Sydney's Lean
Tech Partner.

Problem: A Melbourne-based geotechnical engineering firm of roughly 500 staff was building each day's field schedule by hand. Allocating jobs to crews and sequencing site visits took hours, and drive time between sites was never properly optimised.

What we built: An AI optimisation solver that assigns field staff to jobs and sequences their routes, accounting for skills, site locations, travel time, and daily capacity constraints.

Outcome: Scheduling went from hours to minutes, total drive time dropped, and the firm now places more jobs per day with the same field team.

Problem: A Queensland strata management group held years of documents across a large, unstructured SharePoint estate. Finding the right document meant manual searching, and routine strata forms were filled in by hand every time.

What we built: An AI retrieval system indexed across the full document estate, letting staff ask a question and get the source document back. Paired with AI process automation that generates strata forms from the underlying records.

Outcome: Document lookups that took staff minutes of searching now return instantly, and routine form generation runs without manual data entry.

Problem: One of Australia's largest regional councils was running roughly $500,000 a year in AWS spend with no recent independent review of cost or architecture.

What we did: A full review across their AWS environment covering compute sizing, storage tiers, idle and orphaned resources, licensing, and architectural patterns driving avoidable cost. Every recommendation documented with the change required and the saving attached.

Outcome: Approximately $20,000 per month in identified savings, close to 50% of existing spend, alongside a clearer architecture for the council to run against.

Problem: A client's Azure environment hadn't been reviewed since setup. They were paying for idle compute, over-provisioned resources, and misaligned storage tiers.

What we did: A 14-day audit across their full Azure environment covering web apps and AI/ML workloads. Identified idle resources, inefficient queries, misaligned storage, and legacy services. Every change documented before being applied.

Outcome: Azure spend dropped from $26,280/year to $2,190/year — a 92% reduction. Projected annual saving: $24,090.

Problem: A healthcare platform needed to improve the accuracy and coverage of their geographic clinic index. Existing data was incomplete and manually maintained.

What we built: End-to-end integration of a nationally verified healthcare dataset into their core platform, with automated weekly refresh pipelines, data normalisation, LLM-driven analysis, and custom-trained ML algorithms to identify actionable insights from input data.

Outcome: The platform now runs on live, high-fidelity data covering every registered health clinic in Australia, refreshed automatically with zero manual intervention.

Problem: A consultancy's site search process had analysts spending days manually pulling data across dozens of tabs and assembling reports by hand.

What we built: A custom Site Discovery Engine that pulls site data via API, layers in industry-specific intelligence, merges datasets, and feeds into an automated PDF report generator. Deployed as a web application.

Outcome: Greater than 4x return on investment through consultant time saved. The team now focuses on analysis and negotiation while the engine handles everything else.