ShooflyAI | Case Study:

Vigilant – In‑App RAG Safety Assistant

Live safety intelligence from 47 official sources across 16 countries, refreshed hourly, delivered instantly to users in the field.

0%
faster access to personalized safety intelligence vs static content search
0+
analyst hours saved monthly on manual safety content curation
0
official sources across 16 countries, auto‑refreshed every hour
0%+
accuracy in RAG‑sourced safety guidance with source citations

Users in the field get instant, cited answers on travel safety, health risks, and security alerts. No hunting through PDFs. No waiting for analyst support. Intelligence infrastructure that scales to any geography without analyst headcount.

Vigilant - Stay Safe. Stay Vigilant.

Safety Intelligence Was Scattered. Users Were Hunting. Analysts Were Bottlenecked.

Before: User question → Search State Dept site → Check CDC → Download PDF → Still incomplete → Email analyst → Wait → Get answer (maybe outdated)

An In‑App Safety Assistant Built on Live Intelligence Infrastructure.

Three layers that turn scattered safety data into instant, country‑specific, cited guidance for users in the field.

Data Ingestion Layer

  • 47 official sourcesAutomated web scraping system that pulls content from State Department, CDC, meteorological agencies, and security databases—keeping safety intelligence fresh without manual analyst curation. refreshed every hour—State Department advisories, CDC health alerts, weather data, security intelligence across 16 countries.
  • 100% success rate on all production sources with 9.6 second full refresh cycles at optimal concurrency.
  • Automated scraping with zero manual curation required. New advisories appear in the assistant within one hour of publication.
  • Production‑ready: 0% ingestion errors, sub‑400MB memory footprint, enterprise‑grade reliability certified through comprehensive QA.
Why this matters

Manual analyst curation becomes automated infrastructure. Adding new countries or sources is configuration, not headcount. Travel advisories update hourly, not weekly. Users get current intelligence, analysts shift to high‑value risk analysis.

RAGRetrieval Augmented Generation: an AI approach that retrieves relevant information from a knowledge base first, then generates answers grounded in that specific content—ensuring accuracy and enabling source citations. Intelligence Layer

  • Semantic search across curated safety intelligence—finds relevant advisories even when users ask in natural language.
  • Advanced language model generates answers strictly grounded in official sources: no hallucinations, no generic advice.
  • Filters by country, region, and topic (security, health, weather, mobility) so guidance is geographically precise and contextually relevant.
  • Every answer includes source citations (State Dept, CDC, etc.) to build trust and enable verification.
Why this matters

Users get accurate, cited safety guidance from official sources, not generic ChatGPT responses. Reduces liability. Builds trust. Analysts curate once (by vetting sources), intelligence compounds infinitely.

In‑App Experience

  • Embedded directly in Vigilant's mobile app—users access safety intelligence without leaving their workflow or opening external sites.
  • Seamless authentication via mobile app login—no separate chatbot login, no extra friction.
  • Source citations visible in every answer (State Dept, CDC, etc.) to build trust and enable users to verify guidance.
  • Analytics tracking query volume, geographic trends, and topic patterns to identify content gaps and user needs.
Why this matters

Users stay in‑app. Zero context switching to external sites. The assistant becomes part of the safety workflow, not a separate research tool. Drives adoption, increases engagement, strengthens product stickiness.

Built Like a Platform, Not a One‑Off Feature.

A two‑phase approach: start tactical, prove value, then scale.

1

Phase 1 – Data Foundation and QA

Web scraper deployed and battle‑tested across functional, performance, API integration, reliability, edge cases, and monitoring dimensions.

Technical

0% ingestion errors, minimal memory footprint, sub‑10 second refresh cycles. Production‑ready certification achieved.

Business Impact

Data pipeline that scales to 50+ sources without breaking. Foundation for all future intelligence features.

2

Phase 2 – In‑App RAG Assistant

RAG backend integrated with vector database and language model. Seamless authentication from mobile app to chat interface. Full system delivered and deployed in under 30 days from kickoff.

Technical

Vector database with semantic search, AI generation grounded in official sources, JWT authentication from mobile app to chat UI.

Business Impact

Users get instant, cited safety answers in‑app. Analysts freed from manual content updates and repetitive user questions. Fixed platform cost, scaled intelligence delivery.

What Changed Once It Went Live.

65%

Faster Access to Critical Safety Intelligence

Users get country‑specific safety guidance 65% faster than manual search across government sites and static PDFs.

Less time hunting for advisories. More time making informed decisions in the field.

What this means economically

Faster intelligence access = better risk decisions = safer operations. Users stay engaged in‑app instead of leaving to search external sites. Drives retention and product stickiness.

20+

Analyst Hours Reclaimed Monthly

Safety analysts freed from manual content curation and fielding repetitive "what's the advisory for X country?" questions.

Those 20+ hours shift to strategic intelligence analysis, client-specific risk assessments, and proactive threat monitoring.

What this means economically

Manual curation was variable cost scaling with countries covered. Automated scraping is fixed infrastructure. Launch in 10 new countries? Same platform cost, zero marginal analyst time.

90%+

Answer Accuracy With Source Attribution

90%+ accuracy in side‑by‑side analyst review. Every answer cites official sources (State Dept, CDC, CIA Factbook, local agencies).

Trust drives usage. Attribution reduces liability. Precision matters when lives are on the line.

What this means economically

Users trust the system because answers are verifiable. Reduces compliance risk. No bad safety advice liability. System defensibility through cited, official sources.

Why This Matters at the Platform Level.

📈

Margin Expansion Through Intelligence Automation

Analysts manually curating safety content for each geography = variable cost that scales linearly. Automated scraping + RAG = fixed platform cost that scales to any number of countries.

Launch coverage in 10 new countries? Same infrastructure, zero proportional analyst cost increase. Variable labor becomes fixed technology leverage.

🔒

Product Differentiation Through Owned Intelligence

In‑app AI safety guidance, grounded in Vigilant's curated mix of official sources and proprietary alert feeds, is hard to replicate. Generic travel apps cannot compete on precision, freshness, or trust.

The intelligence layer compounds: more usage improves retrieval patterns, better answers drive engagement, geographic expansion leverages the same backbone. This is a moat, not a feature.

🚀

Platform Foundation for Future Intelligence Products

Same data ingestion and RAG infrastructure powers multiple future capabilities: incident triage, client‑specific safety SOPs, premium intelligence tiers, proactive risk alerts pushed to users.

One build. Multiple monetization levers. Solve the intelligence problem once, then stack products that drive pricing power and market expansion. That's platform thinking.

From Case Study to Operating System.

  • Expand geographic coverage using the same scraper framework. Add 20 more countries with zero platform rebuild. Same ingestion backbone, broader safety intelligence reach.
  • Layer client‑specific safety protocols and proprietary alert feeds into the RAG stack. Turn generic advisories into custom intelligence that enterprise clients pay premium for.
  • Package the assistant as a premium tier or standalone product. Users already see the value. Existing infrastructure becomes new revenue stream with minimal incremental cost.
This is not just "adding AI to the app." It is upgrading the core intelligence layer in a way that compounds over 3–5 years. Same capability that delivers safety guidance can power incident response, compliance workflows, and client‑specific intelligence products. Own the intelligence layer, do not rent it.