AI automation · Use case
AI Workflows That Actually Ship in Production
Real AI automations engineered for reliability — intake triage, document extraction, lead qualification, content classification — not demo-ware that breaks at the second edge case.
The problem
What we usually see when a ai in production reaches out.
Most "AI automations" you see in marketing material are demo-ware: they work for the showreel and break on the second edge case. Production AI is a different discipline — evaluation suites, cost monitoring, prompt versioning, fallback handling, and rigorous testing against a representative sample of real inputs. Skip those and you ship something that costs more than it saves and frustrates the team it was meant to help.
The landscape
Where the gap lives.
The current generation of AI tooling — OpenAI's GPT family, Anthropic's Claude, Google's Gemini, plus open-source models like Llama and Mistral — is genuinely capable of replacing human time on specific tasks. The catch is that the gap between a working demo and a production-grade system is much wider than the demo videos suggest. A demo handles the happy path, two edge cases the founder thought about, and stops there. Production needs to handle every input the real world throws at it, gracefully fail when the model output is malformed, monitor cost-per-execution, and provide an audit trail when something goes wrong.
The Australian market is full of "AI automation agencies" that ship Make.com or Zapier flows wrapping a single GPT call. Those work for low-stakes workflows and break the moment the input shape shifts. Genuine production AI requires structured-output schemas the model must conform to, evaluation suites built from representative real inputs, prompt versioning with rollback, fallback handling for malformed outputs, and observability into both cost and quality. None of that is hard, but it's the boring engineering that separates a demo from a system you can stake a process on.
Where production AI works well: high-volume classification, structured extraction from unstructured text, content triage and routing, summarisation for human review, retrieval-augmented Q&A over your own documents, and voice transcription with structured-data extraction. Where it doesn't: anything requiring perfect accuracy without human review, anything where the cost of a wrong answer is high, and anything where the input distribution shifts faster than the evaluation suite can keep up.
Our approach
How we think about it.
Treat the AI workflow as production software, not as a clever prompt. Build an evaluation suite from real input samples before shipping. Version prompts and track which version is in production. Monitor cost per execution and alert on drift. Engineer for graceful failure when the model output is malformed. The boring engineering is what makes AI automation actually work.
Why bespoke
Where off-the-shelf falls short.
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Make.com / Zapier wrappers around a single GPT call don't handle malformed outputs, cost monitoring, or evaluation — fine for prototypes, not production
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Off-the-shelf "AI agent" platforms hide the prompt engineering, making evaluation and iteration impossible
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Structured output schemas (JSON, function calling) require explicit validation logic the no-code tools rarely produce
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RAG (retrieval-augmented generation) systems need bespoke chunking, embedding, and retrieval engineering tuned to your specific content
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Cost monitoring and rate limiting at scale require infrastructure that low-code automation tools don't provide
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Australian data residency for AI workloads requires explicit configuration — defaults usually send data to US-based endpoints
What we typically build
Concrete examples.
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Inbound enquiry triage classifying messages into priority queues with reasoned summaries
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Document extraction pipelines turning PDFs / scans / forms into structured records
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Lead qualification scoring against your actual ICP rather than generic heuristics
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Content classification for editorial workflows (legal, medical, regulatory)
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Knowledge-base retrieval (RAG) over your documents for internal Q&A and customer support
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Long-form content quality checking against editorial standards
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Voice-to-text and structured-data extraction from recorded calls
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Multi-step agent workflows with tool use and human-in-the-loop checkpoints
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AI-assisted code review and documentation generation for engineering teams
Common integrations
Platforms we typically connect.
Platforms we typically integrate with for this kind of work. The list isn't exhaustive — if it has an API or webhook, we can connect to it.
OpenAI
GPT-4 / GPT-5 family — broad capability, mature tooling, function calling and structured outputs
Integration page →
Anthropic
Claude family — long context windows, strong reasoning, native PDF/image input
Integration page →
Google Gemini
Gemini family — strong multimodal, integrates well with Google Cloud
Integration page →
Azure OpenAI
Enterprise OpenAI with Australian data residency and Microsoft compliance posture
AWS Bedrock
AWS-managed model gateway — multi-provider with Sydney region for residency
Google Cloud APIs
Speech-to-Text, Document AI, Vision, and Translate APIs for specialist tasks
Integration page →
n8n
Self-hosted workflow automation — orchestration with full data residency control
Integration page →
Make
Low-code automation for lighter-weight workflows wrapping AI calls
Integration page →
Zapier
Quick prototypes and low-volume workflows; rarely production-grade alone
Integration page →
Twilio
Voice transcription and SMS delivery for AI-driven communication workflows
Integration page →
Slack
Surface for AI assistants — Q&A bots, triage notifications, escalations
Integration page →
Notion
Knowledge source for RAG systems and target for AI-generated documentation
Integration page →
Sanity
Content store for AI-assisted editorial workflows
Integration page →
Microsoft 365
SharePoint as RAG source, Outlook for AI-assisted email triage
Integration page →
Pinecone / Weaviate / pgvector
Vector databases for RAG retrieval — choice depends on scale and existing stack
Don't see what you use? See the full integrations catalogue or tell us what you run — if it has an API, we connect to it.
Services involved
What this draws on.
AI Automations
Real AI automations engineered for reliability — not demo-ware that breaks at the second edge case.
Typical projects $5K–$40K
API Integrations
Wire your business systems together so data flows automatically — no more copy-paste.
Typical projects $8K–$45K
Custom Web Apps
Custom-built web applications when off-the-shelf SaaS forces you to compromise.
Typical projects $20K–$120K
Indicative pricing
Most AI workflow engagements fall between $5K and $40K. Single classification or extraction workflows can be at the lower end; production-grade RAG systems with custom retrieval and evaluation scale higher.
Real pricing is set after a scoping call. We give honest ranges up front rather than hiding behind "contact us" — the actual quote may land lower or higher depending on what discovery surfaces.
FAQs
The questions we usually get.
Which AI providers do you build with?
OpenAI (GPT family), Anthropic (Claude family), and Google Gemini, plus open-source models when self-hosting matters. We pick the right model for the workload and engineer to swap providers when the economics or model quality shifts.
How do you handle hallucinations and unreliable outputs?
Multiple layers: structured output schemas the model must conform to, validation on every response, evaluation suites against known-correct outputs, and graceful fallbacks when the model is uncertain. Hallucinations are an engineering problem, not a model problem alone.
How much does it cost per execution?
Depends entirely on the workload. We monitor cost per execution as a first-class metric and tune prompt design and model choice to keep it economically defensible. A well-engineered workflow is usually 5-100x cheaper than the human time it replaces.
How do you handle data sensitivity?
Enterprise tiers of OpenAI / Anthropic / Azure with explicit no-training data agreements. Australian or EU data residency where required. For maximum sensitivity, self-hosted open-source models. The right choice depends on the regulatory bar of the data involved.
How long until a production AI workflow ships?
A focused workflow ships in 4–8 weeks including evaluation. RAG systems and multi-step agents are 8–16 weeks. We resist the demand for "ship in two weeks" pressure because the result is rarely production-grade.
Written and delivered by
Andrew Roper — Founder & Technical Director
22+ years of practice across SaaS, ecommerce, healthcare information systems, manufacturing platforms, and government-adjacent compliance software. Every engagement is led personally — not handed off.
Let’s build something
The right system,
built once, properly.
If your business is ready to scale beyond what off-the-shelf tools can support — we should talk.