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AI Strategy & Implementation
We help businesses implement AI that actually works: starting with a readiness assessment, then deploying targeted solutions for content generation, customer operations, and data analysis. Every implementation includes deterministic guardrails so outputs are reliable, auditable, and measurable.
Why it matters
Most businesses that attempt AI implementation waste 3–6 months and $50k+ on proof-of-concepts that never reach production. The gap isn't technology. It's the absence of a pragmatic technical leader who can separate hype from value and build systems that work reliably at scale.
The solution
We start with an AI Readiness Assessment, a structured diagnostic that evaluates your data quality, process maturity, and team capability against specific AI use cases. Then we build and deploy targeted solutions: automated content pipelines, intelligent routing, predictive analytics, and AI-augmented customer operations. Every system includes monitoring, fallback logic, and human-in-the-loop checkpoints.
Results
Recent implementations have reduced content production costs by 70% while maintaining editorial quality, automated 60% of tier-1 customer support queries, and cut data analysis turnaround from 2 weeks to 2 hours.
Best for
- Businesses wanting to automate content production at scale with quality controls
- Operations teams drowning in manual data analysis and reporting
- Companies with customer support volume that outpaces headcount growth
- Leaders who want AI ROI, not AI experimentation
Not for
- Companies seeking AGI or research-stage AI capabilities
- Businesses without clean, structured data to train on
- Organisations that want to 'do AI' for marketing optics rather than operational value
Frequently Asked Questions
A 2-week diagnostic evaluating your data quality, process maturity, team capability, and infrastructure readiness against 5–10 specific AI use cases. Deliverable: a prioritised roadmap with expected ROI, risk factors, and recommended implementation order.
We use the best model for each task: Claude for content and reasoning, GPT-4 for structured outputs, and open-source models where cost or privacy requires it. Platform-agnostic by design. Vendor lock-in is a risk we avoid by default.
Deterministic guardrails: structured output validation, confidence scoring, human-in-the-loop checkpoints for high-stakes decisions, and automated monitoring with fallback to manual processes. We don't deploy AI that can't be audited.
First production deployment typically happens within 4–6 weeks of engagement start. Measurable ROI (cost reduction or revenue impact) is expected within 8–12 weeks. The readiness assessment identifies quick wins that often pay for the assessment itself.
Readiness assessments start at A$8,000. Implementation projects range from A$10,000–$50,000 depending on scope and integration complexity. Ongoing AI operations support starts at A$5,000/month.
Sources
- Harvard: Generative AI Business Impact(accessed 2026-02-18)
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