Skip to content
New evidence report: AI and machine learning at Port Botany, for Sydney's freight and port operators.Read the report

Services

Generative AI & LLMOps

Take LLM applications from demo to dependable. We build deployment frameworks for RAG, agentic and LLM apps, version and evaluate prompts before release, gate every change on measured quality, and monitor hallucination rate, response quality, latency and cost in production.

We operationalise generative AI — RAG, agents and LLM applications — with versioned prompts, evaluation sets, release gates and production monitoring, so quality is measured, releases are controlled and costs stay visible.

What's included

  • Deployment frameworks for RAG, agentic and LLM applications
  • Orchestration with LangChain, LangGraph, CrewAI and Vertex AI Agents
  • Prompt versioning and evaluation with release gates
  • Monitoring of hallucination rate, response quality, latency and cost

How it works

A clear path from the first conversation to a system your team runs.

  1. Step 1:

    Use-case and risk review

    We agree what the application must do, what it must never do, and how much risk each use case carries.

  2. Step 2:

    Build the evaluation set

    Real questions and documents, scored for faithfulness, relevance and safety, become the bar every release must clear.

  3. Step 3:

    Ship behind release gates

    Versioned prompts and models are promoted only when evaluations pass, then rolled out gradually.

  4. Step 4:

    Monitor and improve

    Quality, hallucination rate, latency and cost tracked per release, with alerts and regular reviews.

Technologies we work with

Chosen for your environment: we work in your cloud and with the tools you already use.

MLOps platforms

  • PyTorch
  • TensorFlow
  • Vertex AI
  • MLflow
  • Kubeflow
  • Prometheus
  • Grafana

Cloud

  • AWS
  • Google Cloud
  • Vercel
  • Microsoft Azure

Generative AI

  • OpenAI
  • Anthropic
  • Hugging Face
  • LangChain
  • LangGraph
  • CrewAI
  • Vertex AI Agents

In practice

Healthcare

Clinical Document AI for an Australian Healthcare Practice

Clinicians spent hours reading specialist reports, referral letters and patient records by hand.

~70%Less time spent reviewing clinical documents

Client engagement · anonymised

Read the case study

Frequently asked questions

Which models and frameworks do you use?

We work with OpenAI and other leading model APIs, and orchestrate with LangChain, LangGraph, CrewAI or Vertex AI Agents, chosen for your use case, data and constraints.

How do you measure LLM quality?

With an evaluation set built from your real questions and documents, scored for faithfulness, relevance and safety, and reviewed by people where automated scores are not enough.

How do you monitor hallucinations?

Responses are checked against their sources and sampled for review. Hallucination rate, response quality, latency and cost are tracked per release, with alerts when they move.

Can this run in our own cloud?

Yes. Retrieval, orchestration and monitoring can run in your cloud account, with model access through your provider's regional endpoints where that is required.

How is governance handled?

Prompts, models and evaluation results are versioned with an audit trail, access is controlled, and higher-risk use cases get a human review step, designed with GDPR and the EU AI Act in mind.

Keep exploring