AI Solutions & Case Studies
Real Results from Real Businesses
Explore how companies across industries are leveraging AI to automate operations, boost conversion rates, and scale their business with measurable results.
Challenge
Respond to high volume of inquiries on WhatsApp, Instagram, and Facebook without delays, provide accurate quotes, and reduce friction between diagnosis, coordination, and closing.
Solution Approach
- Omnichannel assistant with RAG trained on client's know-how
- Preliminary diagnosis based on symptoms described by user
- Automatic quotes and referral to human advisor with structured summary
- Dashboard with metrics + mini-CRM for tracking
Implementation Details
Channels: WhatsApp Business API, Instagram Graph, Facebook Messenger
Stack: LLMs + RAG (technical documentation, prices and policies), n8n/Workers for orchestration, FastAPI, Postgres (CRM), Qdrant/PGVector for embeddings
Results
(averages from recent implementations)
Response Time Reduction
Query-to-Visit Conversion
Queries Resolved Without Human Intervention
Average Ticket Value Increase
"We went from responding at night to having answers in seconds. The assistant creates the quote and the salesperson just closes. Visits grew more than double."
Challenge
Respond to comments and DMs with video content, maintaining the owner's brand voice and publishing quickly.
Solution Approach
- Script generated with RAG (business FAQs and know-how)
- Cloned voice synthesis (ElevenLabs) and client avatar (HeyGen)
- Approval on a dashboard and publication to client's account
Implementation Details
Channels: TikTok (comment reading), ElevenLabs (cloned TTS), HeyGen (avatar), web dashboard (Next.js) with review queue
Stack: Integrations with TikTok, ElevenLabs, HeyGen. Controls: whitelist responses, script styles, duration limits, 1-click human review
Results
(latest comparable implementations)
Time from Comment to Publication
Average Video Retention
More Responses/DMs from Personalized Videos
"Instead of the comment getting old, we respond with video before the competition. The voice and avatar are identical to the owner."
Challenge
Monitor hundreds of tickers and detect, in real-time, those with the highest probability of sharp movement, considering news, filings, and market metrics.
Solution Approach
- Data pipeline: news sentiment, SEC filings, and price/volume stream
- Spike score model with technical features + events (8-K/6-K, ATMs, delisting pressure, etc.)
- Panel with ranking and configurable alerts
Implementation Details
Channels: Polygon (real-time), SEC-API (filings), YFinance/Finnhub (supplement)
Stack: Python (ingestion and feature store), Postgres/Timescale-like, Celery/n8n schedulers, internal APIs, dashboard (Next.js + Recharts)
Results
(in recent deployments)
Tickers Monitored in Parallel
Daily Manual Screening Time Reduction
Operational Hit-Rate vs Manual Watchlists
"The system alerts us to what's relevant and saves us hours of scrolling through news and filings. Score prioritization improved entry discipline."
Challenge
Respond quickly to interested parties, filter by requirements, send relevant options, and coordinate visits with the right salesperson without losing context.
Solution Approach
- WhatsApp assistant that captures requirements (area, budget, rooms, financing)
- Matching with available properties and sending property sheets
- Visit coordination: proposes days/times, validates availability, and refers to assigned salesperson
Implementation Details
Channels: WhatsApp Business API, property and salesperson database (Postgres)
Stack: RAG with real estate documentation (appraisals, requirements, guarantees, commissions, processes)
Results
(comparable implementations)
First Response Time
Inquiry-to-Visit Scheduled Ratio
Fewer No-Shows
"Potential clients get immediate responses and scheduled visits. The assistant handles everything until the agent takes over with all the information."
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