Harnessing Intelligent Solutions with AI & ML

AI and machine learning services overview

AI and ML in 2026 means a specific stack of well-understood techniques applied to specific problems. RAG over enterprise documents. Agentic workflows for back-office automation. Predictive models for time-series and tabular data. Anomaly detection for fintech and operations. Classification and extraction for unstructured documents. We build with that stack honestly. Large language models accessed via API (Anthropic, OpenAI, AWS Bedrock) — prompt engineering and RAG before custom training. Evaluation harnesses before scale. And explicit no-go zones where probabilistic outputs would be inappropriate. The sections below break down what we deliver and the tools we use.

AU data sovereignty

Australian engagements are scoped against a clear data-sovereignty posture before any model touches your data.

Onshore hosting: AWS Sydney (ap-southeast-2) is the default region for AU engagements, with an on-premises or private-cloud option available for regulated workloads that cannot leave your environment.
Privacy posture: we work to the Privacy Act 1988 and APP 11 (security of personal information), and customer data is never used to train third-party models — we run on enterprise API tiers with training opt-out and zero-retention options where the provider supports them.
Contract terms: ownership of output data, prompts, embeddings and fine-tuned weights sits with you, with audit logs for prompts, retrieved sources and model responses retained for the term of the engagement.

Agentic AI & Workflow Automation

Multi-step workflows that need decisions between steps, integrations with enterprise systems, and approval-and-routing flows — built as agents, not as scripts pretending to be agents.

When this is right

Multi-step workflows that need decisions between steps; integrations with enterprise systems (Salesforce, SAP, Odoo, Jira); approval-and-routing flows where the path depends on what the data says.

When it isn't

Single-turn Q&A, and deterministic processes that ML would make less reliable. If a rules engine or a workflow tool already solves it, an agent will only add cost and failure modes.

Stack

Models: Claude and OpenAI via API, selected per task on cost-per-token versus quality, with provider redundancy where uptime matters.
Orchestration: LangChain and LangGraph for tool-calling, state, and branching; deterministic fallbacks for steps that must not be probabilistic.
Service boundaries: FastAPI for the agent runtime and tool endpoints — see our API development practice for the production patterns.
Observability: LangSmith or Langfuse for traces, prompts, tool calls and token spend per run; structured logs into your existing SIEM.
Cost discipline: cost-per-task budgeting as a first-class constraint, not an afterthought — every workflow has a token and dollar ceiling that fails the run if breached.

For the deeper architecture — planner/executor patterns, tool-use design, human-in-the-loop checkpoints — see our agentic AI page.

Enterprise system integration via AI agents

RAG & Enterprise Search

Retrieval-augmented generation grounded in your own documents, policies and runbooks — so answers cite the source instead of inventing one.

When this is right

Question-answering over your own documents, policies and runbooks; reducing hallucination by grounding responses in source material; building internal answer engines for support, legal, sales enablement and operations.

When it isn't

When the answer requires generating new content not present in your corpus — use the models directly. When the latency budget is sub-100ms — build search infrastructure and rank with a smaller model instead of a full RAG round-trip.

Stack

Vector databases: we default to pgvector for engagements where Postgres is already in scope; Pinecone or Weaviate for high-volume or hybrid retrieval where the workload outgrows a single Postgres instance.
Retrieval strategies: BM25 plus dense embeddings as a baseline; HyDE (Hypothetical Document Embeddings) when query phrasing diverges from corpus phrasing; cross-encoder reranking when top-3 precision matters.
The unglamorous 80%: chunk-size tuning, metadata filtering, embedding model selection and corpus hygiene — this is where RAG quality actually lives, not in the prompt.
Citation and provenance: every answer carries the retrieved chunks and source URLs so reviewers can verify, and so the system fails safely when retrieval returns nothing relevant.

Vector database and retrieval architecture

Evaluation Harnesses & Reliability

Evals before you ship, and continuously after. The difference between a demo and a production AI system is the harness — not the model.

When this is right

Any production deployment of GenAI — before you ship, and continuously after.

When it isn't

Never. This is non-negotiable for production AI. If a vendor or team tells you evals are optional, that is the signal to ask harder questions.

Stack

RAG-specific metrics: Ragas for faithfulness, answer relevance and context precision — scored against a versioned ground-truth set, not a vibe check.
End-to-end harnesses: TruLens or DeepEval for full-pipeline evaluation, including tool-use traces and multi-turn dialogues.
Golden datasets: versioned alongside the model code in the same repo, with regression suites that fail the build on drift — the same discipline you apply to unit tests.
Cost-per-task SLOs: dollar ceilings as a hard SLO alongside latency and accuracy, alerted on the same dashboards.
Guardrails: input-side PII redaction; output-side refusal patterns, jailbreak detection and schema validation so a bad model response never reaches a downstream system.

Predictive Models & Specialised Workloads

Classification, forecasting, anomaly detection, NLP extraction, computer vision — anywhere the workload has structure and an LLM would be overkill or unreliable.

When this is right

Classification, forecasting, anomaly detection, NLP extraction, computer vision — anywhere the workload has structure and an LLM would be overkill or unreliable. Tabular data, time-series, document extraction with stable schemas.

When it isn't

Open-ended generation or free-form reasoning. Use a foundation model for those and reserve specialised models for the work they do better and cheaper.

Stack

Classical workloads: scikit-learn for classification, regression, clustering and gradient-boosted trees — we ship these more often than people expect, because they are usually the right answer.
Fine-tuned specialised models: PyTorch and the Transformers library for domain-tuned models where a general LLM under-performs on cost or accuracy.
Inference efficiency: ONNX Runtime for portable, low-overhead serving where per-request cost matters.
Document workloads: OCR pipelines combining Tesseract and PyMuPDF for invoices, contracts and structured form extraction.

Our AI/ML engagement case studyis a real example — scikit-learn over a transformer because the data didn't justify the complexity, and the cheaper model was also the more reliable one.

Predictive model training and validation

Production AI Engineering

What separates “we ran an experiment” from “we operate this in production for a customer” — serving, monitoring, drift, audit and the boring engineering that keeps the lights on.

When this is right

Any of the above going to production — agentic workflows, RAG systems, specialised models. The moment a model serves a real user or makes a real decision, this section becomes the work.

When it isn't

Proofs-of-concept and notebooks. Don't over-engineer experiments. But also don't confuse a notebook with a system.

Stack

Model serving: FastAPI services or AWS SageMaker endpoints, with the choice driven by the existing platform rather than fashion.
Containerised inference: Docker images sized for the actual hardware, with cold-start and tail latency monitored as first-class metrics — not just the median.
Drift monitoring: data drift and concept drift detection with retraining triggers wired into the deployment pipeline.
Safe rollouts: A/B and shadow deployments so a new model proves itself on production traffic before it owns the decision.
Auditability: structured audit logs of every model decision — prompt, retrieved context, model version, output, downstream action — retained for the regulatory window the workload demands.

Production AI engineering and observability

Want a calibrated, no-hype assessment of where AI fits in your business?

Two ways in: book a 30-minute discovery call (better for CXOs scoping a project) or request a written technical architecture review of a specific use case (better for CTOs and engineering leads who want a second opinion). Both are no-obligation.

Our 30-minute discovery covers: which of your current ideas are best suited to RAG vs custom training vs prompt engineering, where the regulatory boundaries sit, what the realistic build cost looks like, and which categories we'd explicitly recommend against.