Makehitec helps companies turn AI from experimentation into secure, useful and production-ready systems.
We design and build agentic AI architectures, fine-tuned models, private AI assistants, RAG platforms and self-hosted AI infrastructures for organizations that need control, security and business value.
Our approach covers the full AI lifecycle: use-case analysis, data preparation, model selection, fine-tuning strategy, agent orchestration, tool and API integration, knowledge retrieval, guardrails, evaluation, deployment, monitoring and continuous improvement.
For sensitive environments, we also provide local and on-premise AI solutions that keep data under your control. This includes self-hosted LLMs, private inference servers, vector databases, secure access control, GPU infrastructure, Kubernetes-based deployment and integration with existing enterprise systems.
If your company wants to automate business processes, build intelligent assistants, use AI on private data or deploy AI without sending sensitive information to external platforms, Makehitec can help you design and deliver a secure AI architecture adapted to your reality.
Agentic AI architecture
Design of AI agents capable of using tools, APIs, workflows, memory and business logic in a controlled architecture.
Private AI assistants
Internal assistants connected to company documents, databases, applications and operational knowledge.
RAG platforms
Retrieval-augmented generation systems using private knowledge bases, vector databases, embeddings and secure document pipelines.
Fine-tuning and model adaptation
Model customization strategy, dataset preparation, fine-tuning, evaluation and performance improvement for specific business needs.
Self-hosted and on-premise AI
Deployment of local LLMs and private inference platforms on company infrastructure, cloud, hybrid or air-gapped environments.
AI security and governance
Access control, data protection, prompt injection mitigation, auditability, human validation, traceability and secure AI usage policies.
LLMOps and lifecycle management
Model deployment, monitoring, evaluation, versioning, observability, feedback loops and continuous improvement.
Enterprise integration
Connection of AI systems with APIs, business applications, IAM, databases, document management systems, workflows and automation tools.