AI services

Nexiour's AI practice runs the full path from a fixed-scope readiness audit to production systems: strategy and consulting, agentic automation, generative and RAG systems, model development and fine-tuning, computer vision, voice and conversational AI, and the data engineering and MLOps that keep them running.

AI Strategy Consulting
Turn scattered AI ambition into a sequenced, costed plan. We decide what to build, what to buy, what to ignore, and in what order.
AI Readiness Audit
A 3–4 week assessment of your data, systems and processes that returns a ranked, costed list of AI use cases and an honest verdict on what you can deploy now.
Agentic AI Automation
We build AI agents that complete multi-step work inside your systems — with defined scope, human checkpoints, and evaluation. Deployed to production, not demos.
Generative AI Development
Custom generative AI applications built for production — content, code, image and document generation with evaluation, guardrails and cost control.

service.tsts

export async function handle(req) {
  const input = await req.json();
  const result = await pipeline.run(input);
 
  if (!result.ok) {
    return Response.json({ error: true }, { status: 422 });
  }
 
  return Response.json(result.data);
}

LLM Development & Fine-Tuning

Fine-tuned and self-hosted language models for specialised tasks, cost reduction at volume, or data residency requirements — with honest advice on when not to.

RAG Knowledge Systems
Retrieval systems that answer questions from your own documents with citations. Built for accuracy and evaluated continuously, not demo-quality chatbots.
Machine Learning Development
Predictive models for forecasting, scoring, recommendation and anomaly detection — built on your data, evaluated honestly, deployed to production.
Computer Vision
Vision systems for inspection, detection, counting and document understanding — built against real operating conditions, not laboratory images.
NLP & Conversational AI
Chat assistants and text-understanding systems that resolve real queries, escalate cleanly, and are measured on resolution rather than containment.
Voice AI Agents
Voice agents that handle calls end to end — booking, qualification, support triage — with latency, interruption handling and escalation designed for real conversation.
AI Data Engineering
The data foundations AI actually runs on — pipelines, quality, governance and access. The unglamorous work that decides whether everything above it succeeds.
MLOps & AI Infrastructure
Deploy, monitor, evaluate and maintain AI systems in production — the operational layer that turns working prototypes into reliable services.
Enterprise AI Platform
Shared infrastructure that lets an organisation build AI repeatedly — governance, access, evaluation and reusable components rather than disconnected pilots.

AI services

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