Our Projects
Here you can find an overview of our projects
Explore a selection of real-world projects we've delivered — from fast AI prototypes to scalable enterprise solutions. Each case showcases how we turn innovation into impact.
Select a section to open the full description.
Baby-Care – The Smart Family Assistant
With Baby-Care, Omni AI Solution GmbH is developing an intelligent, AI-based family assistant designed to support parents with fast, personalized, and easy-to-understand guidance in daily life. The goal is to reduce complexity for families in typical everyday situations by providing relevant information tailored to both the child and the parents – digitally, flexibly, and in a practical way.
The product combines three core capabilities in one solution: a personalized GenAI chat for family-related questions, a local explorer for child-relevant places and current nearby activities, and a smart product recommendation feature with carefully preselected, tailored suggestions. The result is a digital assistant that brings together guidance, orientation, and concrete next steps in one consistent user experience.
Key Outcomes at a Glance
- Personalized support for parents – tailored to child, age, and situation
- Combines guidance, local discovery, and product recommendations in one app
- Faster decision-making in family life through clear and direct next-step suggestions
- Higher user value through the combination of GenAI, local context, and individual relevance
- Scalable digital product foundation for future B2C and B2B use cases
Differentiation from Existing Solutions
| Feature | Typical Standalone Apps | Baby-Care |
|---|---|---|
| Personalized family guidance | ✗ Often generic or limited to single topics | ✓ AI-based, individualized, and context-aware |
| Local child-related places & activities | ✗ Often fragmented, hard to navigate, or not family-focused | ✓ Structured, local, and relevant for families |
| Product recommendations for children & parents | ✗ Often unspecific or purely catalog-based | ✓ Preselected, personalized, and need-oriented |
| Integrated user experience | ✗ Guidance, discovery, and recommendations are usually separated | ✓ All-in-one smart assistance experience |
Capacity & Worker Planning for Gastronomy
Modern gastronomy requires more than guesswork and static shift plans. In this project, we developed an AI-based solution that automates staff planning by translating real-time demand data into optimal shift schedules — ensuring the right number of employees are scheduled at the right time.
Results at a Glance
- Up to 15% reduction in overstaffing
- Better compliance with labor laws (e.g., break time rules)
- Real-time adjustment based on POS & booking data
- Less manual work for managers, faster shift approval
- Increased employee satisfaction through fair and predictable schedules
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Demand forecast from POS/bookings | ✗ Manual estimation | ✓ Automated, real-time |
| Break scheduling compliance | ✗ Often overlooked | ✓ Fully integrated |
| Shift plan generation | ✗ Manual or rigid | ✓ Dynamic & constraint-based |
| Reduction in overstaffing | ✗ Up to 0% | ✓ Up to −15% cost savings |
Efficient Product-Portfolio Optimization in Gastronomy
Many cafés and restaurants make menu decisions based on intuition. In this project, we introduced a powerful data-driven solution: a real-time Effort-Margin Diagram that visualizes every product's contribution to profit vs. operational cost.
Results at a Glance
- Up to 5–10% increase in overall menu profitability
- Identification of unprofitable, high-effort items for removal or revision
- Discovery of “hidden gems” with low effort and high margin for promotion
- Automated alerts on margin shifts due to supplier or labor cost changes
- Continuous optimization based on live POS and kitchen data
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Menu optimization approach | ✗ Static spreadsheets | ✓ Dynamic visual framework |
| Visualization of margin vs. effort | ✗ Not available | ✓ 2D diagram with real-time updates |
| Integration with POS & cost data | ✗ Manual input only | ✓ Full automation + live recalculation |
| Alert system for performance drift | ✗ Missing | ✓ Smart notifications for key metrics |
| Impact on profitability | ✗ Limited | ✓ +5–10% average gain |
Customer Experience & Feedback Analytics
Delivering consistent guest satisfaction is a daily challenge in the hospitality industry. In this project, we implemented a fully automated feedback intelligence solution: from scraping multi-channel reviews to triggering guest recovery workflows — all in real time.
Results at a Glance
- Improved average online rating by +0.3 stars in 3 months
- +5% increase in guest loyalty through targeted follow-ups
- Real-time alerts for high-risk reviews (e.g., allergies, hygiene)
- Clear team feedback loops: action reports for kitchen & service
- Staff leaderboard based on real guest feedback
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Feedback monitoring | ✗ Manual check 1× week | ✓ Continuous multi-channel scraping |
| Sentiment analytics | ✗ Not available | ✓ NLP-based topic & tone analysis |
| Guest recovery | ✗ Not automated | ✓ Trigger-based apology & voucher flow |
| Staff-specific insights | ✗ No feedback linkage | ✓ Shift-level performance leaderboard |
| Kitchen or service improvement loops | ✗ Informal / not tracked | ✓ Action reports by topic & dish |
| Impact on online reputation & loyalty | ✗ Passive, reactive | ✓ +0.3 stars, +5% repeat visits |
AI-Driven Traffic Flow Analysis & Prediction
Managing urban traffic efficiently requires more than just monitoring—it demands predictive intelligence at scale. In this project, we developed a global mobility analytics platform that ingests billions of vehicle data points across continents. Using AI-based pattern recognition, predictive modeling, and big data processing, we deliver real-time traffic insights and future forecasts to city planners, infrastructure providers, and navigation systems.
Results at a Glance
- Up to 30% reduction in congestion hotspots via dynamic traffic routing
- +40% increase in forecast accuracy compared to baseline models
- Scalable analytics across 100+ countries and road types
- Instant anomaly detection (e.g., road closures, mass events)
- Smart city integrations for adaptive traffic light control
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Data scope | ✗ Local & fragmented | ✓ Global-scale, billions of data points |
| Prediction quality | ✗ Based on past patterns | ✓ AI-based future state forecasting |
| Real-time anomaly detection | ✗ Manual incident reports | ✓ Instant alerts from live data streams |
| Smart city integration | ✗ Static signal control | ✓ Adaptive light & lane regulation |
| Geographic scalability | ✗ Limited to few cities | ✓ Cloud-native, global deployment ready |
| Data fusion (GPS, IoT, camera, etc.) | ✗ Often siloed | ✓ Unified multimodal data pipeline |
AI-Powered Traffic Jam Detection via Camera Analytics
Real-time traffic monitoring requires more than just road sensors—it needs eyes on the road and intelligence in the cloud. In this project, we deployed a scalable AI solution that uses computer vision to detect, classify, and track vehicle flows from existing street cameras. Our system analyzes congestion patterns, predicts upcoming traffic jams, and supports urban mobility decisions with accurate, camera-based data.
Results at a Glance
- 90%+ detection accuracy for traffic slowdowns and standstills
- +35% improvement in congestion response time for city operators
- Live classification of vehicle types (cars, trucks, buses, etc.)
- Predictive alerts up to 15 minutes in advance of major jams
- Reduced reliance on expensive sensor infrastructure
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Hardware dependency | ✗ Dedicated sensors needed | ✓ Leverages existing street camera feeds |
| Jam detection granularity | ✗ Speed threshold-based | ✓ Visual analysis of density & flow |
| Vehicle classification | ✗ Not available | ✓ Real-time object detection via AI |
| Predictive capabilities | ✗ Static models | ✓ Adaptive AI with short-term forecasting |
| Infrastructure integration | ✗ Complex installation | ✓ Plug-and-play with camera networks |
| Cost efficiency | ✗ High capex requirements | ✓ Software-first, scalable model |
Driving Behavior Analytics for Autonomous Turns (ADAS & AVs)
Precision in vehicle maneuvering—especially in critical scenarios like left and right turns—is vital for autonomous driving systems and ADAS calibration. In this project, we developed a high-resolution behavior analysis platform that statistically captures, clusters, and benchmarks turning behavior from thousands of autonomous and ADAS-equipped vehicles.
Leveraging advanced trajectory tracking, edge-based telemetry fusion, and behavior modeling, our system supports safer AI driving decisions and OEM performance evaluation.
Results at a Glance
- Analyzed over 10 thousand turn maneuvers from AVs and ADAS
- Identified 15% of left turns with elevated safety risk (e.g., occlusion, speed variance)
- +25% improvement in prediction models for turning intent recognition
- Enabled real-world benchmarking of OEM-specific ADAS turning patterns
- Delivered trajectory clustering & visualization in real-time dashboards
Smart Grid Optimization through Bidirectional EV Charging
Electric vehicles are evolving from passive consumers to active energy participants. In this project, we implemented an AI-driven bidirectional charging system where EVs dynamically feed energy back into the grid.
By integrating real-time grid signals, vehicle state-of-charge, and usage forecasts, our platform enables demand balancing, peak shaving, and decentralized storage—crucial components for the next-generation energy grid.
Results at a Glance
- Enabled up to 12% local peak shaving during high-demand periods
- Improved grid stability KPIs by 18% in test regions
- Dynamic charge/discharge scheduling via predictive energy demand modeling
- Full integration with ISO-compliant smart meters & grid protocols
- Empowered EV owners to earn energy credits via grid participation
Smart Family 360 – AI-Powered Relationship Support with Real Impact
Smart Family 360 is a digital assistant designed to support individuals, couples, and families through emotionally challenging phases.
Outcome-Driven
Using intelligent conversation flows and adaptive surveys, the assistant analyzes emotional patterns and relationship dynamics. The aggregated insights lead to a personalized action plan with clear, practical recommendations for improving communication, strengthening trust, and increasing emotional well-being.
Tangible Results
- Increased user clarity and satisfaction
- Early prevention of relationship crises
- Scalable support tool for professionals, institutions, and municipalities
Built on proven systemic principles (inspired by ISYS BW and DGSF) and fully compliant with GDPR, AI Act, and ethical AI standards.
Influencer Sentiment & Topic Intelligence via NLP
Influencers shape opinions, trends, and brand perception—but tracking their evolving narratives at scale requires more than just keyword monitoring. In this project, we built an AI-powered sentiment and topic modeling engine that continuously analyzes influencer content across YouTube, Instagram, TikTok, and X. Using advanced NLP pipelines, the system detects emotional tone, emerging themes, and audience reactions—empowering brands and analysts with real-time influencer intelligence.
Results at a Glance
- Analyzed over 2 million influencer posts and videos across 5 platforms
- Identified micro-trend shifts within hours of publication
- +38% increase in brand-influencer alignment accuracy
- Clustering of content into 10+ dynamic sentiment-topic maps
- Real-time alerts on reputation risks or viral momentum
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Sentiment analysis | ✗ Based on static word lists | ✓ Transformer-based contextual emotion models |
| Topic detection | ✗ Manual tagging | ✓ Dynamic topic modeling (LDA/BERT embeddings) |
| Platform coverage | ✗ Limited to 1–2 sources | ✓ Cross-platform (YouTube, IG, TikTok, X, etc.) |
| Trend detection | ✗ Delayed or reactive | ✓ Near real-time with notification triggers |
| Visual insights | ✗ Basic charts | ✓ Sentiment-topic heatmaps and cluster graphs |
| Brand relevance scoring | ✗ Manual mapping | ✓ AI-driven influencer-brand fit suggestions |
AI-Powered Baby Care Product Review Analyzer
Parents rely heavily on product reviews when choosing the right baby care items. In this project, we developed an AI-based sentiment classification system that automatically analyzes user-generated content (UGC) from e-commerce platforms and forums. Leveraging Natural Language Processing (NLP), the system distinguishes between positive, neutral, and negative sentiments and provides product-level insights in real time.
Results at a Glance
- Classified over 500,000 reviews across 150+ baby care products
- Increased consumer trust through AI-backed product transparency
- Enabled dynamic sentiment-weighted product scoring
- Surfaced topic-level concern clusters for quality, usability, and safety
- Delivered ready-to-use dashboards for marketing and product teams
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Sentiment scoring | ✗ Simple averages only | ✓ Dynamic sentiment-weighted scoring |
| Topic-level insights | ✗ Not available | ✓ Clustering by concern and feature |
| Real-time monitoring | ✗ Delayed or batch-based | ✓ Instant insights with continuous updates |
| Actionable outputs for teams | ✗ Raw data only | ✓ Ready-to-use dashboards for marketing |
AI-Driven Knowledge Retention for Teams
Losing critical know-how during employee transitions is a major risk for any organization. To solve this, we developed an AI-powered system that extracts, structures, and preserves departmental knowledge from diverse formats like PDF, Word, PowerPoint, and text—supporting both seamless onboarding and efficient offboarding.
Results at a Glance
- Reduced onboarding time by -40% through structured knowledge access
- Preserved 90%+ of critical know-how during employee offboarding
- Unified documentation from 5+ file formats into searchable knowledge graphs
- Enabled AI-powered Q&A for new joiners to query legacy content
- Department-specific dashboards for knowledge gaps & coverage insights
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| Knowledge extraction from mixed files | ✗ Manual / fragmented | ✓ Automated multi-format ingestion |
| Searchability of legacy knowledge | ✗ Folder-based retrieval | ✓ Semantic and graph-based access |
| Onboarding support | ✗ Static handover docs | ✓ AI-powered Q&A and guided access |
| Coverage & gap visibility | ✗ Not transparent | ✓ Dashboard with coverage & freshness KPIs |
| Query response for new employees | ✗ Human-dependent | ✓ Conversational AI interface |
Voice-Triggered Intelligence for Embedded & Edge Devices
To enable seamless user interaction with smart devices, we developed an audio keyword spotting system tailored for embedded and edge environments. Using advanced signal processing and AI models, our solution detects predefined voice commands locally, then securely triggers cloud-based workflows — delivering low-latency performance with minimal energy consumption.
Results at a Glance
- Achieved >95% keyword detection accuracy on low-power edge chips
- Reduced cloud load by up to 70% through on-device pre-filtering
- Wake-word latency below 150 ms for instant user engagement
- Enabled privacy-first voice UX with offline processing
- Scalable deployment across IoT, wearables & automotive platforms
Differentiation from Existing Solutions
| Feature | Legacy Tools | Our Solution |
|---|---|---|
| On-device keyword spotting | ✗ Not supported | ✓ Lightweight edge inference |
| Wake-word latency | ✗ >300 ms | ✓ ~150 ms response time |
| Cloud dependency | ✗ Always-on cloud streaming | ✓ Local activation, cloud only when needed |
| Energy efficiency | ✗ High resource usage | ✓ Optimized for low-power chips |
| Privacy model | ✗ Audio sent externally | ✓ Offline-first signal processing |