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Generative Business Intelligence for AnalystsDevcin engineers production-grade LLM systems from RAG-powered search to automated content workflows to AI agents. Grounded in your data, optimized for cost, deployed on your infrastructure.
Custom LLMs trained on proprietary business data
Secure private deployment (cloud or on-premise)
Fine-tuned accuracy for domain-specific use cases
Scalable inference and cost-controlled architecture
Devcin integrates large language models into your product — custom fine-tuning, RAG pipelines, prompt engineering, evaluation frameworks, and production deployment. We work with OpenAI, Anthropic, Google, and open-source models like Llama and Mistral.
We provide end-to-end LLM development services, from strategy and model design to deployment and ongoing optimisation.
Design and development of private large language models trained on your internal data, terminology, and workflows.
Fine-tuning of existing foundation models (GPT-based, Llama-based, Mistral-based) to improve accuracy, relevance, and task-specific performance.
Development of production-ready applications powered by LLMs, including internal assistants, analytics tools, automation engines, and customer-facing systems.
AI solutions designed for regulated and enterprise environments, supporting governance, access control, auditability, and compliance requirements.
Ongoing monitoring, retraining, performance tuning, and cost optimisation as usage and data volumes scale.
Our process is designed to reduce AI risk and ensure predictable, enterprise-grade delivery.
We define business objectives, evaluate data readiness, and identify where LLMs provide measurable value.
Private LLMs belong inside your boundary grounded on docs and operational data your teams already trust. We build retrieval, summarisation, and automation with evaluation gates, not prompt roulette.
Our LLM development services are best suited for:
Enterprises handling large volumes of internal data
SaaS platforms embedding AI into core workflows
Organisations requiring private, secure AI deployments
Teams automating research, reporting, or support operations
Businesses scaling AI beyond proof-of-concept
If accuracy, security, and control matter, custom LLM development is essential.
Experience delivering production-grade AI systems
Strong focus on security, governance, and compliance
Architecture designed for scale and cost control
Clear, structured delivery process
Long-term support beyond initial deployment
We build LLMs as long-term assets, not short-term demos.
We have been building powerful, secure, and scalable digital solutions for our clients for many years and have received consistent, high-quality feedback. Here is what they have to say.
Whether you're introducing AI into internal operations or embedding LLMs into your product, we can help you build a controlled, production-ready solution.
LLM development is the engineering practice of building production applications powered by large language models — systems that understand, generate, and reason with human language at scale, connected to an organization's proprietary data through retrieval-augmented generation (RAG), fine-tuning, and structured prompt workflows. Devcin offers end-to-end LLM development services, building custom applications including RAG-powered search and Q&A systems, AI content generation pipelines, automated document analysis tools, and intelligent agent architectures. The company serves SaaS product teams embedding LLM features into existing platforms, enterprise innovation groups automating knowledge-work in regulated industries, and AI-native startups building entire products around LLM capabilities. Devcin is model-agnostic, selecting from GPT-4o, Claude, Llama, Mistral, and Gemini based on accuracy benchmarks, latency requirements, cost constraints, and data privacy needs, and deploys on customer infrastructure using vLLM or TGI with full isolation for sensitive data. Each LLM project includes automated evaluation pipelines, content guardrails, cost monitoring, and continuous prompt optimization to ensure the system remains accurate, safe, and cost-effective as models and usage patterns evolve.
We partner with ambitious teams to solve real problems, ship better products, and drive lasting results.
ChatGPT is a general-purpose tool with no access to your data. LLM development builds custom applications that connect to your proprietary information, follow your business rules, operate within your cost constraints, and deploy on your infrastructure with full security controls.
Retrieval-Augmented Generation (RAG) retrieves relevant information from your knowledge base and feeds it to the LLM before it generates a response. This grounds outputs in your actual data, dramatically reducing hallucinations and making answers accurate and verifiable.
Fine-tune when you need the model to adopt a specific writing style, tone, or domain knowledge that appears repeatedly across queries. Use RAG when answers depend on dynamic or changing information — documents, product catalogs, support articles — that needs to stay current without retraining.
Yes. We deploy on AWS, Azure, GCP, or your private data center using vLLM, TGI, or Ollama. For sensitive industries, we set up fully isolated deployments where your data never touches a public API endpoint.
We build automated evaluation pipelines that test each model or prompt configuration on relevance, faithfulness, hallucination rate, safety, and latency. We use both automated metrics and human raters for high-stakes tasks.
Building an LLM application typically ranges from $70,000 to $300,000 depending on complexity. Ongoing inference costs vary by model, query volume, and deployment — we optimize up front and monitor continuously to control spend.
We are model-agnostic. We work with GPT-4o, GPT-4 Turbo, Claude, Llama 4, Mistral, Gemini, and fine-tuned open-source variants. We select based on accuracy benchmarks, latency, cost, and privacy requirements.
We combine RAG grounding, constrained prompt templates, confidence thresholds, automated evaluation, and human-in-the-loop escalation for low-confidence outputs. No single technique eliminates hallucinations, but the combination reduces them to acceptable levels.
Tell us about your idea or business needs. Our team will review your requirements and get back to you with a clear plan, timeline, and a free consultation call.
