Agentic AI & Automation — From Pilot to Production
Most AI projects stall between a promising demo and a system that actually runs in your business. We close that gap. BMP designs, builds, and operates production-grade AI agents and automation — governed, Australian-owned, and onshore.
- Agents that run reliably
- Production
- AI engineers mobilised
- 2–4 wks
- Human-in-the-loop by default
- HITL
- Governance-aligned
- ISO 27001
Our AI Approach
AI Readiness & Use-Case Discovery
We assess your data, systems, and highest-ROI opportunities, then define success metrics and a low-risk pilot — so you invest where AI actually pays back.
Build Agents & Automation
We design custom AI agents, multi-agent workflows, RAG knowledge assistants, and end-to-end automations — engineered to integrate with your existing stack, not sit beside it.
Govern & Productionise
Human-in-the-loop approval gates, monitoring dashboards, role-based access, and compliance documentation ship as standard — not as add-ons. We get agents past the demo and into reliable production.
Operate, Optimise & Scale
We stay accountable after go-live: monitoring, retraining, prompt and cost optimisation, and scaling to new use cases as confidence grows.
Where AI Accelerates — and Humans Stay in Control
Across the delivery lifecycle, BMP pairs agentic AI with human accountability. AI handles the heavy lifting; your people make the calls that matter — Australian-owned and governance-first, the model regulated industries require.
Product Manager
- AI Delivers
- Drafts PRDs, user stories, and backlog grooming from your inputs.
- Human Decides
- Priorities, scope, and the success metrics that define done.
Architect
- AI Delivers
- Proposes designs, evaluates trade-offs, and scaffolds services.
- Human Decides
- Architecture approval, security and compliance constraints.
Designer
- AI Delivers
- Wireframes, design-system variants, and first-draft copy.
- Human Decides
- Brand direction, UX decisions, and final approval.
Developer
- AI Delivers
- Implementation, refactors, unit tests, and documentation.
- Human Decides
- Code review, engineering standards, and merge approval.
QA Engineer
- AI Delivers
- Test generation, regression runs, and bug triage.
- Human Decides
- Release sign-off and risk acceptance.
DevOps
- AI Delivers
- Pipelines, IaC, monitoring config, and anomaly detection.
- Human Decides
- Production changes and rollback calls.
Support
- AI Delivers
- Tier-1 responses, ticket triage, and knowledge-base updates.
- Human Decides
- Escalations and customer commitments.
Productivity gains are typical ranges for AI-augmented delivery and vary by engagement, stack, and governance requirements.
What We Build
Agentic AI & Multi-Agent Systems
Autonomous and human-in-the-loop agents that take action across your tools and workflows — built with LangGraph, CrewAI, and LangChain.
AI Workflow Automation
Automate document processing, reporting, support triage, and back-office operations with AI-driven, orchestrated workflows (n8n, custom pipelines) that keep humans in control of exceptions.
RAG & Enterprise Knowledge Assistants
Secure retrieval-augmented assistants grounded in your own documents and data, with vector search (pgvector, ChromaDB, FAISS) and strict access controls.
Conversational & Voice AI
Chat and voice agents for customer support, sales, and internal helpdesks across web, app, WhatsApp, and voice channels.
Custom Model & LLM Integration
Integrate and optimise leading models (Claude/Anthropic, OpenAI, open-weight LLMs), with fine-tuning, prompt engineering, evaluation, and guardrails.
AI Engineers — Dedicated or Embedded
Hire vetted AI/ML and agentic-AI engineers who work under your roadmap and standards, embedded in your team, mobilised in 2–4 weeks.
The Agentic Frameworks We Build On
We engineer production-grade agents and automation on the leading agentic frameworks, model providers, and orchestration platforms — chosen to fit your stack, security, and budget.
LangChain
Agent framework
LangGraph
Agent orchestration
CrewAI
Multi-agent
Google ADK
Agent Development Kit
Microsoft AutoGen
Multi-agent
LlamaIndex
RAG framework
OpenAI
Models & Agents SDK
Anthropic Claude
Models & MCP
Semantic Kernel
Agent framework
n8n
Workflow automation
Built for the Pilot-to-Production Gap
AI adoption is widespread, but production is rare — roughly four in five organisations have experimented with agentic AI, while only about one in ten run agents reliably in production. The gap is rarely the model; it's data foundations, governance, KPIs, and integration.
BMP Technologies is built for exactly that gap. We combine an AI engineering practice with our established data-governance and ISO 27001-aligned delivery, so your agents are not just clever — they’re compliant, observable, and dependable. Australian-based teams keep strategy, delivery, and accountability close, with your data kept in Australia.
Our AI Stack
Agent Frameworks
- LangGraph
- CrewAI
- LangChain
- LlamaIndex
Models
- Claude / Anthropic
- OpenAI
- Open-weight LLMs
RAG & Vector
- pgvector
- ChromaDB
- FAISS
Orchestration & MLOps
- n8n
- MLflow
Cloud
- AWS
- Azure
- GCP
Why Choose BMP for AI
- Governance-first by default — monitoring, human-in-the-loop, and compliance docs as standard deliverables.
- Regulated-industry credibility — banking, healthcare, government, retail; ISO 27001-aligned; APRA/GDPR-aware.
- Production focus — success measured by agents that run reliably, not demos that impress.
- Australian-owned and onshore — senior local talent, timezone-aligned collaboration, and data kept in Australia.
- Flexible engagement — advisory + PoC, full build, or dedicated AI engineers — mobilised in 2–4 weeks.
- Modern stack — LangGraph, CrewAI, LangChain, LlamaIndex, Claude/OpenAI/open-weight LLMs, pgvector/ChromaDB/FAISS, MLflow, n8n, on AWS/Azure/GCP.
Let's Move Your AI From Pilot to Production
Whether you need a low-risk proof of concept, a production agentic system, or AI engineers embedded in your team, BMP gets you there with governance and accountability built in. Contact us today to book an AI readiness call.
Frequently Asked Questions
Agentic AI systems don’t just answer — they take actions and complete multi-step tasks across your tools, with reasoning, memory, and (where appropriate) human approval. A chatbot responds; an agent gets work done.
Most pilots stall on data foundations, missing governance, unclear KPIs, or integration gaps — not the model. We assess all four up front and design for production from day one.
Every deployment includes human-in-the-loop approval gates, role-based access, monitoring/observability, and compliance documentation as standard — aligned with ISO 27001 and regulated-industry requirements.
LangGraph, CrewAI, LangChain, and LlamaIndex; models from Anthropic (Claude), OpenAI, and open-weight LLMs; vector stores like pgvector, ChromaDB, FAISS — chosen to fit your stack, security, and budget.
Yes. We provide dedicated or embedded AI/ML and agentic-AI engineers who work under your roadmap and standards, typically mobilised within 2–4 weeks.
A focused proof of concept typically runs in weeks, not months. We scope a low-risk pilot with clear success metrics before any larger build.
Engagements scale from a fixed-scope PoC to ongoing build/operate or monthly dedicated-engineer rates. Our Australian-owned, onshore model focuses on value and quality — senior local talent, accountability, security, and data sovereignty — so you invest where AI actually pays back.
Check out our latest projects
Driving Visual Perfection Across Digital Platforms — ING
A leading bank achieves pixel-perfect digital experiences with AI-powered visual testing — 20-minute visual sanity checks, 60% more bugs found, and an 80% productivity increase.
Read case study
Test Automation at Scale — Transport for NSW
A NSW Government agency accelerates safe, integrated transport systems with test automation — coverage increased 20x across all layers and simplified, faster testing across 20 browsers.
Read case study
Automated Testing for Medical Indemnity — Avant Mutual
Australia’s largest medical defence organisation scales quality with automation — coverage up 40x, moving from 500 manual tests a week to 20,000 automated validations.
Read case study