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Read Case StudyEnterprise LLM Infrastructure for Secure, Private & Scalable AI
Your proprietary data can't go through public AI APIs. Axiomra designs and delivers enterprise LLM infrastructure so your models run on your own servers, inside your own network, fully under your control — no third-party data exposure, no unpredictable API costs, no vendor lock-in. On-premise, VPC-isolated or fully air-gapped, we build the complete stack you need to run LLMs securely at scale.
Who this is for
Your Business Has Outgrown Public AI APIs
Most enterprises start with a public LLM API. It works fine at first. But as usage grows, sensitive data passes through third-party servers, costs scale faster than value, compliance teams raise red flags, and you lose control over model behavior and pricing. That's the point where enterprises stop patching and start building real AI infrastructure.
Data Privacy Risks
Every query sent to a public LLM API leaves your network. For organizations handling financial records, patient data or legal documents, that's not an acceptable risk.
Compliance Exposure
GDPR, HIPAA and SOC 2 often require strict data residency and access controls. Public AI APIs rarely meet those requirements out of the box.
Unpredictable API Costs
At low volumes, API pricing feels manageable. At enterprise scale, token costs compound fast. Private infrastructure gives you a fixed, predictable cost model.
Vendor Lock-In
When your AI depends entirely on one provider, a pricing change or policy update can disrupt your whole business. Private infrastructure puts control back in your hands.
High Latency
Round-trip API calls add latency to every AI process. For real-time tools and high-frequency workflows, on-premise deployment delivers far faster response times.
No Control Over Behavior
Public models update without notice and fine-tuned behaviors can change overnight. Run LLMs on your own infrastructure and you decide which version runs, and when.
What we build
Enterprise LLM Infrastructure Services We Deliver
Private LLM Deployment
Run large language models entirely within your own environment — no data leaves your network, no third-party access, no shared compute. We set up deployment on your dedicated servers or isolated cloud, configure access controls, and connect the model to your internal systems. Your team gets a private ChatGPT-like environment with complete data ownership.
Dedicated Server SetupAccess ControlsFull Data Ownership
On-Premise LLM Deployment
Keep your AI models physically inside your own data center for the highest level of data control available — no internet dependency, no cloud exposure. We handle GPU server configuration, model installation, inference optimization and internal API setup, so your team interacts through a secure endpoint with zero data leaving the facility.
GPU Server ConfigurationAir-Gapped EnvironmentsSecure Internal Endpoints
VPC & Private Cloud Deployment
Already on AWS, Azure or GCP? We deploy your LLM inside a Virtual Private Cloud so the model runs in an isolated network segment, fully separated from public internet access. You get the flexibility of cloud infrastructure with the privacy of an on-premise setup, secured by strict network policies.
Isolated VPC NetworkingSecure API EndpointsCloud Scalability
Open-Source LLM Hosting
You don't need to pay per token to run a capable model. Open-source models like LLaMA 3, Mistral, Mixtral, Phi-3 and Falcon now match or beat commercial APIs on many enterprise tasks. We evaluate your use case, select the right model, and host it on your private infrastructure with optimized inference — no recurring API fees.
Model Evaluation & SelectionOptimized InferenceZero Per-Token Fees
LLMOps & Model Serving
Deploying a model is only the beginning — running it reliably at scale needs proper serving infrastructure. We cover the full production stack: vLLM and Hugging Face TGI serving, load balancing, request batching, rate limiting, multi-model routing, auto-scaling and observability dashboards for latency, errors and usage in real time.
Production Serving FrameworksAuto-Scaling & Load BalancingReal-Time Observability
RAG Infrastructure & Fine-Tuning
Give your LLM access to your own knowledge without retraining, and build a proper fine-tuning pipeline on your proprietary data. We set up document ingestion, embeddings, vector databases and retrieval logic, plus secure training environments, LoRA/QLoRA pipelines, experiment tracking and a model registry with version control.
Private RAG PipelinesLoRA & QLoRA Fine-TuningModel Registry & Versioning
Internal ChatGPT for Enterprises
Give your team a private AI assistant that works like ChatGPT but runs entirely on your own infrastructure, trained on your internal knowledge and accessible only to employees. We build the LLM backend, an OpenAI-compatible internal API, a chat interface, role-based access, and integrations with Slack, Microsoft Teams or your internal portal.
OpenAI-Compatible Internal APIRole-Based AccessSlack & Teams Integrations
Not Sure Which Setup Fits Your Needs?
Most enterprises need more than one of these working together. We'll map your current operations, identify where AI delivers the fastest return, and recommend the right combination for your environment.
Our tech stack
Expertise In Advanced Development Technologies.
As a result-driven AI agency, we combine industry-leading frameworks with advanced cloud infrastructure to deliver seamless AI integration. Our team selects the best tools for your specific needs to ensure long-term scalability and measurable ROI.
GPTClaudeGPT-3Phi-2GroqCTRLPALMGPT-4oPix2PixGeminiMediapipeGuardrailsVertexAIWhisperStyleGANLlama3DeepDreamMid JourneyMistralAIStable DiffusionOpenAI Embedding Model
What industries do we specialize in?
Enterprise LLM Infrastructure Built For The Industries That Cannot Afford To Get AI Wrong
We deploy private and on-premise LLM infrastructure for businesses across industries. Every deployment is designed around the specific data sensitivity, compliance requirements and workflows of that industry, so your LLM infrastructure matches the way your business actually works.
View all industriesEnterprise LLM Infrastructure For Healthcare
Deploy private LLM infrastructure that keeps patient data fully within your network while giving clinical and administrative teams access to powerful AI assistance.
Build solutions for:
- HIPAA-compliant clinical documentation and note summarization
- Patient record analysis and discharge summary generation
- Medical coding assistance and billing document processing
- Secure onboarding assistant for clinical staff and policy queries
- Private RAG over electronic health records and medical databases
- Radiology and pathology report summarization
- Internal research assistant for clinical trial documentation
- Knowledge assistant for protocols, drug interactions and guidelines
How we work
How We Build YourEnterprise LLM Infrastructure
Every enterprise environment is different. Our process accounts for your existing infrastructure, compliance requirements and business goals — from the first conversation to a fully operational LLM environment.
Contact us nowStep 1 of 5
01
Assessment & Discovery
We start by understanding your current environment before writing a single line of config — reviewing your IT setup, data residency needs, compliance obligations, GPU availability, expected query volumes and the internal systems your LLM must connect with.
Step 2 of 5
02
Architecture & Model Selection
We design the deployment architecture that fits your situation — on-premise, VPC, private cloud or hybrid — and recommend the best open-source or licensed model based on accuracy, latency, hardware and cost. You get a detailed architecture doc before any build begins.
Step 3 of 5
03
Environment Provisioning
We build the foundation: provisioning servers or cloud, configuring Kubernetes, setting up networking and firewalls, installing GPU drivers and CUDA, and preparing storage and database layers — hardened for security and production-grade workloads.
Step 4 of 5
04
Deployment & Optimization
We deploy your model using the right serving framework (vLLM, TGI or Triton), tune the inference server for throughput and latency, set up batching and caching, and expose a secure internal API endpoint. Your LLM is now live inside your private infrastructure.
Step 5 of 5
05
Integration, Security & LLMOps
We connect the LLM to your knowledge via a RAG pipeline and integrate it with Slack, Teams, ERP or CRM. Then we lock it down with SSO, role-based access, audit logging and encryption, and set up full observability — plus documentation, handover and ongoing support.
What innovations have we delivered to businesses?
Showcasing Our AI Development Projects
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Marketing
Haslinked
Navigation & Logistics
Map App AI
Education
FluentTalk AI
Creative Tools
Background Eraser AI
Marketing & Advertising
Bestprover
Productivity Automation
Word2Excel AI
Food & Agentic AI
Ladle
Creative Tools
Photo Retouch AI
AI Solutions
Rodii
Marketing & Advertising
AutotagAI
AI Solutions
Alisia
AI Solutions
Swi
Email Marketing
Mailpitch
Beauty & Cosmetics
Virtual Makeup AI
Edtech & Entertainment
Moveally
Video & Entertainment
Peersuma
Fashion
FN-AD Sales CRM
AI Solutions
Minimi
AI Solutions
Upstar
Fashion
FN-AD Match
AI Solutions
Spintip
Technology & Agentic AI
Visa Bot
Beauty & Cosmetics
Deepduck
Education
Navex
Marketing
Haslinked
Navigation & Logistics
Map App AI
Education
FluentTalk AI
Creative Tools
Background Eraser AI
Marketing & Advertising
Bestprover
Productivity Automation
Word2Excel AI
Food & Agentic AI
Ladle
Creative Tools
Photo Retouch AI
AI Solutions
Rodii
Marketing & Advertising
AutotagAI
AI Solutions
Alisia
AI Solutions
Swi
Email Marketing
Mailpitch
Beauty & Cosmetics
Virtual Makeup AI
Edtech & Entertainment
Moveally
Video & Entertainment
Peersuma
Fashion
FN-AD Sales CRM
AI Solutions
Minimi
AI Solutions
Upstar
Fashion
FN-AD Match
AI Solutions
Spintip
Technology & Agentic AI
Visa Bot
Beauty & Cosmetics
Deepduck
Education
Navex
What our clients say about us?
Real-World Results From Leaders Using Our Enterprise LLM Infrastructure
Their willingness to take any problem, break it down, and work through it is impressive. Strong software development skills and real knowledge of the tools we needed.
Faisal Huq
CEO & Founder, FormOle
Why Axiomra?
What You're Actually Getting When You Work With Us
We Build For Production, Not Demos
A working demo isn't a production system. Every engagement is scoped and built with production in mind from the first call — evaluation infrastructure, monitoring, rollback capability and documented handoff, not just a model that works in a notebook.
We Work Across The Full Stack
Most AI vendors specialize in one layer. We cover the full stack — from data architecture and model selection through deployment, monitoring and ongoing iteration. You don't need to coordinate three vendors to get one system into production.
We Stay Engaged After Go-Live
AI systems drift and models degrade without ongoing evaluation. We offer structured post-deployment support — monitoring, re-evaluation against your golden sets, and proactive recommendations when performance signals change. You won't have to chase us down.
5+
Partnership
200+
Projects
20+
Countries Served
20+
Tech Expert
Frequently Asked Questions
A public LLM API means your data leaves your network and is processed on a third-party server. Private LLM infrastructure means the model runs entirely inside your own environment — on your servers, in your VPC, or in your private cloud. Your data never travels outside your network boundary, and you control the model, the infrastructure, the access and the outputs.
No. Private LLM infrastructure can run on GPU instances provisioned within your existing cloud (AWS, Azure or GCP) inside an isolated VPC. On-premise GPU hardware is one option, not a requirement. We assess your current environment and recommend the most cost-effective compute setup based on your query volume, latency needs and budget.
There's no single answer. The right model depends on your use case, the languages you operate in, your available compute and your latency requirements. We evaluate models including LLaMA 3, Mistral, Mixtral, Phi-3, Command R+ and others against your specific needs before recommending — we don't default to one model across all deployments.
Most enterprise LLM infrastructure deployments complete within 6 to 12 weeks. The timeline depends on environment complexity, the number of systems the LLM integrates with, compliance requirements, and whether fine-tuning is in scope. We provide a precise timeline after the initial infrastructure assessment.
Yes. We build RAG pipelines that connect your private LLM to internal documents, databases, wikis, SharePoint, ERP data and other sources. The LLM retrieves relevant context from your internal data before generating a response, so answers are grounded in your actual business knowledge, not just the model's general training.
Azure OpenAI and AWS Bedrock are managed services inside a cloud provider's environment — more isolated than a public API, but you still depend on the provider's model versions, pricing and terms. Our private infrastructure gives you full control over the model, serving framework and configuration, independent of any single provider — and it can run entirely on-premise if your compliance requires it.
Build Your Private LLM Infrastructure
With A Team That's Done It Before.
Handling sensitive data, hitting API cost ceilings, or working toward compliance a public AI service can't meet? This is the right conversation to have.