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Indexus

What these engagements look like

We are not publishing client names yet. Here is the shape of the work, so you can judge whether it matches yours.

Fee proposals, scoping notes to costed proposal

An engineering practice turns a set of scoping notes into a costed fee proposal without re-keying anything into the job system. The scoping notes, the rate card and the history of similar jobs already exist, they just sit in three places and get assembled by hand every time. The work is to join them, draft the proposal against the practice's own template, and surface the assumptions the estimator needs to check. The estimator still reviews and signs off every number before it leaves the building. The measure is simple: how long a proposal takes today against how long it takes after.

Job reporting without the weekly spreadsheet rebuild

Natural language reporting over live job data, so a director asks the question directly instead of waiting on someone to rebuild a spreadsheet every Monday. The reporting that ships with most job systems answers the questions the vendor anticipated, not the ones a director actually asks. The work is joining job, time and accounting data into one model, then putting a query layer over it that shows the logic it generated rather than hiding it. Definitions are written down, so utilisation means one thing across the business.

Subcontractor administration between site and invoice

The variations, approvals and paperwork that sit between work happening on site and an invoice going out, handled as a defined workflow. Variations get chased by phone, compliance documents expire quietly, and progress claims are assembled by hand under time pressure. The work is to make each step explicit, track what is outstanding, alert before something expires rather than after, and keep a person approving anything that commits money. It is unglamorous and it is where margin leaks.

Named case studies follow as clients approve them.

Start with a discovery call.

An hour on how your business runs, where the time goes, and whether AI is worth spending money on yet. You get our read either way.