Large Language Models such as GPT‑4, Claude 3, LLaMA 3, Mistral 7B or Mixtral are powerful AI engines. But when fine-tuned on your data through custom LLM development, they further reason over domain-specific knowledge. They understand internal documents and workflows. They automate decisions, generate content, and retrieve insights. They also maintain full data privacy, compliance, and governance aligned with regional standards. At Gravity Base, we enable organizations to harness this entire capability.
At Gravity Base, we engineer domain-trained large language models that go beyond conversation. The models we build can reason, predict, and operate with reliability. Each service we offer is built under enterprise-grade architecture. This makes our large language models not just experiments but long-term infrastructure.
Build domain-adapted LLMs trained on proprietary knowledge graphs and data using GPT-4, Claude, LLaMA, Mistral, Mixtral. These models understand your internal language, rules, and decisions, not just public internet knowledge.
Create reasoning engines, copilots, autonomous workflows, internal assistants powered by RAG, embeddings, vector search (LangChain, LlamaIndex). We design applications where the LLM actively supports workflows, decisions, and operations, not just chat interfaces.
As an enterprise LLM development company, we connect LLMs to ERP, CRM, knowledge bases, databases. For example, SAP, Oracle, Salesforce, Dynamics 365, PostgreSQL, MongoDB. This allows the model to work with live business data while respecting access controls and system boundaries.
A full pipeline of LLM software development, including distillation, quantization (GPTQ, AWQ), pruning, instruction tuning, balancing performance with cost and compliance. The result is faster inference, lower operating cost, and outputs aligned with accuracy requirements.
Deploy models across cloud, on-prem, hybrid stacks using Kubernetes, MLflow, Ray, FastAPI, ensuring scalable, reliable inference with governance and monitoring. We treat LLMs as long-running infrastructure with observability, version control, and reliability.
Design governance frameworks including role-based access, audit logs, policy enforcement, data residency, and compliance controls. This ensures your LLM operates safely within regulatory boundaries while remaining traceable, explainable, and auditable.
Identify high-impact LLM opportunities through structured discovery, ROI analysis, data readiness assessment, and technical feasibility planning. This helps enterprises avoid experiments and focus only on LLM initiatives that are scalable, compliant, and business-critical.
Custom LLMs don’t just automate but elevate the capability, intelligence, and responsiveness of your workflows across every industry. This includes use-cases seeking the best LLM for web development globally, securely, scalably.
Enterprises in both UAE & globally need large language models built for privacy, control, and accuracy. Our LLM models are built to solve real-world tasks with reliability. That’s why clients trust us as one of the leading large language models companies in UAE across every industry.
Custom LLMs at Gravity Base are engineered as enterprise-grade infrastructure, not experiments, combining advanced AI stack, RAG pipelines, vector databases, scalable deployment, and governance. Each stage integrates architecture, tooling, and compliance to deliver reliable, high-performing intelligence solutions globally.
These stories show how Gravity Base built LLM solutions in UAE that turn documents, knowledge, and workflows into actionable intelligence for modern businesses.
Partnering with Gravity Base means choosing a team that treats AI as infrastructure, not just marketing.
Off-the-shelf models are generalists. A custom LLM understands your terminology, compliance rules, documents, and workflows, giving you accuracy and control that generic models can’t match.
Yes. Your data stays private, encrypted, access-governed, and never used to train any external provider. Deployment can be on-prem or UAE-region cloud to meet residency requirements.
Chatbots respond. RAG retrieves. A custom LLM can reason, predict, summarize, classify, and operate over your knowledge, acting like an internal intelligence engine, not a single feature.
Most enterprises see a functional prototype in 30 days. From there, we scale with governance, monitoring, and integration without losing reliability.
Absolutely. Data doesn’t need to be perfect. We help audit, structure, and prepare what you already have so the model can reason accurately and safely.
Yes. We integrate with SAP, Oracle, Salesforce, Dynamics, custom databases, legacy systems, turning your existing software into intelligence-enabled platforms.
We use retrieval grounding, rule layers, fine-tuning, and domain constraints. Outputs become consistent, traceable, and aligned with your policies.
Role-based access, audit logs, versioning, drift monitoring, compliance checks, and data-flow controls. Governance is the backbone of how we deploy LLMs.
GPT-4/4o, Claude 3.x, LLaMA 3, Mistral 7B/Mixtral, Falcon, and custom architectures. We choose what fits latency, cost, privacy, and depth of reasoning for your use case.
Through distillation, quantization, caching, token optimization, and on-prem inference. Your cost structure becomes predictable, efficient, and scalable.
Yes. We embed policies, validation layers, and logic constraints so decisions are smart, explainable, and aligned with risk thresholds.
Your LLM evolves with you. We run continuous monitoring, drift detection, and targeted retraining to keep the model accurate over time.
Not immediately. We handle architecture, training, deployment, monitoring, and updates, while enabling your team with training and operational playbooks.
Understand where AI will create immediate operational lift in your organization. Book an Executive Briefing Today.
WR 1008 – Falcon City of Wonders, Dubai, UAE