Development Services
Large Language Models have fundamentally changed what software can do with language. At Zentury Studio, we build production-ready LLM solutions — from custom fine-tuning and Retrieval-Augmented Generation (RAG) systems to full LLM-powered applications — that solve specific business problems rather than just demonstrating what’s technically possible.
From choosing the right use case to running models in production — the full lifecycle of a language-model product, built properly.
We help you identify where an LLM can genuinely add value to your product or operations — and equally important, where a simpler approach would work better. We evaluate your use case, your data, and your constraints before recommending a technical approach.
We fine-tune foundation models (GPT, LLaMA, Mistral, and others) on your proprietary data — teaching the model your domain vocabulary, your content style, your decision logic, and your quality standards. The result is an LLM that behaves like an expert in your specific field.
RAG lets your LLM answer questions based on your actual documents, data, and knowledge base — rather than relying solely on its training data. We build RAG pipelines that retrieve the right context and generate accurate, grounded responses, dramatically reducing hallucination.
We build the full application layer around your LLM — user interfaces, conversation management, memory systems, tool use, and the integrations needed to connect your language model to your data sources, APIs, and workflows.
We build systematic evaluation frameworks to measure your LLM’s accuracy, consistency, and safety — and implement guardrails that prevent harmful outputs, prompt injection attacks, and responses outside your intended scope.
We handle the infrastructure side of LLM deployment — whether that means managed API services, self-hosted open-source models, or hybrid architectures. Optimized for latency, cost, and data privacy requirements.
LLM applications are transforming industries from legal and healthcare to e-commerce and customer service.
In line with HIPAA and the medical sector’s strict privacy requirements, we build AI that helps physicians and nurses work more efficiently and make better judgments — while protecting all patient data.
Models that help banks and financial institutions spot fraud, answer customer questions about loans and accounts, and speed up document processing — while meeting legal requirements and safeguarding financial data.
AI that learns what each customer loves, shows them products they want, writes unique product descriptions, and sends targeted emails that lift sales and satisfaction.
AI tutors that let students learn at their own pace — answering questions, creating practice exams, and tracking progress, while giving teachers insight into each student’s performance.
AI that reads legal documents, reviews contracts for issues, answers common legal questions, and helps attorneys find the right cases and regulations faster — with compliant, auditable work.
AI that plans the most efficient truck routes, monitors cargo in transit, flags delays, and helps teams schedule their work so deliveries arrive on time.
We design your AI project from beginning to end and make sure it works properly for your users on launch day.
We confirm where an LLM genuinely adds value, and where a simpler approach would work better.
We gather, clean, and structure your documents and data for fine-tuning and retrieval.
GPT, LLaMA, Mistral, or a fine-tuned model — chosen for your accuracy, privacy, and cost needs.
RAG pipelines, the application layer, and integrations with your data sources and workflows.
Accuracy and safety testing, guardrails, and protection against prompt injection before launch.
Production hosting tuned for latency and cost, with continuous evaluation after go-live.
Foundation models, retrieval, and production infrastructure — selected for each project’s accuracy, privacy, and cost requirements.
The right model. Grounded in your data.
Our LLM development capabilities cover the full range of what modern language models can do in production.
Ask questions of your documents — contracts, manuals, research, support tickets — and get accurate, source-cited answers.
Specific fields, entities, and structured data pulled from unstructured text — at a scale and accuracy manual work can’t match.
Product descriptions, email drafts, report summaries, and code — in your formats, voice, and quality standards.
Stateful agents with memory and tool use that handle multi-step tasks — well beyond single-turn Q&A.
Generic software rarely fits complex industries. We take time to understand the regulatory environment, user behavior, and operational realities of your space before writing a single line of code.
Accelerating the path to disruption and value creation has earned us recognition from leading ratings and review platforms.
Working with Zentury has been a game-changer for our business. Their AI solutions have revolutionized our operations, enabling us to automate repetitive tasks and make data-driven decisions with ease. We couldn’t be happier with the results.
Partnering with Zentury has transformed our business. Their AI solutions have streamlined our operations by automating routine tasks and empowering us to make smarter, data-driven decisions. The results have exceeded our expectations.
It depends on your use case, data sensitivity, and cost tolerance. GPT-4 and similar APIs are excellent for general tasks with no data privacy concerns. Open-source models (LLaMA, Mistral) are better when you need data privacy, lower inference costs, or the ability to fine-tune extensively. We’ll analyze your requirements and recommend the right approach — not the most expensive one.
RAG is a technique that gives your LLM access to a knowledge base — your documents, data, or database — at inference time. Instead of relying purely on what the model learned during training, it retrieves relevant context and uses it to generate accurate, grounded responses. Most production LLM applications benefit from RAG because it dramatically reduces hallucination and keeps responses up to date.
Hallucination prevention is a systems problem, not just a model problem. We combine RAG (to ground responses in real data), output validation (to check responses against known facts), confidence scoring, and human-in-the-loop escalation for low-confidence responses. We also build evaluation pipelines that continuously test for factual accuracy on your specific domain.
A focused LLM integration — like a RAG-powered Q&A system for your documents — can be delivered in 6–10 weeks. A full custom LLM application with fine-tuning, evaluation infrastructure, and production deployment typically takes 3–5 months.
Let’s Build Something That Works
Have an idea you’re ready to move on? A problem your current technology isn’t solving? Our team is based in Austin, Texas, and works with businesses all over the world. Reach out — the first conversation is free, and you’ll leave with clarity, not a sales pitch.
