Purpose
AI Superior is an AI development and consulting company focused on building custom AI software for enterprises, growth-stage companies, and nonprofit organizations, advancing step by step from proof of concept to production systems that teams can rely on, centered on clients' business problems and data. The company was founded by AI researchers in 2019, earlier than the generative AI boom.
Main Capabilities
The company provides end-to-end AI services from strategy to production, including:
- AI Consulting and Strategy: Identify use cases worth investing in, check data readiness, and provide an approvable roadmap.
- Custom AI Software Development: Production-grade AI applications and components, integrated into existing systems.
- Generative AI and LLM Development: Private LLMs, retrieval-augmented generation (RAG), and fine-tuned models, running on clients' own data.
- AI Chatbot Development: Assistants for customers and employees that answer from a knowledge base and cite sources.
- Computer Vision: Detection, segmentation, and inspection in images and video, from laboratory precision to the shop floor.
- Predictive Analytics and Machine Learning: Forecasting, scoring, and anomaly detection models that turn historical data into decisions.
- AI Research and Development: Cutting-edge research, new algorithms, and patent support to solve problems for which no off-the-shelf solution exists.
- Team AI Training: Workshops and projects that provide engineers and managers with hands-on AI skills.
How to Use
After a client submits a project request, the company replies by email and then arranges a call with an AI expert to understand the problem, data, and goals, and provides a clear recommended approach and a high-level estimate.
Projects follow a phased lifecycle, with a reviewable result at the end of each phase, so clients can decide whether to continue after seeing that the previous phase worked:
01 Discovery: Work through the business problem with the client's team and define the direction for solving it. Output: scope, approach, and a high-level estimate of effort and expected results, for a go/no-go decision.
02 Data and Feasibility: Understand the team and the data, and determine whether AI is suitable for the problem. Output: a data assessment and a clear feasibility conclusion, for a go/no-go decision before starting to build.
03 Proof of Concept / MVP: Test the approach at small scale using existing data. Output: measured results on the client's own data and the basis for an investment decision, for a go/no-go decision.
04 Integration and Scaling: Integrate the approach into existing systems, fine-tune the model, and adjust as needed. Output: within the client's processesRun the plan, ensure compatibility with data and systems, and make a go/no-go decision.
05 Evaluation: Review the results together with the client to ensure correct interpretation. Deliverables: project deliverables and directions for subsequent improvement.
Applicable Scenarios
The company's projects cover 13 industries, and past cases include pharmaceuticals, insurance, aerospace, and nonprofit organizations, among others. Specific client cases:
- Conducted drug detection and counting for a medical technology provider, achieving 99.9% precision, recall, and F1 for all predictions.
- Developed a custom LLM chatbot with document citations for a consulting company, using its own LLM without relying on third-party LLM services, with complete data control.
- Provided driving scores based on connected vehicle data for usage-based insurance, using deep learning models to analyze driver phone telemetry data, detect driving behavior, score each trip, and calculate personalized discounts and safe driving recommendations.
- Conducted roof detection and segmentation for a solar company, using deep learning models to detect residential roofs in satellite images, segment them into different planes, and estimate area and dimensions to efficiently plan solar panel installation.
- Conducted map segmentation for urban area pricing for a real estate platform, mapping 5 land use categories pixel by pixel, comparing with historical maps and tracking changes in vacant land to support urban area price assessment.
Limitations
The company is a German company operating under GDPR, with offices in Frankfurt Rhine-Main and Berlin, and can remotely deliver projects for clients in the United States, the United Kingdom, Poland, Serbia, and Saudi Arabia, among other locations. When data cannot leave the organization, the company builds solutions based on privately self-hosted models rather than third-party AI services. Project time and cost depend on problem complexity, available data, deployment environment, and customization requirements, and a phased approach avoids large upfront budget commitments. Clients on Clutch have reported that proof of concept was completed within four weeks. If existing data is insufficient, the company will clearly inform the client and propose options such as data collection, annotation, or alternative approaches.
