Turnkey LLM development: turning text into a tool for business growth
We build LLMs from scratch. We choose the right architecture, train the model on relevant texts, and validate performance. You get a tool that captures nuance, produces accurate answers, and analyzes large volumes of information. We integrate the LLM into your systems, ensure scalability, and train your team. We focus on data quality so the model works only with trusted information.
Our capabilities
To train the model, we collect corporate reports, emails, and analytics. The LLM extracts key metrics, forecasts trends, and produces executive digests. In retail, the model analyzes sales feedback, identifies weak points, and suggests improvements. LLM development for business analytics helps process thousands of documents in minutes. We adapt the language model to industry vocabulary—finance, logistics, marketing, and more. We keep data security in mind at every stage.
We train generative AI on templates for contracts, statements of work, and internal guidelines. The LLM generates documents from key parameters, checks terminology consistency, and fills in forms. Turnkey LLM solutions automate up to 80% of routine work. The model translates terms, closes informational gaps, and flags risks. We connect language models with CRM and ERP systems.
We train the model on chats, emails, and calls transcribed into text. As a result, the LLM classifies requests, drafts templated replies, and escalates complex cases. E-commerce teams use language models to understand customer slang, recommend products, and resolve disputes. Our LLM engineers align the model with your brand voice.
A custom LLM helps you create articles, posts, and product descriptions in a consistent tone. The model adapts content to SEO requirements and adds verifiable facts. LLM development for content generation scales SMM and SEM. Ordering a tailored model from FreeBlock is a practical way to reduce AI-text plagiarism risk and make outputs more readable and brand-consistent.
We train LLMs on parallel corpora while preserving cultural context. Our models translate marketing copy, documentation, and websites while keeping style, selecting the right synonyms, handling idioms, and maintaining domain terminology. LLM solutions for translation speed up international expansion. Want to build a localization-focused LLM? We’ll design the approach and deliver it.
We build language models trained on repositories, technical docs, and real engineering tasks. LLMs suggest functions, tests, SQL queries, regex patterns, and reusable templates while following your team’s conventions. Generative AI speeds up development, reduces mistakes, and helps prototype complex modules quickly. We add guardrails, quality checks, and static analysis integrations.
We use textbooks, quizzes, and validated cases to train learning-focused models. The LLM creates tests, explains topics, and generates examples. It personalizes content to each learner’s level. LLM development for education improves course completion and helps close knowledge gaps. We integrate learning models with LMS platforms.
We develop domain-specific language models for niche workflows. In healthcare, LLMs read discharge summaries; in legal, they parse regulations; in support, they diagnose issues; in pharma, they summarize research faster. Specialized LLM development ensures domain accuracy. Models can be trained on private data with full compliance. Your experts get a premium-grade tool that fits their daily work.
Why choose us
We’ve been building AI since 2018. Our team combines data scientists, ML engineers, and domain experts. We reduce overfitting, maximize accuracy, and deliver a prototype fast. FreeBlock LLM engineers understand tokenization, fine-tuning, and production constraints.
We analyze your processes, interview stakeholders, and design an LLM aligned with measurable success metrics. We build models for unique requirements, including generative systems for retail chains and personal brands. We use techniques like LoRA to reach strong performance efficiently.
We apply transformer architectures, RAG, and quantization. When needed, models can run on edge devices without the cloud. We monitor metrics in real time and keep systems up to date with fresh datasets and feedback loops.
We deploy on AWS, GCP, or on-prem infrastructure. We train your team in prompting and tuning workflows. Our contracts include monitoring and updates. We track model behavior changes and provide 24/7 support where required.
We encrypt data and run audits for systematic errors, bias, and unsafe outputs. Every LLM goes through stress testing. Our approach minimizes leakage risks. We can provide an SLA with high availability (excluding scheduled maintenance windows).
Development
stages
Initial discovery & requirements
Data & source research
Solution & architecture design
Testing & quality validation
Model development & training
Data preparation & labeling
Deployment & integration
Monitoring, support & improvements
LLM development — launch a language model that strengthens your market leadership
Want to see how turnkey LLM solutions can transform your business? Reach out. We’ll choose the stack, assemble the right data, and deliver an MVP in weeks. You can order LLM development from scratch, fine-tune an existing model, or build on prior internal work.
FreeBlock’s LLM development is always outcome-driven. Leave a request and move forward.
LLM development FAQ
Is it better to train our own LLM or use an existing one?
For most projects, fine-tuning open models such as Llama and Mistral or building RAG on top of an existing LLM is optimal — faster and cheaper than training from scratch. Full training pays off only with special requirements for data and control.
What is RAG and when is it needed?
RAG (retrieval-augmented generation) connects the model to your knowledge base: answers are built on your company's up-to-date documents rather than training data alone. It fits documentation search, customer support, and analytics.
What data is needed to train or fine-tune a model?
Texts from your domain: documentation, correspondence, articles, reports. We clean and label the data, remove confidential information, and verify quality — the model's accuracy depends on it directly.
Can an LLM be deployed inside our infrastructure?
Yes, we deploy open models on-premise or in a private cloud so data never leaves your infrastructure. We optimize models for your hardware using quantization and distillation.
How do you deal with model hallucinations?
We use RAG with mandatory source references, configure system-level constraints, add answer validation, and test the model against benchmark sets before launch and after every update.
How long does an LLM project take?
A RAG-based prototype takes 4-6 weeks; fine-tuning a model with deployment in your infrastructure takes 2-4 months. We fix the timeline and stages after analyzing the data and requirements.