Large Language Models (LLMs)

Build domain-specific conversational AI, copilots, and knowledge systems using advanced LLMs engineered for security, governance, and enterprise scale.

At Radiansys, we build LLM systems that combineGPT, LLaMA, Claude, and Mistral with retrieval, fine-tuning, and secure deployment to deliver accurate, governed AI for enterprise workflows.

Fine-tune LLMs for domain-specific reasoning and internal terminology.

Enhance reliability with RAG pipelines grounded in verified enterprise data.

Embed AI copilots into CRMs, ERPs, and workflow systems for real-time automation.

Deploy in private VPCs with encryption, SSO, audit logging, and Zero Trust governance.

How We Implement LLMs

At Radiansys, LLM implementation is treated as an end-to-end engineering practice. We go beyond simple API usage, building secure, fine-tuned, retrieval-augmented systems that align with enterprise workflows, data policies, and regulatory requirements. Every deployment includes guardrails, evaluation, and observability so LLMs deliver consistent, measurable outcomes.

Domain-Tuned LLM Development

We start by adapting GPT, LLaMA, Mistral, or Claude models to your industry and internal knowledge. This includes supervised fine-tuning, instruction tuning, and domain alignment using curated datasets. The result is an LLM that understands your terminology, workflows, and compliance rules, producing accurate and contextual responses across all use cases.

01

RAG Pipelines & Knowledge Retrieval

We design Retrieval-Augmented Generation architectures using vector databases like Milvus, Pinecone, or pgvector. LLMs gain access to your documents, databases, and knowledge bases through semantic search and verifiable context injection. This eliminates hallucinations and ensures answers are grounded in approved enterprise data.

02

Multi-Agent Orchestration

For complex workflows, we deploy LangChain- or AutoGen-based multi-agent systems. Each agent has a clear role: retriever, analyst, planner, executor, or validator, enabling coordinated reasoning. Guardrails ensure safe execution, policy compliance, and rollback capabilities for production environments.

03

Enterprise System Integrations

LLM copilots are embedded directly into systems like Salesforce, ServiceNow, SAP, and Microsoft Teams. Through secure API connectors and authentication standards including OAuth2, SSO, and SCIM, models can read data, take actions, and automate multi-step processes inside enterprise tools.

04

Security, Governance & Compliance Controls

All deployments apply SOC2, HIPAA, and GDPR-aligned controls such as RBAC/ABAC permissions, encryption, auditing, output filtering, and data minimization. Governance layers validate model outputs, enforce rules, and ensure every inference is safe, traceable, and compliant with enterprise policies.

05

Use Cases

Enterprise Knowledge Assistants

RAG-powered assistants that deliver accurate, citation-backed answers from enterprise documents.

AI Copilots for Operations

Copilots embedded in CRMs and ERPs to draft responses, validate entries, and automate daily tasks.

Document Intelligence

Summarize, classify, redact, and extract data using fine-tuned LLMs for high-volume document workflows.

Workflow Automation

LLM and multi-agent systems that automate onboarding, ticketing, approvals, reporting, and reviews.

Business Value

Higher Accuracy

Fine-tuned and RAG-powered LLMs deliver grounded, verifiable results across business processes.

Lower Costs

Optimized prompt design, caching, and local inference reduce operating costs across high-volume workloads.

Faster Automation

LLM copilots and agents reduce manual tasks by up to 60%, improving productivity across departments.

Stronger Governance

Compliance-ready setups ensure safe, secure, and explainable AI across the enterprise.

FAQs

We support GPT, LLaMA, Claude, Mistral, Falcon, and specialized open-source models from Hugging Face.

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