Solutions
AI-Powered Products
AI product agency for Australian teams building copilots, automations, and data-grounded features—safety-first by design.
Who It's For
Perfect for
Products needing intelligent automation
Platforms requiring natural language understanding
Applications with predictive analytics needs
Businesses wanting to leverage AI for competitive advantage
Products requiring personalization at scale
Use Cases
Built for these scenarios
Deliverables
Everything you receive
Timeline
Typical timeline
Discovery
AI use case definition, model selection, and architecture planning
Build
AI integration, model fine-tuning, inference pipelines, and testing
Launch & Stabilize
Performance optimization, cost monitoring, and production deployment
Metrics
Success metrics
AI accuracy: Model performance meets business requirements
Response time: Sub-2 second AI inference
Cost efficiency: Optimized API usage and caching
Scalability: Handles 1,000+ requests per minute
Reliability: 99.9% uptime for AI services
Considerations
Risks & assumptions
AI model performance may require iteration
API costs can scale with usage
Hallucinations in LLMs need mitigation
Regulatory compliance for AI varies by region
Related
You might also need
AI Capability Layer
Add AI to this solution
Common AI modules teams add to accelerate support, ops, and internal workflows—without rebuilding the core product.
Start with a fixed-scope gig
If you want a quick pilot, these gigs ship fast with clear scope, deliverables, and handoff.
FAQ
Frequently asked questions
We use OpenAI GPT-4, Claude, and other leading models based on your needs. For custom requirements, we can fine-tune models or use open-source alternatives. We choose models based on accuracy, cost, and latency requirements.
We use Retrieval-Augmented Generation (RAG) to ground responses in your data, implement prompt engineering, add fact-checking layers, and provide source citations. We also set confidence thresholds.
Yes. For specialized use cases, we can fine-tune existing models or train custom models. However, most use cases work well with pre-trained models and fine-tuning, which is faster and more cost-effective.
We implement caching, batch processing, model selection based on use case, and usage monitoring. We optimize prompts to reduce token usage and choose cost-effective models when appropriate.
We implement bias detection, fairness testing, and ethical AI practices. We ensure AI systems are transparent, explainable, and fair. We follow AI ethics guidelines and best practices.
Regional
Delivery considerations for your region
Compliance & Data (AU)
For Australian teams, we keep privacy and data-handling explicit: access boundaries, safe logging, and clear retention policies.
We can support residency-sensitive designs (where feasible) and document data flows for stakeholder review.
- Privacy Act-aware delivery posture (generic, no legal claims)
- Documented data flows and access boundaries
- Retention/deletion options where required
- PII-safe logging and least-privilege defaults
- NDA and DPA templates available on request
Timezone & Collaboration (APAC)
We support APAC collaboration with AEST/AEDT-friendly meeting windows and async progress updates.
We keep momentum with weekly milestones, crisp priorities, and predictable release planning.
- APAC overlap with AEST/AEDT windows
- Async-first updates and written decisions
- Weekly milestone demos and scope control
- Release planning with staged rollouts
- Clear escalation path for blockers
Engagement & Procurement (AU)
We can structure engagements with clear scope, milestones, and invoicing that fits common procurement expectations.
If you need a lightweight vendor onboarding pack, we can provide delivery process notes and security posture summaries.
- AUD-based engagements and invoicing options
- Milestone-based billing for fixed-scope work
- Time-and-materials for evolving scope
- Procurement-friendly documentation on request
- Optional paid discovery to de-risk delivery
Security & Quality (APAC)
With APAC teams, async clarity matters: written decisions, stable releases, and test coverage that prevents regressions.
We use performance budgets and release checklists so handoffs stay smooth across timezones.
- CI-friendly testing: unit + integration + smoke tests
- Performance budgets + bundle checks
- Release checklist + rollback plan for production launches
- Security checklist for auth and sensitive data flows
- Observability hooks (logs + error tracking) ready for production
Want to scope this properly?
Tell us the workflow + data sources and we’ll propose an AI roadmap with guardrails and clear next steps. AUD-based engagements.
Reply within 2 hours. No-pressure consultation.