Which AI services does Arixent offer?

Four flagship offerings: AI strategy, consulting and governance; generative AI and LLM development, including conversational AI and AI integration; AI agents and intelligent automation; and machine learning, computer vision and MLOps. Each is a page on this site with the capabilities inside it named.

Do we need a data platform before starting with AI?

Not always, but you need trustworthy data for the use case at hand. We assess that in discovery and, where needed, our data engineering team fixes the foundations in parallel with the AI build.

Which LLM providers and frameworks do you work with?

We are provider-neutral. Depending on the requirement we work with OpenAI, Anthropic, Google, Mistral and open-weight models from Hugging Face, with orchestration in LangChain, LlamaIndex or plain code, and vector search in pgvector, Pinecone, Weaviate or OpenSearch.

How do you keep AI costs under control?

Cost is a design input. We size models to the task, cache aggressively, batch where latency allows, monitor token spend per feature and set budgets and alerts before launch.

Can you work inside our cloud and security perimeter?

Yes. We regularly build inside client AWS, Azure and GCP accounts, use private model endpoints and follow the access and audit controls your security team defines.

AI Engineering

AI development services that survive contact with production.

Strategy, agents, models and the plumbing around them. Built by engineers who have shipped AI to real users and stayed to run it.

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Bring us the roadmap. We will bring the engineers.

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Most AI projects stall between the pilot and production. That gap is where we work.

A model that works in a notebook is the easy part. What decides whether AI earns its place is everything around it: the data feeding it, the evaluation proving it, the guardrails constraining it and the monitoring that catches drift before your customers do. Arixent's AI engineering practice covers that whole surface. We are product engineers first, so every AI capability we build ships inside real software, with real users and real operating costs in mind.

Need a delivery plan before you commit to a team?

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Capabilities

What we deliver in AI Engineering

Four offerings, on purpose. The capabilities underneath each one are named, so you know exactly what you are buying.

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AI Strategy, Consulting & Governance

Use-case discovery, readiness, a roadmap and the governance that keeps AI defensible.

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Generative AI & LLM Applications

LLM applications, RAG, conversational AI and AI features inside the products you already run.

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AI Agents & Intelligent Automation

Agentic systems and automation that complete multi-step work under human oversight.

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Machine Learning, Vision & MLOps

Forecasting, vision and edge models with the MLOps that keeps them reliable after launch.

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How we work

How ai engineering works at Arixent

01
Frame the problem and the metric
Agree the decision AI supports, the measure of success and the baseline it has to beat before any model is chosen.
02
Prove it on your data
A time-boxed experiment on real data with an evaluation set, so feasibility is answered with evidence.
03
Engineer it into the product
Guardrails, integration, security review, cost controls and the interface users will actually work in.
04
Operate and improve
Monitoring for drift, quality and spend, with a cadence for prompt, retrieval and model updates after launch.