Efficiency
Generic models answer generic questions. A custom LLM trained on your internal data handles domain-specific queries faster and with higher accuracy — without hallucinating terminology your team actually uses. Unlike API-based models that bill per token, your own model runs on predictable infrastructure costs.
Domain knowledge
When your model has been trained on your product documentation, support history, or regulatory filings, it understands the context that GPT-4 simply won’t. That means fewer corrections, fewer manual reviews, and outputs that actually fit your processes.
Security
Your training data stays in your environment. No third-party model sees your proprietary content, and there’s no risk of it appearing in someone else’s outputs. For regulated industries (FinTech, healthcare, legal) this isn’t optional, it’s required.
Fintech
- Document processing, contract analysis, regulatory compliance monitoring
- Fraud detection and transaction narrative classification
- Internal knowledge assistants for compliance teams and analysts
- Customer-facing chatbots with KYC context and secure authentication
What clients get: faster review cycles, lower manual processing costs, compliance confidence in regulated environments
Edtech
- AI tutors, adaptive assessment tools, personalized learning path generators
- Content creation pipelines for course authors and curriculum teams
- Student query handling across LMS platforms without live support overhead
What clients get: higher learner engagement, lower support costs, scalable 1-on-1 interaction at class size
Retail
- Product recommendation and search relevance improvement
- Inventory and supply chain query automation
- Internal knowledge bots for store operations and associate training
What clients get: higher conversion, faster internal response times, reduced dependency on manual reporting
Real estate
- Property listing generation and comparison from structured data
- Tenant screening documentation processing
- Investor report generation from portfolio data
What clients get: faster time from data to decision, more consistent communication across large property portfolios
Healthcare
- Clinical documentation assistants that summarize patient records and flag anomalies
- Medical coding support and prior authorization automation
- Research literature synthesis tools for clinicians and R&D teams
What clients get: reduced administrative burden on clinical staff, faster documentation cycles, HIPAA-compliant data handling
Legal
- Contract review, clause extraction, and risk flagging
- Case research summarization across large document volumes
- Regulatory change monitoring and internal policy Q&A
What clients get: faster legal review cycles, lower external counsel costs, reduced human error in high-volume document work
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 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.
Security
None 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
Fine-tuning & training
RAG & orchestration
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.
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.
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.
Deployment and maintenance
Once tested, we deploy your custom LLM solution 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.
Do you build custom LLMs from scratch, or do you fine-tune existing models?
Both, and we’ll tell you which makes more sense for your situation. Custom training from scratch gives you full architectural control but requires substantial data and budget. Fine-tuning an open-source model like Mistral or LLaMA 4 on your domain data is often faster and more cost-effective for most mid-complexity use cases. We assess this during discovery before any development begins.
How much does custom LLM development cost?
It depends significantly on the scope. A fine-tuning project with existing clean data and a defined use case can start at $30–80k. A full custom training project with data collection, architecture design, and infrastructure setup typically runs $100k+. We provide a detailed estimate after a discovery scoping session, but you also can also calculate an approximate price with our Geniusee Estimator.
How long does LLM development take?
A fine-tuning engagement with good data can move from kickoff to deployment in 6–12 weeks. A custom training project with data preparation from scratch typically takes 3–6 months. We build milestone-based delivery plans so you have clear checkpoints throughout.
Can you deploy the model on our own infrastructure?
Yes. We can deploy on your private cloud, on-premise servers, or a hybrid setup. For regulated industries like healthcare and finance, on-premise or private cloud deployment is often the right choice for data governance reasons. We configure the full serving infrastructure — not just the model.
What data do we need to provide?
It depends on the approach. For fine-tuning, domain-specific text data (internal documents, support tickets, product knowledge bases) is usually sufficient. For custom training, larger labeled datasets are needed, and we help with collection and annotation. For RAG, we work with your existing document corpus directly.
How do you evaluate whether the model is actually working?
We don’t rely on vibes. We define success metrics at the start of the project (accuracy benchmarks, response quality scores, latency targets) and validate against them before deployment. We use tools like HoneyHive and HumanLoop for structured evaluation, alongside standard benchmarks where applicable.
