Loading...
Loading...
Develop and integrate large language model solutions that understand your business context, work with your data, and power intelligent enterprise applications.
Develop and integrate large language model solutions that understand your business context, work with your data, and power intelligent enterprise applications.
Large Language Models (LLMs) are incredibly powerful, but out of the box, they don't know your business. LLM Development is the engineering discipline of connecting these foundation models securely to your proprietary data, business logic, and user applications.
Whether you need to embed AI search into your SaaS product, build an internal copilot that knows every PDF in your company, or automate complex document processing workflows, we architect solutions that turn raw LLM capabilities into reliable, production-ready business applications.
Off-the-shelf AI doesn't understand your specific business logic, terminology, or proprietary processes.
Employees waste hours searching for information across scattered enterprise databases and messy document stores.
Teams are overwhelmed with repetitive manual tasks like summarizing reports, classifying emails, and drafting responses.
Struggling to seamlessly and securely connect advanced language models into your existing legacy software products.
Custom LLM development, prompt engineering, RAG architecture, and enterprise LLM integration using OpenAI, Anthropic, Gemini, and open-source models.
We engineer secure LLM pipelines tailored to your enterprise terminology, proprietary knowledge bases, and strict privacy boundaries.
Connect GPT-4, Claude 3.5, or Gemini directly into your business web platforms with optimized API orchestration.
Deploy Llama 3, Mistral, or Qwen models on your private cloud infrastructure for 100% data control.
Ground model responses in real-time enterprise databases to eliminate hallucinations and produce cited answers.
Implement safety filters, PII redaction, prompt injection defense, and output verification mechanisms.
Schedule a technical architectural review to evaluate the optimal LLM framework for your application performance and budget.
Retrieval-Augmented Generation (RAG) grounds the LLM in your private company data, drastically reducing hallucinations and making responses highly accurate and verifiable.
Internal conversational agents that securely query your HR, IT, and operational documents.
Automatically extract, summarize, and structure data from massive volumes of complex unstructured documents.
Best for rapid development and extremely complex reasoning tasks.
Best for data privacy, on-premise deployment, and fine-tuning.
Best for specific tasks like coding, math, or low-latency operations.
An LLM is only as smart as the context it is given. We engineer dynamic prompts that inject real-time business data, tools, and conversation history into the model's context window.
While RAG is perfect for teaching an LLM facts, fine-tuning is required when you need the model to learn a specific behavior, tone, or complex output format (like strict JSON structures for code generation).
We prepare high-quality training datasets and fine-tune open-source models, giving you a proprietary, custom-built LLM that runs in your own secure environment.
We don't just deploy models; we scientifically evaluate them. Our testing frameworks programmatically score LLM responses before they reach production.
Security is the biggest barrier to enterprise LLM adoption. We architect solutions with strict data privacy, ensuring your proprietary documents never leak into public foundation models.
Identify the business problem and LLM opportunity.
Choose the appropriate model and architecture.
Prepare prompts, context, knowledge, and data.
Build and validate the initial LLM experience.
Connect applications, APIs, and business systems.
Test quality, safety, cost, and performance.
Release the LLM solution into production.
Track usage, quality, cost, and potential failures.
Continuously improve prompts and vector retrieval.
Analysts spent 40% of their time searching through 50,000+ unorganized PDF reports and internal regulatory documents to answer basic compliance questions.
Built a custom RAG (Retrieval-Augmented Generation) pipeline using pgvector and an LLM to semantic-search documents and synthesize highly accurate answers with source citations.
Reduced information retrieval time by 75%. Analysts now query their knowledge base in natural language and receive verified answers instantly.
A complex SaaS platform struggled with user onboarding; users frequently submitted support tickets because they couldn't find specific features.
Integrated a context-aware LLM Copilot directly into the software interface. The model was contextually aware of the user's screen and previous actions.
Decreased tier-1 support tickets by 45% and improved feature adoption rates by guiding users through complex workflows step-by-step.
Explore some of our most impactful digital transformations.
"CodeCyper didn't just plug into an API; they built a comprehensive architecture that securely connects our proprietary data to the LLM. The RAG pipeline they implemented is a game-changer for our legal research team."
"We wanted to add generative AI to our product, but we were worried about hallucinations and costs. The CodeCyper team engineered a prompt and context management system that keeps responses accurate and API costs incredibly low."
Turn large language models into secure, scalable, business-ready applications that work with your data, systems, and users.