Meet the people responsible for diagnosing business constraints, designing the response and delivering software, AI, data, economics and managed-service work.
Web Lifter is deliberately principal-led: the person who diagnoses your constraint owns the engagement that removes it. That shapes everything about how the team is structured.
Diagnosis is done by the principal accountable for the recommendation, not by a salesperson who hands the findings to a delivery team you never met.
The person who worked out what's constraining your business stays on the engagement through design and delivery, so context is never re-explained.
Every engagement has one named owner responsible for decisions, documentation and the outcome: you always know who is accountable.
When a project needs capability beyond the core team, alliance partners are brought in under the same ownership and standards, never as a silent hand-off.
Profiles are drawn from the same records that sign the published work: no padded bios, no borrowed credentials.
Managing Director & Principle Engineer
John O'Connor is the founder and principal engineer of Web Lifter, a Brisbane software studio building custom software, AI systems, and structured data for Australian SMBs. He has spent over eight years shipping production AI and backend systems, and writes about what actually holds up once the demos are over. Everything published here is drawn from systems running in production for real clients.
Alliance specialists join engagements where the work calls for them, always under the project ownership described above. How alliances work.
These are the working commitments every engagement is run to: the things you can hold the team accountable for.
You know who is accountable for the engagement from the first conversation, and that doesn't change mid-project without being discussed.
You talk to the people doing the work. Cadence and channels are agreed at kickoff rather than assumed.
The thinking that shaped the diagnosis carries through delivery: no relay race between strategy people and build people.
Decisions and the reasoning behind them are written down as the work happens, so the engagement survives staff changes on either side.
If something isn't working, there is a direct line to the principal, not a ticket queue.
Expertise claims are cheap. The team's published writing, delivered projects and open-source work are all public. Inspect them directly.
Articles and guides written by the people who do the work: drawn from systems running in production, not theory.
Selected projects and case studies, with the constraint, the response and the result.
Public repositories and tooling the team maintains on GitHub.
Estimates are labelled as estimates and unknowns are flagged early: confident-sounding guesses cost clients more than honest ranges.
When the team disagrees with a client's framing (or with each other) it's argued in writing, with evidence, before work proceeds.
AI is applied where the evidence supports it, with human review of what it produces. Where it isn't the right tool, that's said plainly.
Work ships with the checks that prove it does what was claimed, not a demo that behaves until the first real input.
Clients end an engagement owning both the system and the understanding of it. Dependence on the team is a defect, not a retention strategy.
There are no advertised openings right now. Listings are never left up stale. The careers page explains what the work is like and how to register interest for future roles.