- Melbourne · New Delhi
- 10 years (since 2016)
- AEST overlap (4–6 business hours)
- AWS Sydney (ap-southeast-2)
AI Automation Development for Australian Businesses
AI automation development means building production systems that use AI/ML to remove manual work — intelligent document processing, retrieval-augmented generation (RAG) grounded in your data, LLM-assisted workflows and agentic systems that plan and use tools. Brainstack engineers these for reliability, not demos: typically a 2 to 4 week proof of concept, then 6 to 12 weeks to production once value is clear.
What We Build
Intelligent Document Processing
OCR + NLP pipelines (Tesseract, PyMuPDF, fine-tuned DistilBERT NER, scikit-learn, Apache Airflow) with human-in-the-loop review. Production reference: ~70% of a certification body's 6,000+ documents/year automated.
Generative AI & RAG
LLM APIs (OpenAI, Anthropic Claude) and retrieval-augmented generation over your data with vector stores (Pinecone), guardrails, red-teaming and cost controls.
Agentic AI Workflows
Multi-step agents (LangChain, CrewAI) that plan and use tools, with observability, audit trails and human-in-the-loop decision policies.
Production-Grade MLOps
Monitoring, drift detection and retraining workflows; hallucination mitigation via grounding; cost control via caching and usage monitoring.
Related: see our broader AI & machine learning services and agentic AI work.
Have a Process Worth Automating?
We start with a tightly scoped proof of concept and clear success criteria. One business day response. NDA first.
AI Automation — FAQ
Will an AI automation hallucinate?
We ground outputs in your data with retrieval-augmented generation, add guardrails and red-teaming, and gate high-stakes steps with human-in-the-loop review. Confidence thresholds route uncertain cases to a person.
Do we need a large dataset to start?
Not always. Traditional ML benefits from larger labelled datasets, but generative AI with RAG can deliver value on a smaller proprietary corpus when it is grounded properly.
How long does it take?
Typically a 2 to 4 week proof of concept to validate feasibility, then 6 to 12 weeks to production once value is clear. Complex multi-integration agentic systems can run 3 to 6 months.
How do you handle Australian data and privacy?
We deploy to AWS Sydney (ap-southeast-2) by default and configure LLM integrations so your proprietary data is not used to train third-party models — handled in line with the Privacy Act 1988 and the Australian Privacy Principles, with on-prem or private cloud for regulated finance and health workloads.
