Efficiency
Custom LLMs are faster and more accurate at solving your specific tasks because they’re fine-tuned to your data, workflows, and objectives. Unlike public models like OpenAI’s, which charge based on usage volume, custom LLMs offer more predictable costs based on actual resource usage.
Domain knowledge
A custom-trained LLM built on your internal data understands your context, terminology, and industry specifics far better than generic models. This means more relevant outputs, fewer errors, and better alignment with your business goals.
Security
With custom LLM, your data stays yours. You control the training environment and avoid risks of data leakage, third-party exposure, or your proprietary information ending up in someone else’s model.
Fintech
With our integrated solution approach, you can flawlessly link different business-critical applications. Whether you need ERP, SCM, CRM, e-commerce, or specialized niche platforms like document management and manufacturing execution systems, we unify these diverse tools into a single digital conglomerate.
Edtech
We develop LLM solutions for automating legal agreements, content generation, managing student data privacy, and supporting regulatory compliance.
Retail
Improve contract management, protect customer data privacy, and ensure AI-powered regulatory compliance across global markets.
Real estate
Real estate businesses leverage LLMs to streamline property listings, tenant screening, property management, and transactions.
With years of experience in AI development, we help you gain more control, better performance, and secure AI adoption tailored to your business infrastructure.
Domain expertise
Up to 2–3x better results on your queries. Fine-tuned LLMs understand your industry-specific language, internal context, and data structure. That’s why you deliver only relevant and accurate outcomes.
Reproducible results
You get 100% consistent performance. Unlike third-party models that may change over time without notice, your own LLM delivers stable, reproducible outputs you can rely on for critical workflows.
Cost predictability
You don’t pay for data volume — you pay for the servers running your models. This gives you full cost transparency, especially at scale. With large datasets, you can accurately predict how much your LLM will cost per month — 100% pricing clarity.
Security
0% of your data leaves your environment. Unlike public models, your data stays within your private infrastructure and cannot be accessed or used for external training. You retain full control and ownership.
Our portfolio
Scaling Edge AI & computer vision: QuantumEye real-time surveillance platform
Geniusee helped the QuantumEye AI platform that upgrades legacy CCTV with real-time detection, instant incident notifications, and decision-ready analytics.
Case details
Anatomy Financial: Automated financial management software for healthcare
Anatomy introduces an innovative solution designed to automate document processing, compare various documents and banking transactions, and handle the intricacies of physical check processing. By conducting a thorough analysis, the system identifies discrepancies between the declared prices of medical procedures and the actual amounts received by the clinic.
Case details
MartechCompose AI: Advanced content writing solution
We partnered with Compose AI to enhance their AI-powered writing platform through cloud migration, UX improvements, seamless integrations, and scalable feature development, upporting their growth from YC-backed startup to a widely adopted productivity tool.
Case details
MartechForethought: Enterprise AI agents for customer support
Forethought partnered with Geniusee to extend and integrate its AI-powered support platforms (Assist and Discover) into complex enterprise environments. We helped enhance real-time agent assistance, streamline multi-system connectivity, and deliver a unified support experience across channels. The result is a scalable AI agent ecosystem that accelerates resolution times, boosts team productivity, and powers more consistent customer experiences.
Case details
Enhancing FactMata’s AI to detect misleading content at scale
We partnered with Factmata to scale their AI platform for analyzing online content at volume, enhance data pipelines, and modernize their NLP models. Our team strengthened system performance, improved processing efficiency, and enabled Factmata to handle millions of records with higher accuracy and lower operational cost. By implementing our expertise in machine learning, natural language processing, and data analysis, we worked closely with FactMata to create web scrapers and optimize data pipelines and the project’s overall performance. The client addressed Geniusee to modernize their platform by combining real-time IoT data, AI analytics, and an intuitive user experience. The result: a scalable, secure, and analytics-driven ecosystem that helps enterprises make informed workspace decisions.
Case details
AI-driven-communication-assistant
Our client is a technology company specializing in providing services for industries with highly regulated communications. Their AI-based expertise allows to make those processes more simple. Geniusee was entrusted with migrating client’s infrastructure from Azure to Amazon, aiming to consolidate their crucial infrastructure in one place. The project involved migrating their database and leveraging Amazon services to optimize the system's scalability, security, and performance.
Case detailsLLM Models
LLM as a Service
LLM Infrastructure
1 step – Defining objectives
During this phase, we define your specific needs and goals, market challenges, the context in which the model will operate, and use cases that the LLM will address. By the end of it, we will set clear objectives and success criteria.
2 step – Data preparation
Our team helps you with data collection. We prepare annotated datasets optimized for model training and fine-tuning by cleaning and annotating them. This involves resolving inconsistencies, handling missing values, and labeling data for the LLM to learn from. The outcome is a refined, high-quality dataset optimized for training your model.
3 step – Model development
Next, we select the most suitable LLM architecture based on your needs, then train and fine-tune the model using your prepared data to maximize performance.
Afterwards, we evaluate the model to ensure it meets your target metrics. This process includes multiple iterations of training, evaluation, and improvement.
4 step – Deployment and maintenance
Once tested, we deploy your custom LLM to a scalable infrastructure. We handle ongoing hosting, maintenance, and support to keep your model up-to-date. As your data and use cases evolve over time, we can retrain and enhance the model to ensure continued high performance.
Genuisee’s versatile experience, gained over more than 8 years, has enabled us to form a team with a proven track record.
Certified AWS Partner delivering secure, scalable cloud-native solutions.
ISO-compliant processes ensuring quality, security, and reliability.
Trusted integration partner for financial data connectivity and open banking.
Team of ISTQB-certified QA engineers for world-class software testing.
Consistently rated ★5.0 by clients for reliability and delivery excellence.
Accredited partnership supporting advanced testing and continuous QA automation.
