Your AI is only as good as the data under it.

Problem

CollegeWear has made custom graduation stoles and officially licensed campus apparel since 2010, serving students, organisations and universities nationwide across two platforms — direct retail and institutional partners. A made-to-order configurator generates data continuously: every design a customer starts creates records and assets, whether or not the order is ever completed. Fifteen years of that, against a traffic peak concentrated into graduation season, is a demanding foundation to run a business on.

What we’re doing

Problem

Scaling one-to-one tutoring is economically impossible with humans, and static tools lose students because they can't hold a conversation.

What we built

Results

• Eliminated separate vector-database cost entirely by unifying embeddings in pgvector
• Sub-120ms similarity search
• 3x increase in session duration after voice launched

Problem

Asset-intensive enterprises — oil, gas, mining — running disconnected legacy systems and spreadsheets, where strategy set in the boardroom never reaches the daily workflow. KPIs live in slide decks, progress is chased through emails and weekly check-ins, and compliance exposure sits in every gap between systems.

What we built

• Tri-layer synchronisation (KPI → Project → Task). What matters, how we get there, and who does what today, connected as one structure rather than three disconnected tools — so strategy and execution stop drifting apart.
• Automated escalation engine. When a deadline slips or a metric drops, the system flags and routes it to the right stakeholder without anyone chasing a status update.
• Silo-free rollup. Granular daily task data natively aggregates into leadership dashboards — no re-keying, no parallel reporting spreadsheet.

Results

• Audit preparation reduced from three weeks to automated generation
• API latency down 65% via Go concurrency models
• Manual-entry anomalies reduced 98%

Problem

A supplier of lab coats, safety goggles and lab kits selling through two channels that share products and almost nothing else — retail customers who check out with a card, and institutional buyers who need quotes, negotiated pricing, purchase orders and tax exemption. The same operational data has to serve both, plus the back office behind them.

What we’re doing

Problem

A staffing company running employees across branches, timezones, and concurrent client contracts had attendance data that nobody could fully trust. The difficulty isn't the timer — it's that one day of work has to come out simultaneously correct for three different consumers: the client invoice, the payroll processor, and the employee's leave balance. Get a single rule wrong and you over-bill a client, under-pay an employee, or both — and nobody notices until month-end.

What we built

• Split timezone evaluation. Every timestamp stored in UTC, but tardiness is evaluated against the client's timezone while holiday boundaries evaluate against the employee's local branch. Monthly payroll cut-offs run per branch, never at a global UTC midnight.
• Simultaneous billing. An employee can hold concurrent active sessions across multiple clients, and both are billed — while overtime is computed strictly from physical desk time, so shared-service agents never trigger phantom overtime.
• Audit and correction hub. Every override is logged with admin identity and before/after state, and approving a correction triggers cascading recalculation of every dependent metric.
• Automated evaluation engine. Daily completion percentage drives attendance status, with punitive handling that voids payroll hours and adjusts leave balances automatically.

This is what data integrity looks like in practice. Automated decisions are only trustworthy when the rules underneath are exhaustively specified and consistently applied — the same discipline we bring to AI systems, where an ungrounded answer is just a data problem wearing a friendlier interface.

What we’re doing