Generative AI Development Company
Build robust Generative AI solutions tailored for the enterprise. From **LoRA fine-tuning** and **RAG with Pinecone**, to **LangChain agentic workflows** and **secure private hosting** (vLLM/Ollama).
Enterprise Generative AI Capabilities
Custom LLM Fine-Tuning
We employ parameter-efficient fine-tuning (LoRA/QLoRA) on models like Llama 3 and Mistral to adapt them to your specific enterprise vernacular and tasks.
LoRA & QLoRA Fine-tuningAdvanced RAG Systems
Eliminate hallucinations by grounding LLMs in your internal knowledge bases using cutting-edge vector databases like Pinecone, Milvus, and Weaviate.
Pinecone / Milvus Vector DBsAgentic Workflows
Move beyond simple chat. We build autonomous AI agents using LangChain and LlamaIndex that can plan, use APIs, query SQL databases, and execute tasks.
LangChain & LlamaIndexPrivate LLM Hosting
Guarantee 100% data security by hosting open-source LLMs entirely within your private cloud infrastructure using high-throughput engines like vLLM and Ollama.
vLLM & Ollama DeploymentZero-Compromise Private LLM Deployments
For enterprises handling sensitive PII, PHI, or proprietary financial data, sending payloads to public APIs is often a non-starter. We engineer fully isolated Generative AI pipelines.
On-Premises & VPC Hosting
Deploy high-performance open-source models (Llama 3, Mixtral) directly within your own AWS VPC or on-premise hardware using vLLM.
Enterprise Guardrails
Implement NeMo Guardrails to enforce strict output formatting, prevent prompt injection, and ensure compliance with internal policies.
Generative AI Technical Questions
What is the difference between RAG and LLM Fine-tuning?
RAG (Retrieval-Augmented Generation) grounds the LLM by retrieving external facts from a vector database (like Pinecone or Milvus) at query time, making it ideal for dynamic knowledge bases. Fine-tuning adjusts the model's internal weights (via LoRA) to adapt to specific tones, tasks, or niche domain jargon.
How do you ensure our enterprise data remains secure when building Generative AI?
We guarantee data security by deploying private LLMs within your own infrastructure using frameworks like vLLM and Ollama, or by utilizing SOC2-compliant enterprise cloud APIs with zero-data-retention policies. Your data is never used to train public models.
Not Sure Which AI Architecture Fits Your Budget?
Use our interactive LLM Selector and AI Chatbot Cost Calculator to get a tailored architecture estimate based on your specific security, token volume, and deployment requirements.
Build Custom Enterprise AI Solutions
Deploy powerful LLMs, agents, and RAG systems with uncompromised data security.
Schedule Generative AI Engineering CallTalk Directly to an AI Solutions Architect
Book a zero-pitch, 20-minute engineering session to evaluate your dataset readiness, scope vector database options (Pinecone/Milvus), map LLM architectures (RAG/Agentic), or calculate model training costs.
Book a 20-Min Technical Strategy Call
Discuss your architecture, feasibility, hardware sizing, or custom software requirements directly with a senior engineer.
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