Cinovic Technologies LLP delivers end-to-end LLM solutions, integrating OpenAI, Anthropic, Mistral, LLaMA and custom models into your existing tech stack with precision and speed.
We partner with startups, SMBs, and large enterprises worldwide to design, build, and deploy Large Language Model solutions that deliver measurable business outcomes.
We don't just integrate APIs, we architect production-grade LLM systems built for accuracy, latency, security, and long-term maintainability.
From LLM selection and fine-tuning to RAG pipeline design, API integration, and enterprise deployment we cover the full LLM development lifecycle.
Fine-tune open-source and proprietary LLMs (LLaMA, Mistral, GPT-4, Gemini) on your domain-specific data for higher accuracy, lower hallucination, and reduced inference costs.
Build retrieval-augmented generation systems that connect LLMs to your knowledge base, documents, and databases, delivering grounded, accurate, and up-to-date responses.
Seamlessly integrate OpenAI, Anthropic Claude, Cohere, Mistral, and Hugging Face models into your existing applications, CRMs, ERPs, and internal tools.
Design, test, and iterate structured prompts and prompt chains that maximise LLM output quality, consistency, and safety across your use cases.
Deploy intelligent chat interfaces, virtual assistants, and support bots built on LLMs, with memory, multi-turn context, and tool-use capabilities.
Production deployment of LLMs on AWS, Azure, or GCP, with CI/CD pipelines, model versioning, monitoring, and cost optimisation built in.
Fine-tune open-source and proprietary LLMs (LLaMA, Mistral, GPT-4, Gemini) on your domain-specific data for higher accuracy, lower hallucination, and reduced inference costs.
Build retrieval-augmented generation systems that connect LLMs to your knowledge base, documents, and databases, delivering grounded, accurate, and up-to-date responses.
Seamlessly integrate OpenAI, Anthropic Claude, Cohere, Mistral, and Hugging Face models into your existing applications, CRMs, ERPs, and internal tools.
Design, test, and iterate structured prompts and prompt chains that maximise LLM output quality, consistency, and safety across your use cases.
Deploy intelligent chat interfaces, virtual assistants, and support bots built on LLMs, with memory, multi-turn context, and tool-use capabilities.
Production deployment of LLMs on AWS, Azure, or GCP, with CI/CD pipelines, model versioning, monitoring, and cost optimisation built in.
We combine cutting-edge LLM research with production engineering discipline to deliver AI capabilities that are robust, explainable, and enterprise-ready.
Automate complex reasoning tasks, document processing, data extraction, and multi-step workflows using orchestrated LLM agents.
Domain-specific model training using RLHF, PEFT, LoRA, and QLoRA techniques, optimising for your data, latency targets, and cost constraints.
Stateful conversation management, memory injection, persona design, and tool-calling for LLM assistants that handle complex, extended interactions.
Combine LLMs with structured data, vector databases, and analytics pipelines to surface insights, forecasts, and recommendations at scale.
Stay ahead with practical guides, case studies, and technical deep-dives on Large Language Model development, RAG, fine-tuning, and enterprise AI deployment.
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Book a free consultation and discover how custom LLM development and integration can transform your product, automate workflows, and unlock new revenue streams.
LLM development involves designing, training, fine-tuning, and integrating Large Language Models into software products and business workflows to automate tasks, generate content, and power intelligent applications.
Retrieval-Augmented Generation (RAG) is a technique that connects LLMs to external knowledge bases so they produce accurate, grounded, and up-to-date responses, eliminating hallucinations in enterprise applications.
We work with all major LLM providers, including OpenAI GPT-4o, Anthropic Claude, Meta LLaMA 3, Mistral, Google Gemini, and Cohere, as well as open-source models deployed on private infrastructure.
LLM integration connects an existing pre-trained model (like GPT-4) to your application via API. Fine-tuning further trains the model on your proprietary data to improve accuracy for domain-specific tasks.
Timelines vary by scope. A basic LLM API integration can take 2–4 weeks. A full custom RAG pipeline or fine-tuned model deployment typically takes 6–12 weeks, depending on data availability and infrastructure requirements.