GenAI Ops:
Industrialize generative AI
to serve your business
Drive the industrialization of multi-agent, multi-model generative AI
Just as DevOps revolutionized application development and deployment, MLOps (Machine Learning Operations) is establishing itself as a pillar of AI operations, enabling agile, secure, and scalable model deployment to support business innovation.
In line with this, GenAI Ops, LLMOps, and RAGOps are specializations of MLOps, each dedicated to the industrialization of generative AI, the management of language models (LLMs), and the optimization of generation-augmented search systems.
As generative AI becomes increasingly multifaceted—multi-agent, multi-model, multi-context—and becomes deeply integrated into business uses, GenAIOps consists of defining the strategy, orchestrating, industrializing, deploying, and monitoring IAGen models (LLM/SLM) and multi-model, multi-agent (autonomous) agents for business use.
The GenAI Ops support offered by Everience aims to help organizations structure their operations around this technology in response to five major challenges:
- Design architectures capable of efficiently orchestrating flows between agents, models, and data.
- Manage the risks associated with task automation by ensuring supervision and transparency.
- Secure usage and interactions by integrating control and compliance mechanisms.
- Ensure system scalability to support increased usage.
- Adapt skills and tools to ensure smooth, sustainable adoption focused on business needs.
Our teams are involved in every stage of the operational lifecycle of generative AI: from maturity assessment to production, including model customization, inference cost optimization, and autonomous agent coordination.
Powered by our symbiotic by design approach, Everience’s support is based on an in-depth understanding of business challenges, technical constraints, and regulatory requirements, making generative AI a lever for sustainable and responsible performance.
Our key figures
Our GenAi Ops services
We conduct an audit of the organization’s current capabilities in generative AI and MLOps. This assessment identifies areas for optimization and points to watch and formulates strategic recommendations for structuring usage.
We support teams in defining a roadmap for the industrialization of generative AI, integrating DevOps, MLOps, and LLMOps dimensions to ensure controlled and sustainable ramp-up.
Our experts help you automate the deployment of generative models and integrate them into business applications via APIs and microservices, while adhering to best practices in engineering and supervision.
We adapt language models to the specific needs of your organization through fine-tuning, instruction tuning, or techniques such as LoRA, to ensure the functional and contextual relevance of responses.
We implement pruning, quantization, or distillation techniques to reduce inference costs while optimizing latency and scalability of agents on cloud infrastructures.
We design infrastructures capable of dynamically selecting the most appropriate model based on context, expected performance, and budget constraints.
We support the implementation of agents capable of collaborating to perform complex tasks autonomously.
We guide you in choosing and customizing models such as GPT according to your business needs and technical constraints (selection between proprietary or open-source models, adaptation via LoRA).
We implement tools to monitor performance in production, enabling us to detect deviations, audit responses, and ensure the traceability of AI decisions.
We improve research augmented by generation (RAG) systems to enhance the relevance of responses and the quality of retrieval.
