Practice · Engineering

AI, data and analytics consulting

Production AI systems, machine learning, data engineering and analytics. Coder Trove builds LLM applications, retrieval-augmented generation and predictive models that run in production on the organisation's own data, hosted in Australia where residency requires it.

Start a conversation See a production AI system

The practice

Production systems, not demonstrations.

Most AI projects fail on data quality and retrieval, not the model. Our practice starts there: what data exists, what condition it is in, and what retrieval has to do before any model produces an answer worth acting on. The result is AI that survives contact with real users, real permissions and real audit requirements.

The practice has shipped AI systems into production for Australian financial services, including automated document generation, role-aware assistants and entity risk scoring, and works alongside our Dynamics 365 and Power Platform practices where Copilot and Dataverse are part of the estate.

What we deliver

Five lines of work.

LLM SYSTEMS

LLM applications and RAG

Retrieval-augmented generation, document automation and assistants grounded in the organisation's own systems, with role-aware access so users see only what their permissions allow. Multi-model routing keeps cost and latency proportionate to the task, and structured outputs make results usable by downstream systems rather than just readable by people.

AGENTS

Agents and automation

Agentic workflows that carry multi-step processes: assessments, document preparation, case summarisation and scoring. Built with guardrails, human checkpoints where decisions carry consequence, and full traceability of what the system did and why. An agent that cannot show its working does not go to production.

ML

Machine learning

Predictive models for risk scoring, forecasting and classification, built on properly engineered features and evaluated against baselines before anyone commits to them. The practice is direct about when a regression beats a neural network and when neither beats the heuristic the business already uses.

DATA

Data engineering

Pipelines, lakehouse architecture, data quality and integration across operational systems, warehouses and streaming sources. This is the foundation the rest of the practice stands on, and it is engineered as software: versioned, tested, monitored, with lineage that shows where every number came from. Power BI and reporting layers are built with our Power Platform practice.

OPERATIONS

MLOps and observability

Evaluation harnesses, monitoring, cost tracking and drift detection for AI systems in production. Every system ships with observability over what it was asked, what it retrieved, what it answered and what it cost, so quality is measured continuously rather than assumed from the demo.

Delivery

How the work runs.

Assessment before ambition

Engagements start with a short assessment of the data, the use cases and the constraints: privacy, residency, latency, budget. The output is a ranked list of what is worth building, what is not yet, and what the data needs first, with reasoning written down.

Pilot to production is one build

Pilots are built on production architecture from day one, with the same security, observability and evaluation as the final system. That costs slightly more at the pilot stage and removes the rewrite that usually kills momentum between demo and deployment.

Privacy and residency by design

Client data is not used to train third-party models. Systems run in Australian regions where residency is required, with retrieval scoped to the requesting user's permissions and audit trails retained for compliance review.

Case study

Grapple Finance

A production AI layer for an invoice finance platform: automated document generation, a role-aware assistant, entity risk scoring and structured assessments, live on AWS Australia.

READ THE CASE STUDY →

Common questions

AI and data, answered plainly.

Why do most AI projects fail to reach production?
Because the demo hides the two hard problems: retrieval and data quality. A model answering questions over ten clean documents impresses; the same model over the organisation's actual estate, with permissions, duplicates and stale content, does not. Projects that start with retrieval design and data condition reach production. Projects that start with the model usually stop at the pilot.
Should we use Copilot, buy a product, or build?
Use Copilot and off-the-shelf products where they fit; they are the cheapest path for generic tasks like drafting and summarisation inside Microsoft 365. Build when the value depends on your own data, your own process or your own margin: risk scoring, document automation on your templates, assistants over your systems. We work across both, including Copilot Studio for the middle ground, so the recommendation follows the case rather than a preference.
Will our data be used to train models?
No. Systems are built so client data is not used to train third-party models, with commercial API terms that exclude training, or models hosted in the client's own environment where sensitivity requires it. Data residency in Australian regions, permission-scoped retrieval and audit logging are design requirements, not add-ons.
How do we know the system is actually accurate?
By measuring it. Every system ships with an evaluation harness: a test set built from real cases, scored on correctness, groundedness and refusal behaviour, run before every release and continuously in production. Accuracy is reported as a number with a trend, not an anecdote from a good demo.
What does an engagement look like, start to finish?
Typically: a two-to-four week assessment producing a ranked build list, then a first system into production in one to two quarters, then expansion on evidence. Teams blend Australian consultants with our Vietnam delivery centre for sustained build capacity, under one point of accountability.
Our data is a mess. Is AI premature?
Sometimes, and we will say so. But the answer is usually scoped rather than binary: one process with adequate data can go first while engineering fixes the foundations behind it. The assessment exists to find that first process, and the data engineering line of the practice exists because the foundations are half the value.

Start here

Tell us what you want the data to do.

A process that should not need people to copy and paste, a model someone promised and never shipped, or a data platform that needs engineering. A consultant who has done comparable work replies within one business day.

Start a conversation