LLMs, RAG & predictive models
Custom AI & machine learning development in Sydney
Off-the-shelf AI only goes so far. NKROFT builds custom AI around your data and workflows — assistants that know your business, models that predict what’s next and AI features that become a genuine competitive advantage.
What we deliver
Custom AI & Machine Learning services we deliver
Our hybrid team of AI researchers and product engineers takes models from experiment to production: evaluated rigorously, deployed securely and monitored so they keep performing in the real world.
LLM applications
Assistants, copilots and AI features built on leading large language models and tailored to your use case.
Retrieval-augmented generation (RAG)
AI that answers from your documents, policies and knowledge bases with citations, instead of guessing.
Fine-tuning & model customisation
Adapt models to your terminology, tone and tasks for better accuracy and lower running costs.
Predictive machine learning
Forecasting, classification, anomaly detection and recommendation models trained on your historical data.
Evaluation & guardrails
Test suites, red-teaming and safety controls that measure quality and keep AI behaviour within bounds.
MLOps & deployment
Production pipelines for training, versioning, monitoring and retraining models in your cloud.
Use cases
Common use cases
- Internal knowledge assistants for staff
- Demand and sales forecasting
- Predictive maintenance and anomaly detection
- Document classification and summarisation
- Personalised product recommendations
Technology
Tools we build with
We’re vendor-neutral and choose the tools that fit your stack, budget and team — not the other way round.
- Commercial & open-source LLMs
- PyTorch
- Vector databases
- LangChain / LlamaIndex
- scikit-learn & XGBoost
- MLflow
How we work
From first conversation to production
- 01
Discover
We learn your goals, map processes and data, and pinpoint the highest-value opportunities.
- 02
Design
Architecture, user experience and a clear plan with milestones, risks and success metrics.
- 03
Build
Iterative delivery in short cycles, with working software you can see and test early.
- 04
Launch & evolve
Production rollout, monitoring and continuous improvement as your needs grow.
FAQ
Custom AI & Machine Learning questions, answered
Can’t find what you’re looking for? Ask us directly.
Do we need a lot of data to use custom AI?
Not always. Many LLM applications work with a modest set of documents through retrieval, while predictive models need relevant historical data. We assess what you have during discovery.
What’s the difference between RAG and fine-tuning?
RAG lets a model look up answers in your documents at question time — ideal for knowledge that changes. Fine-tuning adjusts the model itself to your tasks and style. Many solutions use both.
Will our data be used to train public AI models?
We design solutions so your data isn’t used to train public models — using enterprise API terms that exclude training, or hosting open-source models in your own cloud.
How do you know if an AI model is good enough?
We agree success metrics with you up front and build evaluation sets that test accuracy, safety and edge cases before and after launch.
Related solutions
- AI AutomationAI agents that take the busywork out of operations — reading documents, routing requests and triggering actions across the tools you already use.
- Data & AnalyticsData engineering that turns scattered information into reliable pipelines, clean warehouses and dashboards your team actually uses.
- Voice & Chat AgentsNatural-sounding voice and chat agents that answer calls, book appointments and resolve enquiries in your brand’s voice — then hand off to your team when it matters.
Let’s engineer what’s next.
Tell us about the problem you can’t stop thinking about. You’ll hear back from an engineer — not a sales script.