01
Problem
We have data but don't know how to use it.
Solution
We start with a data readiness audit, identify gaps, and recommend high-impact AI use cases before building a proof of concept.
AI Development Company for Custom AI Software Solutions
Build custom AI software, integrate LLMs, and automate workflows with Unisam. We deliver AI development services that turn data into actionable systems — from machine learning models to production-ready AI products across Switzerland, Austria, Poland, and Italy.
What We Deliver as an AI Development Company
Unisam builds custom AI software for businesses that want to automate decisions, extract insights from data, and integrate artificial intelligence into existing products. Our AI software development services cover the full lifecycle: data preparation, model development, LLM integration, deployment, and ongoing optimization.
Our AI development services are available internationally, including as an AI development company Austria, AI development company Poland, and AI development company Italy.
Note:
We serve clients in Zurich, Geneva, Vienna, Warsaw, Krakow, Milan, and Rome with delivery teams that understand data privacy, GDPR compliance, and the EU AI Act requirements.
Custom AI Software Development
Machine learning models, predictive systems, and intelligent automation tailored to your workflows.
LLM Integration & Development
OpenAI, Claude, Llama, and Mistral integration via APIs, fine-tuning, or custom deployment.
AI Consulting Services
AI strategy, data audit, roadmap planning, and ROI-driven implementation guidance.
AI Automation & Workflows
Automate repetitive business processes using NLP, vision systems, and ML models.
AI Integration into Software
Add recommendation engines, predictive analytics, and intelligent search into existing platforms.
MLOps & Model Management
Production deployment, monitoring, retraining pipelines, and drift detection systems.
Business Problems Our AI Software Development Services Solve
We design practical AI solutions that help businesses clean up data, reduce manual work, connect systems, and improve customer experience at scale.
02
Problem
We want to add AI to our product but lack ML expertise.
Solution
We embed machine learning, NLP, or computer vision into your application through clean APIs without disrupting the existing codebase.
03
Problem
Our AI prototype works in the lab but fails in production.
Solution
We optimize inference, handle edge cases, monitor model drift, and build retraining pipelines for stable production performance.
04
Problem
We need to process documents, emails, or customer messages at scale.
Solution
We build NLP pipelines using LLMs and custom models to extract, classify, summarize, and route content automatically.
05
Problem
We want to automate visual inspection or quality control.
Solution
We train computer vision models to detect defects, classify images, and monitor visual data in real time on edge or cloud infrastructure.
01
Problem
We have data but don't know how to use it.
Solution
We start with a data readiness audit, identify gaps, and recommend high-impact AI use cases before building a proof of concept.
02
Problem
We want to add AI to our product but lack ML expertise.
Solution
We embed machine learning, NLP, or computer vision into your application through clean APIs without disrupting the existing codebase.
03
Problem
Our AI prototype works in the lab but fails in production.
Solution
We optimize inference, handle edge cases, monitor model drift, and build retraining pipelines for stable production performance.
04
Problem
We need to process documents, emails, or customer messages at scale.
Solution
We build NLP pipelines using LLMs and custom models to extract, classify, summarize, and route content automatically.
05
Problem
We want to automate visual inspection or quality control.
Solution
We train computer vision models to detect defects, classify images, and monitor visual data in real time on edge or cloud infrastructure.
Feature 01
Data Readiness & Preparation
Every AI project starts with data. We clean, label, and structure your data for machine learning. This includes handling missing values, removing bias, building feature pipelines, and setting up data validation checks.
Feature 02
Model Selection & Architecture Design
We choose the right approach for your problem: traditional ML (Random Forest, XGBoost), deep learning (TensorFlow, PyTorch), or LLM-based solutions (RAG, fine-tuning, prompt engineering). No over-engineering — we match complexity to business value.
Feature 03
LLM Integration & Prompt Engineering
We integrate large language models into your workflows with retrieval-augmented generation (RAG), custom fine-tuning, or API orchestration. Our prompt engineering ensures consistent, accurate outputs that align with your business rules.
Feature 04
AI Governance & Explainability
We build explainable AI systems with audit trails, bias detection, and human-in-the-loop review where needed. For regulated industries, we document model decisions and maintain compliance with GDPR and EU AI Act standards.
Feature 05
API-First AI Delivery
Every AI capability we build is exposed through clean REST or GraphQL APIs. Your development team can integrate AI features into web apps, mobile apps, or backend systems without learning machine learning.
Feature 06
Vector Database & Semantic Search
We implement vector databases (Pinecone, Weaviate, pgvector) for semantic search, recommendation engines, and knowledge retrieval. Turn unstructured data into queryable intelligence.
Example AI Use Cases We Can Build
01
Document Intelligence for Financial Services
A Swiss insurance company needs to process claims documents, extract relevant data, and flag suspicious patterns automatically. We would build an NLP pipeline using LLMs and custom entity extraction to read documents, validate against business rules, and route complex cases to human reviewers — reducing processing time and improving accuracy.
02
Predictive Maintenance for Manufacturing
An Austrian industrial equipment manufacturer wants to predict machine failures before they occur. We would develop a time-series forecasting model using sensor data, build alerts for maintenance teams, and create a dashboard showing equipment health across facilities — reducing unplanned downtime and maintenance costs.
03
Customer Support Automation for E-commerce
A Polish online retailer wants to automate responses to common customer inquiries while escalating complex issues to agents. We would implement a conversational AI system using LLMs with RAG, connected to the product catalog and order database — handling routine questions automatically and improving response times.
04
Visual Quality Inspection for Food Production
An Italian food processing company needs to detect product defects on production lines. We would build a computer vision system using custom-trained models to identify anomalies, integrate with existing conveyor systems, and provide real-time alerts to operators — improving quality consistency and reducing waste.
Step 1: AI Discovery & Use Case Definition (Weeks 1–2)
01We interview stakeholders, audit your data assets, and identify the AI opportunities with highest business impact.
Output
AI roadmap, data readiness report, and proof-of-concept plan.
Step 2: Data Preparation & Validation (Weeks 3–4)
02We clean, label, and structure your data. We build training pipelines, validate data quality, and establish baselines for model performance.
Output
Production-ready datasets and feature engineering pipelines.
Step 3: Model Development & Training (Weeks 5–10)
03We train and evaluate multiple model architectures. For LLM projects, we build RAG systems or fine-tune models on your data. We track experiments, compare metrics, and select the best-performing approach.
Output
Trained model with performance benchmarks.
Step 4: Integration & Deployment (Weeks 11–12)
04We integrate the model into your application via APIs, set up cloud infrastructure, and configure monitoring. For critical systems, we implement A/B testing and gradual rollout.
Output
AI feature live in production.
Step 5: Monitoring & Continuous Improvement (Ongoing)
05We track model performance, detect data drift, and retrain models as needed. Our MLOps pipelines automate testing, deployment, and rollback.
Output
Sustained model accuracy and business value.
Industries We Serve With AI Development
We deliver AI development solutions for fintech, healthcare, manufacturing, retail & e-commerce, logistics, and enterprise software, helping businesses automate workflows, improve decision-making, and scale with smarter systems. From fraud detection, predictive maintenance, and recommendation engines to intelligent document processing and route optimization, our AI solutions are built for performance, compliance, and real-world business impact across Europe and beyond.
Fraud detection, credit scoring, algorithmic trading, regulatory reporting automation. We understand financial data privacy and compliance requirements in Switzerland, Austria, Poland, and Italy.
Medical image analysis, patient risk stratification, clinical document processing, drug discovery support. HIPAA and GDPR-aligned data handling with explainable AI for clinical decision support.
Predictive maintenance, quality inspection, supply chain optimization, demand forecasting. Edge AI deployment for factory-floor real-time processing.
Recommendation engines, demand forecasting, dynamic pricing, customer segmentation. Real-time personalization at scale.
Route optimization, warehouse automation, delivery prediction, fleet management. AI-driven operational efficiency improvements.
Smart document processing, intelligent search, automated reporting, workflow optimization. AI embedded into existing business applications.
Why Businesses Choose Unisam for AI Development
Businesses choose Unisam Technologies for AI development because we go beyond models and automation to build intelligent solutions that align with business goals, user needs, technical reliability, and long-term scalability.
Frequently Asked Questions About AI Development Services
Start Your AI Project with Unisam
Whether you need a machine learning model, LLM integration, or full AI product development, Unisam delivers AI software that works in production.
Tell us about your AI idea or data challenge. We reply within 24 hours with a scope document and approach or Schedule a 30-Minute AI Discovery Call to discuss your use case with our AI team.
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