Build

The standout feature that MeDo had the biggest hand in was the Public Sentiment Analyzer.

This system collects and analyzes brand-related content from across the internet, including:

Reddit
Twitter/X
LinkedIn
Blogs and forums
Review platforms
News websites

From that raw data, the AI generates:

Sentiment scores
Strategic summaries
Key discussion themes
Risk analysis
Actionable recommendations
Content strategy suggestions

This single feature shifted RivalScope from being a simple competitor tracker into a genuine market intelligence platform — one that does not just surface information, but tells you what to do with it.

Plugins and API Integrations

Several integrations and AI-powered workflows were built into the platform:

Web search systems for automated competitor discovery
Website content extraction for business analysis
AI-powered summarization and recommendations
Background schedulers for hourly insight updates
Automated email alert systems

One of the more impressive workflows was using AI with live web search to discover and analyze competitors directly from a business's own website — no manual input required.

Challenges We Faced

The hardest problem to solve was making AI outputs reliable at scale. RivalScope depends heavily on structured, AI-generated JSON responses. When those responses are malformed or inconsistent, the whole pipeline breaks. We had to build out:

JSON validation layers
Retry mechanisms
Error handling pipelines
Fallback recovery systems

Beyond that, we ran into real complexity around real-time data aggregation, processing noisy and unstructured internet data, managing dashboard complexity, and keeping background scheduling systems stable under load.

The honest takeaway: building stable AI systems is less about making the AI smarter and more about building enough infrastructure around it that it cannot quietly fail without anyone noticing.

What We Learned

Building RivalScope was a crash course in several things that do not show up in tutorials:

How to design scalable AI systems that hold up under real workloads
Prompt engineering that produces consistent, structured outputs
Real-time data processing at a meaningful scale
How to build AI-assisted development workflows that actually save time
Handling unstructured internet data without losing your mind
Designing dashboards that make AI outputs feel useful rather than overwhelming

The most important lesson of all: AI products become genuinely valuable when they reduce the burden of decision-making, not when they simply generate more information for someone to sift through.