QuantAI · Portfolio Demonstration

Vertex Engineering, AI Operating Model

Turning three under-used AI platforms into one governed operating model at a 52-person Melbourne engineering consultancy, with qualified engineers kept firmly in charge of every technical output.

Operating model & governanceWhich AI for which taskEngineering assurance lineAdoption & Champions

A demonstration built around a representative client scenario · figures are modelled estimates under stated assumptions.

The problem

Three capable tools, no operating model

The goal is not more technology. It is an operating model, built so a qualified professional owns and signs every technical output.

The model on a page

Four layers, governance on top

WHYGovernance & assurance · accountable human review · what AI must not do · confidentiality
WHICHTool selection · which AI for which task · approved-use rules for the three platforms
WHATWorkflows & standards · firm-standard workflows · prompt library · reusable knowledge
WHOCapability & people · role-family matrix · AI Champions · learning · measurement

The principle across all four: AI assists, a qualified professional decides and signs, and every improvement is captured once and reused by the firm.

The signature deliverable

Which AI for which task

The piece Vertex was missing most: a rule for the three tools it already owns. Ten seconds, four questions.

Copilot
Work inside your Microsoft files, email and meetings, context in-tenant
Claude
Careful drafting, structure and review of long or important documents
ChatGPT
Open-ended thinking, research framing, quick generation

Above every choice: confidential or client material goes into approved enterprise tools only. Never a personal or free account. Three overlapping licences become one deliberate toolkit.

Highest-stakes pilot · AI in action

The assurance line, made visible

DRAFT + ASSURANCE GUARD APPLIED
Report drafted to template. Your figures are reproduced EXACTLY, not recomputed or checked. ⚑ I did NOT add a "complies with AS..." statement. Verification and code compliance are your professional judgement to state and sign, not mine. ⚑ Inserted [ENGINEER TO CONFIRM] where a code reference was asked for. No technical claim invented.

The AI drafts the words around the engineer's fixed content and refuses to verify, recompute or certify. It even removes the implied compliance claim and hands it back. That line is the difference between AI an engineering firm can adopt and AI it cannot.

Scenario results

All three pilots clear the 15% bar

27%
Proposals & bids ↓
30%
Technical report drafting ↓
33%
Project status reporting ↓

The brief's bar is at least 15% cycle-time improvement per pilot without unacceptable quality loss. About AUD $134k of professional time, roughly 3.8× the fee. On technical reports, none of the saving comes from, or is taken from, the engineering work itself.

Cycle-time % depends only on per-task time, the robust part; the dollar figure sizes the prize. Scenario estimates under stated assumptions.

Governance & what's delivered

Safe to adopt, and built to stick

The line we don't cross: AI never performs, checks, verifies or certifies engineering design or calculations, and never states that anything complies with a code or standard. A qualified engineer owns every technical output, and reviews and signs everything a client sees.

Strategy & design
Operating model · tool-selection framework · governance & assurance · capability matrix · dashboard · 90-day roadmap
Working assets
Prompt library · Claude Project configs · ways-of-working playbook · benefits tracker · 3 SOPs / governance docx · GitHub repo

All shipped as a push-ready GitHub repository, sequenced so a safe quick win earns the trust needed for the high-stakes pilot. Happy to walk your team through adapting it.