LLM Integration for Enterprise
Deploy Production-Ready LLMs Across Your Enterprise Systems
Bring the power of large language models into your enterprise applications with security, reliability, and governance built in. GRAVITI delivers production-grade LLM integration that scales with your business.
Microsoft Azure
Amazon Web Services
Google Cloud
IBM Cloud
Oracle Cloud- Full flexibility in deployment options. We are not commercial partners of software vendors
Who Is This For?
Built for technology leaders ready to move from LLM experimentation to production deployment.
- CTOs & VP Engineering building AI capabilities into enterprise products and platforms
- AI/ML Platform Teams establishing LLM infrastructure and governance for the organization
- Enterprise Architects designing scalable AI integration patterns across the technology stack
- Innovation Teams moving successful LLM pilots into production-grade deployments
Enterprise LLM Integration Done Right
The gap between an impressive LLM demo and a production-ready enterprise deployment is enormous. Issues with latency, accuracy, cost management, security, and governance derail most enterprise LLM initiatives before they deliver value. GRAVITI bridges this gap with battle-tested LLM integration services.
Our team brings deep expertise in deploying LLMs within complex enterprise environments. We implement RAG architectures that ground model outputs in your data, fine-tuning strategies that optimize performance for your domain, and orchestration layers that manage model selection, fallback, and cost optimization automatically.
Every deployment includes comprehensive governance: prompt injection protection, output filtering, usage monitoring, cost controls, and audit logging. We ensure your LLM integration meets enterprise security standards while delivering the performance and reliability your applications demand.
Connecting to systems already in your organization
Our solutions include integration with popular market systems, as well as any additional system as needed
How It Works
- Use Case Assessment — Evaluate your LLM use cases, data landscape, and technical requirements to define the optimal integration approach.
- Architecture Design — Design the LLM infrastructure including model selection, RAG pipeline, caching, and orchestration layers.
- Implementation — Build, test, and deploy the integration with comprehensive prompt engineering, evaluation frameworks, and monitoring.
- Production Operations — Ongoing model management, performance optimization, cost monitoring, and governance.
Expected Outcomes
- Production-ready LLM deployment in 4-8 weeks from kickoff
- 99.9% uptime with redundant model routing and automatic failover
- 40-60% cost reduction through intelligent model selection and caching strategies
- Enterprise governance with full audit trails, access controls, and content filtering
- Scalable architecture that supports growth from pilot to organization-wide deployment
Service Model
- Technical Assessment — Evaluate your infrastructure, use cases, and readiness for LLM integration
- Architecture & Implementation — Design and build the integration with your team
- Testing & Evaluation — Comprehensive testing including accuracy benchmarks, latency profiling, and security assessment
- Managed Operations — Optional ongoing management of your LLM infrastructure and governance
Frequently Asked Questions
Which LLM providers do you support?
GRAVITI is model-agnostic. We work with OpenAI, Anthropic, Google, Meta, Mistral, and open-source models. Our orchestration layer supports multi-model architectures that route requests to the optimal model based on task type, cost, and performance requirements.
How do you handle data privacy and security?
We implement multiple layers of protection including data anonymization, prompt injection detection, output filtering, and isolated processing environments. We support both cloud and on-premises deployment models to meet your data residency requirements.
What is RAG and why is it important for enterprise LLM deployment?
Retrieval-Augmented Generation (RAG) grounds LLM responses in your enterprise data rather than relying solely on the model's training data. This dramatically improves accuracy, reduces hallucinations, and ensures responses reflect your current information.
How do you manage LLM costs at enterprise scale?
Our platform includes intelligent caching, model routing based on task complexity, prompt optimization, and usage analytics. These strategies typically reduce LLM API costs by 40-60% compared to naive implementations.
Move Your LLM Strategy from Pilot to Production
Partner with GRAVITI to build production-ready LLM integrations that deliver real business value. Schedule a technical consultation with our AI engineering team.
