- Melbourne · New Delhi
- 10 years (since 2016)
- AEST overlap (4–6 business hours)
- AWS Sydney (ap-southeast-2)
Building Intelligent Solutions with AI & ML
Building Intelligent Solutions with AI & MLMost AI pilots don't fail because the model is wrong. They fail because the engineering to move a model from notebook to production is harder than the model itself. We build the full stack — data pipelines, model serving, monitoring, retraining triggers, and the integration layer that connects AI outputs to the tools your team actually uses. Typical timeline: 2–4 weeks to a working proof of concept, then 6–12 weeks to production once the value is clear.
Our applied AI work covers four shapes. Predictive ML — classification, forecasting, anomaly detection. Intelligent document processing. Retrieval-augmented generation over your enterprise knowledge base. And agentic AI systems that orchestrate multi-step workflows across the tools you already run. We build the data infrastructure underneath as well. Clean pipelines are what separate reliable production AI from demo-grade systems that drift within months of launch.
For Australian organisations, data sovereignty shapes the AI architecture from day one. We deploy ML workloads to AWS Sydney (ap-southeast-2) by default. We configure LLM API integrations so proprietary data is never used to train third-party models. We handle data in line with the Privacy Act 1988 and the Australian Privacy Principles. Where sensitive workloads need on-premises or private cloud — common in finance and health — we architect accordingly. AU clients engage through our Melbourne office; engineering delivers from Delhi NCR.
Applied AI engineered for outcomes
We build production-grade AI: machine learning models, intelligent document processing, generative and agentic AI integrations, and the data pipelines that make them reliable.
ML Engineering
Classification, prediction, anomaly detection, and personalisation models built, validated, deployed, and monitored for drift.
Document Processing
OCR + NLP pipelines to extract structured data from surveys, certificates, invoices, and compliance documents at scale.
Generative AI Integration
LLM APIs, RAG systems grounded in your data, and AI-assisted workflows built with privacy, cost, and hallucination controls.
Agentic AI Workflows
Multi-step agents that plan, use tools, and orchestrate research, reporting, and process automation with human-in-the-loop guardrails.
Data Engineering & Analytics
ETL/ELT pipelines, warehouses/lakes, streaming, and BI integrations that keep AI and analytics powered by trustworthy data.
Proof to Production
Two-to-four week PoCs to validate feasibility, followed by hardened production deployments with monitoring and MLOps.
Privacy & Governance
Data minimization, access controls, audit trails, and deployment patterns (on-prem/private cloud) for regulated workloads.
Shaping Tomorrow
with AI & Machine Learning
We build applied AI: ML models, intelligent document processing, generative and agentic AI integrations, and the data engineering that keeps them reliable in production.
01Machine Learning Engineering
Classification, prediction, anomaly detection, and personalisation models built, validated, deployed, and monitored for drift.
We cover supervised and unsupervised techniques, CV/NLP where it fits, and design for MLOps from the start—with monitoring, drift detection, and retraining built into the plan, not bolted on later.
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02Intelligent Document Processing
OCR + NLP pipelines to extract structured data from surveys, certificates, invoices, and compliance documents at scale. Production reference: a US-based sustainability leader's compliance document processing pipeline using Tesseract 5.x LSTM, fine-tuned DistilBERT NER, Apache Airflow orchestration, and human-in-the-loop review for fields where accuracy matters.
- Entity extraction and classification for compliance and operations
- Human-in-the-loop review where accuracy matters
- Integration with downstream systems (ERP/CRM/BPM)
03Generative AI Integration
LLM APIs, retrieval-augmented generation (RAG) grounded in your data, and AI-assisted workflows engineered for privacy, cost control, and hallucination mitigation.
- RAG with vector stores, guardrails, and prompt orchestration
- Policy-aware prompts and red-teaming for safer outputs
- Cost monitoring and caching strategies for LLM usage
04Agentic AI Workflows
Multi-step agents that plan, use tools, and orchestrate research, reporting, and process automation with human-in-the-loop guardrails.
- Tool use (APIs, DBs, search) with clear safety boundaries
- Observability, audit trails, and fallback paths
- Decision policies to prevent runaway agent actions
05Data Engineering & Analytics
ETL/ELT pipelines, warehouses/lakes, streaming, and BI integrations that keep AI and analytics powered by trustworthy data.
- Quality gates, lineage, and monitoring to keep data reliable
- Batch and streaming patterns matched to ML/analytics needs
- Fit-for-purpose storage (warehouse, lake, vector)
06Proof to Production
Two-to-four week proofs to validate feasibility, then hardened deployments with MLOps, monitoring, and retraining workflows.
- PoCs with clear success criteria and go/no-go checkpoints
- Deployment patterns for cloud/on-prem with observability
- Ongoing model monitoring and drift response
07Privacy & Governance
Data minimization, access controls, audit trails, and deployment patterns (on-prem/private cloud) for regulated workloads.
- Data minimization and purpose limitation
- Access controls and audit trails for model and data
- On-prem and private cloud deployment options
AI Delivery Workflow
A pragmatic, production-first approach: assess, prove, ship, and monitor AI that delivers measurable outcomes.
Assessment & Opportunity Identification
We start with business goals, data readiness, and impact sizing to identify the highest-value AI opportunities and define success criteria.
Proof of Concept (2–4 weeks)
Run a tightly scoped PoC to validate feasibility, data quality, and expected performance before committing to full build.
Production Build & Integration
Engineer for production: MLOps, security/privacy, observability, SLAs, and integration with your systems (APIs, data, auth).
Deploy, Monitor & Improve
Launch with monitoring, drift detection, feedback loops, and retraining pipelines to keep models reliable over time.
Guardrails, Compliance & Quality
Test for accuracy, safety, bias, cost, and resilience; add RAG guardrails, audit trails, and compliance controls.
Scale & Iterate
Deploy AI/ML models into production with scalable infrastructure. Continuous monitoring tracks model performance, data drift, and feedback to maintain accuracy.
Ready to Build with AI?
Tell us about your AI initiative and get a free, no-obligation assessment from our engineering team.
Domains We Serve
Our software delivery and AI work spans regulated, data-intensive industries where technology drives measurable outcomes.
Financial Services
Data analytics platforms, portfolio reporting dashboards, and automated compliance systems for asset managers. Real-time data pipelines, secure API integrations with banking middleware, and regulatory reporting modules tailored to regional requirements.
Healthcare
Cloud-based platforms for clinical workflow management, patient data systems, and telehealth integrations. HIPAA-aware architectures with compliance-first development where data privacy and audit trails are non-negotiable.
AgriTech & Sustainability
Offline-capable field data collection platforms and supply chain compliance tools deployed across East Africa, South America, and South Asia. PWAs with local data sync, SMS fallback, and voice interfaces. EUDR compliance workflows, traceability mapping, and certification body integration.
Governance & Compliance
Regulatory compliance platforms, governance assessment tools, and audit management systems. Survey platforms tracking sustainability indicators across global supply chains, with multi-language support and role-based access.
AI, Data, and Engineering Stack We Use
We choose the right tools for each project — from front-end frameworks and backend runtimes to databases, cloud platforms, and DevOps tooling. Every stack decision is driven by your project's requirements: performance needs, team familiarity, long-term maintainability, and cost.
The result is software built on proven technology that your team can own, extend, and operate confidently.
Database
- MongoDB
- MySQL
- PostgreSQL
- SQLite
MariaDB- OracleDB
Firebase
Cassandra
AI & Machine Learning
- OpenAI
Claude
LangChain- LlamaIndex
TensorFlow
PyTorch
Hugging Face
Scikit-learn- Pinecone
Ollama- CrewAI
Python
CI/CD Tools
Jenkins
CircleCI
Travis CI
GitLab CI
GitHub Actions
Bamboo
TeamCity
Buildkite
DevOps Tools
Docker
Kubernetes
Ansible
Terraform
Chef
Puppet
SaltStack- Nagios
Engagement Models
We tailor delivery to your team structure and ownership preference. For full process detail, review the dedicated engagement model page.
Outsourcing
- Outcome-based delivery ownership
- Managed roadmap, QA, and releases
- Best for end-to-end product builds
Staff Augmentation
- Engineers integrated into your team
- You keep sprint and release control
- Best for scaling delivery capacity fast
Tech Consulting
- Architecture and platform strategy guidance
- Roadmap, risk, and cost optimisation
- Best for audits, modernisation, and decision support
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Frequently Asked Questions
Key questions on applied AI: services, timelines, data needs, privacy, GenAI guardrails, and how we keep models reliable in production.