Large Language Models (LLMs)

Deploy Enterprise LLMs That Power Real Business Outcomes

CustomLLM, agenticpipelines, privateRAG… we can integrate, fine-tune and scale production readyAI.

Services Portfolio

Our LLM Services Stack

From model tuning to multi-agent deployment, discover specialized capabilities to implement secure, robust, and cost-effective AI.

Integration

Custom LLM Integration

Connect world-class models to your system.

Wire up the tools you already have to OpenAI, Claude, or open-source LLaMA. We do the secure API connections, we manage the structure pipes, turning stagnant software into alive and kicking software.

OpenAI APIAnthropic ClaudeLlamaIndexLangChain

Agentic

LLM-Powered AI Agents

Automate complex multi-step workflows autonomously.

Use autonomous systems that plan, use tools, and work together to help you reach your business goals. Save time and effort by automating tasks that usually need manual work.

CrewAILangGraphn8nPython

RAG

Retrieval-Augmented Generation (RAG)

Answer queries using your secure private knowledge.

Let large language models look into your private files, databases, and wikis without making things up. Protect your data while giving context-based answers to support staff, customers, and managers.

PineconeLlamaIndexOpenAILangChain

Conversational

Conversational AI & Chatbots

Deliver natural customer support around the clock.

Offer support that sounds human and understands context through chat, email, and messaging. Fix support issues faster, get good leads, and send tough questions to your agents smoothly.

n8nMake.comGPT-4oLangChain

Fine-Tuning

Model Fine-Tuning & Prompt Engineering

Optimize models to speak your brand language.

Train large language models on your own data and custom prompt setups to get the best accuracy. Lower computing costs and delays, while making sure the output matches your company's style and rules

PyTorchLlamaMistral AIOllama

Analytics

LLM-Driven Data Analytics

Turn unstructured text data into actionable insights.

Automatically pull out feelings, key details, and set performance indicators from thousands of support tickets, emails, and PDFs. Give your decision-makers the power of raw text data through interactive dashboards and reports.

PowerBIScikit-learnPythonMistral

Multimodal

Speech, Vision & Multimodal AI

See, hear, and interact with your users.

Create voice agents and visual analysis tools that can record calls, create realistic speech, and analyze video. Improve how users interact by offering multi-sense experiences and automated content checks.

ElevenLabsWhisper STTOpenCVPyTorch

Infra

LLM Deployment & Cloud Infrastructure

Host secure private models at scale.

Install secure, open-source large language models on your own cloud that you can host yourself. These systems offer full visibility and can automatically scale. This reduces delays, keeps your data completely private, and removes the need for costly outside model services.

KubernetesOllamaMistral AICloud Native

Development Process

How We Develop LLM Systems

A structured, security-first process — from selecting models and cleaning data to deploying a fine-tuned pipeline on your secure cloud infrastructure.

Phase 1 — Discovery

Define Objectives & Model Selection

We analyze your business context, define core requirements, and evaluate model trade-offs (e.g., GPT-4o vs self-hosted LLaMA 3) to outline a clear project architecture.

Strategy

Phase 2 — Data Prep

Structure Context & Vectors

We design data ingestion pipelines, clean unstructured assets, and build high-performance vector databases (Pinecone) to form a reliable private knowledge base.

Data Prep

Phase 3 — Prompt Engineering

Refine Outputs & Context

Our engineers build prompting matrices, structural guards, and fine-tune model parameters using custom training data to ensure responses match your exact voice.

Optimization

Phase 4 — Integration

Connect Pipelines & Workflows

We wire LLM pipelines to your application database, connect external APIs via LangChain or LangGraph, and configure automated workflows (n8n/Make).

Integration

Phase 5 — Evaluation

Red-Teaming & Latency Checks

We run comprehensive evaluations to test accuracy, eliminate hallucination vectors, optimize token costs, and ensure absolute enterprise readiness.

Evaluation

Phase 6 — Production Rollout

Deploy & Scale Securely

The pipeline goes live on cloud infrastructure (Kubernetes) with robust monitoring and observability tools for real-time cost, token, and latency analytics.

Deployment

Our Core Strengths

Why Partner with Movya for LLM Services

Deep Technical Expertise

We do not just wire APIs—our engineers understand vector math, fine-tuning parameter updates, and multi-agent states to deliver enterprise-grade performance.

Data Privacy & Compliance

We prioritize security by building private RAG systems and self-hosted models that keep your sensitive client data entirely within your virtual private cloud.

ROI-Driven AI Strategy

We help you select the most cost-effective models and orchestration setups, ensuring your AI automation produces measurable cost savings from day one.

Ready to integrate intelligence into your business?

Let's discuss how customized Large Language Models and prompt architectures can optimize costs and automate critical operations for your platform.

Custom LLM pipelines (OpenAI & LLaMA)
Private RAG systems with Pinecone database
Autonomous agent orchestration (LangGraph)
Data extraction & custom speech solutions

Explore model integrations, prompt setups, or private self-hosted deployments.

Book a Free AI Discovery Call

Large Language Models (LLM) FAQ

Common questions on Large Language Model optimization, custom deployment, and hosting.

What is LLM integration and fine-tuning?

LLM integration refers to connecting pre-trained language models (like GPT-4, Claude, or LLaMA) to your applications via APIs. Fine-tuning is the process of training these models on a specific private dataset to adapt their style, terminology, or tone to match your organization's exact requirements.

Why choose local or open-source models over public cloud APIs?

Hosting open-source models (like Llama 3 or Mistral) on your own private cloud or on-premise servers guarantees complete data privacy. It ensures sensitive company records are never transmitted across external networks, satisfies industry compliance (such as HIPAA or GDPR), and eliminates variable third-party API transaction costs.

How do you optimize LLM token costs?

We implement advanced cost-saving techniques, including semantic prompt caching (storing and reusing previous model responses for similar queries), context pruning (removing irrelevant details from prompts before sending them), and selecting smaller, task-optimized open-source models where possible.

How do you evaluate model outputs for safety and quality?

We deploy automated monitoring frameworks like Guardrails AI and LangSmith. These tools scan outputs in real-time for compliance breaches, hallucination anomalies, and toxic content patterns, automatically blocking or re-routing invalid responses before they reach end users.